#Amazon BI tools

[vuelitics1]

Discover how the world’s top companies are leveraging Business Intelligence (BI) to stay ahead of the competition! In this video, we break down the strategies and tools used by giants like Google, Amazon, Apple, and more to optimize operations, enhance customer experience, and drive innovation. From real-time data analysis to predictive analytics, these companies are transforming the way business is done.

Whether you’re a business owner, a data enthusiast, or just curious about how big brands like Netflix and Tesla use BI to gain a competitive edge, this video is a must-watch. Learn how Business Intelligence tools like Tableau, Microsoft Power BI, and SAP BusinessObjects are being used to make smarter decisions, predict customer behavior, and streamline operations.

Visit Our Webiste: https://vuelitics.com/

[jcmarchi]

10 Best AI Form Generators (August 2024)

New Post has been published on https://thedigitalinsider.com/10-best-ai-form-generators-august-2024/

10 Best AI Form Generators (August 2024)

Efficient data collection and user engagement are crucial for businesses and organizations. Artificial Intelligence (AI) has disrupted the form-building process, offering innovative solutions that streamline creation, enhance user experience, and provide valuable insights.

This article explores the top AI form generators that are transforming how we design, deploy, and analyze online forms. From natural language form creation to advanced analytics and seamless integrations, these platforms leverage AI to make form building more accessible, efficient, and powerful than ever before. Whether you’re a small business owner, a marketing professional, or an enterprise-level organization, these AI-powered tools offer features that can significantly improve your data collection strategies and workflow automation.

Fillout is an innovative AI-powered form builder that simplifies the process of creating dynamic, interactive forms. By leveraging the power of artificial intelligence, Fillout enables users to generate forms quickly and effortlessly, catering to a wide range of needs without the hassle of manual design. With its user-friendly interface and advanced AI capabilities, Fillout streamlines form creation, making it an ideal solution for businesses and individuals looking to collect data efficiently.

One of the standout features of Fillout is its ability to create forms from simple prompts. Users can describe the form they want, and Fillout’s AI will generate a tailored form based on their requirements. The platform also offers a powerful no-code editor, allowing users to customize their AI-generated forms further, ensuring a perfect fit for their specific needs. Fillout’s AI technology continuously learns and improves, providing users with intelligent suggestions and optimizations to enhance their forms’ performance and user engagement.

Key Features:

Fillout uses AI to create forms based on user prompts, saving time and effort.

The platform’s AI suggests design improvements and optimizations to create engaging, high-converting forms.

It constantly learns and adapts, providing users with increasingly accurate and efficient form-building suggestions.

Integrates with popular third-party apps and platforms, ensuring a smooth workflow and easy data management.

Enables real-time collaboration, allowing teams to work together on form creation and leveraging AI to streamline the process

Visit Fillout →

Jotform is a cutting-edge online form builder that also uses AI to streamline the form creation process. With its user-friendly interface and AI-driven features, Jotform empowers businesses and individuals to create custom forms effortlessly, without the need for coding expertise. By leveraging AI technology, Jotform simplifies data collection, enhances form performance, and delivers a seamless user experience.

Jotform offers its AI Form Generator, which allows users to create forms simply by describing their requirements in natural language. The AI chatbot understands the user’s needs and generates a tailored form with basic fields and customizations, saving time and effort. Jotform’s AI capabilities extend beyond form creation, as it also offers an AI Quiz Generator and an AI Signature Generator, demonstrating its commitment to innovation.

Key Features:

Create custom forms effortlessly by describing your requirements to the AI chatbot.

Jotform’s AI features, such as conditional logic and prefill options, improve form completion rates and user experience.

Collaborates with OpenAI’s ChatGPT for its AI Quiz Generator, ensuring data privacy and security.

Dedicated to expanding its AI capabilities to meet evolving user needs and maintain its competitive edge.

Enables businesses to automate repetitive tasks, streamline workflows, and focus on high-value activities

Visit Jotform →

With the introduction of AI-driven features and the launch of its innovative product, Formless, Typeform is redefining how businesses engage with and gather information from their customers. This AI-powered approach simplifies form creation, enhances user engagement, and delivers a personalized, conversational experience for respondents.

At the forefront of Typeform’s AI innovation is Formless, a product that transcends traditional form structures. Formless creates a dynamic, two-way conversation between businesses and respondents, mimicking human-like interactions. By allowing businesses to train the AI on specific topics, Formless can answer respondents’ questions and provide a tailored experience, adapting to responses and asking relevant follow-up questions.

Typeform’s AI capabilities extend beyond Formless, offering features like question recommendation and optimization to craft well-written, concise questions that boost completion rates. The platform’s Smart Insights tool employs AI to analyze form results, providing user-friendly dashboards with high-level data overviews. Additionally, Typeform’s AI streamlines lead qualification by automatically categorizing respondents based on their answers, ensuring efficient prioritization of high-value leads.

Key Features:

AI-powered product creating dynamic, two-way conversations for personalized experiences.

AI-assisted question optimization for enhanced form completion rates.

AI-driven analysis tool providing user-friendly data dashboards.

Efficient lead qualification through AI-powered response analysis.

Continuous AI development, including workflow automation and natural language data querying.

Visit Typeform →

Formstack is pushing the boundaries of form building by integrating artificial intelligence to create a comprehensive workflow automation solution. Unlike traditional form builders, Formstack’s AI doesn’t just assist in form creation—it transforms the entire data collection and processing lifecycle.

At the core of Formstack’s innovation is its AI-powered workflow designer. This feature analyzes your business processes and automatically suggests optimal form structures and data flows, creating end-to-end solutions rather than isolated forms. For example, it might design a customer onboarding process that seamlessly moves from initial contact form to follow-up surveys and integration with your CRM.

Formstack’s AI also shines in its predictive analytics capabilities. By analyzing historical form data, it can forecast submission patterns, helping businesses prepare for peak times or identify potential drop-offs in engagement. This proactive approach allows companies to optimize their forms and processes continuously, staying ahead of user needs and market trends.

Key Features:

Generates tailored forms based on user prompts.

Allows teams to go from idea to solution quickly, regardless of their technical aptitude.

Suggests well-written and concise questions to enhance form completion rates.

Analyzes form data, identifying patterns and anomalies that provide valuable insights for businesses.

Easily integrate with other business systems, such as CRMs and Formstack Documents, for automatic data population and streamlined workflows.

Visit Formstack →

With its user-friendly interface and AI-driven features, Paperform enables businesses and individuals to create engaging, personalized forms effortlessly, without the need for coding expertise. By leveraging AI technology, Paperform enhances the form-building experience, making it more efficient, intuitive, and tailored to users’ specific needs.

One of Paperform’s standout AI features is its ability to generate forms based on user prompts. Users can simply describe the type of form they need, and Paperform’s AI-powered Form Builder will create a customized form with relevant fields and customizations. This feature takes the heavy lifting out of form creation, allowing users to focus on more strategic tasks while ensuring that the generated forms are optimized for engagement and data collection from the start.

Paperform’s AI capabilities extend beyond form creation, with features like question optimization and data analysis. The platform’s AI can suggest well-written and concise questions that encourage higher form completion rates.

Key Features:

Generates tailored forms based on user prompts.

Create personalized forms with no coding.

Question optimization and data analysis.

Suggests well-written and concise questions to achieve higher completion rates.

Visit Paperform →

Tally is reimagining the form-building landscape with its AI-powered platform, designed to eliminate complexity and streamline the creation process. This innovative tool stands out by focusing on simplicity and user experience, making professional form design accessible to everyone, regardless of technical background.

At the heart of Tally’s approach is its conversational AI interface. Rather than navigating complex menus, users can simply describe their form needs in natural language. The AI interprets these requests, instantly generating tailored forms complete with relevant fields and logic. This collaborative process feels more like working with a skilled assistant than operating software.

Tally’s commitment to privacy sets it apart in the AI form-building space. With European hosting, GDPR compliance, and end-to-end encryption, it offers a secure solution for handling sensitive data. This makes Tally particularly attractive for industries with strict data protection requirements, such as healthcare and finance.

Key Features:

Generates tailored forms based on user prompts, simplifying the form creation process.

Enables the creation of dynamic forms that adapt based on user inputs or external data.

Prioritizes data privacy and security, ensuring GDPR compliance, hosting in Europe, and encrypting form data both in transit and at rest.

Caters to a wide range of industries and use cases.

Easily integrate with popular tools like Notion, Slack, and Airtable, streamlining workflows and automating processes.

Visit Tally →

Wufoo has established itself as a trusted cloud-based form builder, serving over 3 million users including major brands like Amazon and Microsoft. Its interface simplifies the creation of various online forms, from registrations to payment forms, without requiring technical expertise. Wufoo’s strength lies in its user-friendly design, extensive template library, and robust reporting capabilities.

While not heavily AI-focused, Wufoo has recently integrated with include.ai, expanding its automation capabilities. This integration, combined with Wufoo’s existing features like automated database building and script generation, positions it as a powerful solution for efficient data collection and management. Wufoo’s ability to integrate with various third-party apps further enhances its appeal for businesses seeking to streamline their workflows.

Key Features:

Intuitive design for easy form creation and customization.

Visually appealing forms matching brand styles.

Automatic database, backend, and script building.

Connects with various third-party apps for streamlined workflows.

Over 3 million users and a decade of experience.

Visit Wufoo →

Forms.app distinguishes itself with its AI Form Generator, which allows users to create forms simply by describing their requirements in natural language. This innovative approach simplifies the form creation process, making it accessible to users of all technical levels. The platform’s AI capabilities extend to survey and quiz creation, offering specialized tools that quickly generate these types of forms with minimal user input.

The AI technology powering Forms.app continuously learns and improves, providing users with intelligent suggestions and optimizations to enhance form performance and user engagement. With integration capabilities spanning over 500 apps, Forms.app offers a flexible and efficient solution for businesses looking to streamline their data collection processes and form-based workflows.

Key Features:

Create custom forms by describing requirements to AI assistant.

Generate online surveys quickly with AI-powered survey maker.

Create engaging quizzes easily with AI assistance.

Expanding AI capabilities to meet evolving user needs.

Connects with over 500 apps for smooth workflow and data management.

Visit Forms.app →

Landingi combines landing page building with AI-powered form creation, offering a comprehensive solution for businesses aiming to generate leads and drive conversions. Its standout AI features include a text generator that creates compelling form content based on user prompts, and an SEO generator that optimizes forms for search engines. These tools significantly reduce the time and effort required for copywriting and SEO optimization.

Beyond content creation, Landingi’s AI capabilities extend to image processing and language support. An AI-powered background removal tool enhances the visual appeal of forms, while machine learning-powered translations enable the creation of multilingual forms. This combination of features makes Landingi a versatile platform for businesses looking to create high-converting forms and landing pages with a global reach.

Key Features:

Creates compelling form content based on user prompts.

AI-powered generator optimizes content for search engines.

AI tool for enhancing visual appeal of forms.

ML-powered tool for creating multilingual forms.

Combines AI-powered form creation with landing page building.

Visit Landingi →

MakeForms leverages AI to offer a secure and highly customizable form-building experience. Its AI-powered form builder automates the creation process by suggesting relevant questions and providing tailored templates based on user requirements. MakeForms sets itself apart with advanced AI capabilities like facial recognition for Know Your Customer (KYC) processes, ensuring enhanced security and identity verification.

The platform’s AI extends to form logic and data analysis. Conditional logic enables the creation of personalized forms that adapt based on respondents’ answers, while advanced data organization features like table, summary, and BI views allow for effective analysis and visualization of form data. This combination of security, customization, and analytics makes MakeForms a comprehensive solution for businesses requiring sophisticated form-building capabilities.

Key Features:

Suggests relevant questions and provides tailored templates.

Facial recognition for KYC enhances security and identity verification.

Conditional logic creates personalized forms adapting to respondents’ answers.

Data organization and analysis offers table, summary, and BI views for insights.

Includes secure payment collection, team collaboration, and integrations.

Visit MakeForms →

Why You Should Use an AI Form Generator

AI form generators are improving the way we create and manage online forms. These powerful tools leverage artificial intelligence to streamline form creation, making it easier than ever to design beautiful, interactive forms without extensive technical knowledge. By using an AI form builder, you can save time and resources while still creating user-friendly forms that effectively collect data.

One of the key advantages of AI-generated forms is their ability to adapt and improve based on user interactions. These intelligent systems can analyze form completion rates, identify potential roadblocks, and suggest optimizations to enhance the user experience. This means your forms can continuously evolve to become more effective at gathering the information you need, while also providing a smoother experience for your respondents.

Moreover, AI form generators often come with advanced features such as conditional logic, data analysis, and seamless integrations with other business tools. This allows you to create powerful forms that not only collect data but also help you derive meaningful insights from it. Whether you’re building a simple contact form or a complex survey, an AI form generator can help you create unique, engaging forms that stand out and deliver results. By embracing this technology, you’re not just keeping up with the latest trends – you’re positioning your organization at the forefront of efficient, intelligent data collection.

[bfpnola]

I am not Palestinian nor am I Jewish. Be that as it may, I hate settler colonialism, even more so as a brown, bi, genderqueer ‘Afab’ person. I just wanted to say. 1) your post on the topic is more empathetic and insightful than I’ve seen a lot of people be about this over my entire life and I’ve asked questions of both sides, I tend to stay out of the fray cause I don’t feel it my place to speak over Palestinians and Jews (who are critical of Israel). But, do you have any advice for being a better ally to Palestinians and combating anti-semitism and anti Jewish racism in the everyday?

hey sweetheart! thank you for your commitment to the movement and your earnestness. i am not Palestinian or Jewish either, so i did what is always considered best: i asked those who are! that's exactly why our Advocacy Committee within BFP exists :)

from one of our Palestinian youth volunteers:

if you have the money to do so, donate to the cause! the unfortunate truth is that to gain access to various resources, things cost money. more specifically, donate to humanitarian aid funds you've done the research for and are sure are doing work on the ground. even better if you can donate directly to those being affected! this includes Palestinians on the ground but also within the diaspora who need self care items, especially for all the work they've been doing educating others. for example, this is an organization this member volunteers with and trusts:

and these are two amazon lists of Palestinian youth within the diaspora:

share posts by Palestinians! the big thing is really just getting the word out, sharing their perspective. Zionist propaganda is hard to penetrate so the least we can do is uplift their voices by sharing!

from one of our Jewish youth volunteers:

understand that not all Jewish people are Zionists and not all Zionists are Jewish. saying the two are equivalent is not only antisemitic but ignores the blatant statistics, like the growing number of anti-Zionist Jewish young adults in the united states for example, or the fact that the biggest supporters of israel are actually evangelicals.

to that same point, know that israel has been purposefully trying to conflate the two in order to then label anyone who does critique the state as automatically antisemitic. it is a tool.

additionally, be careful with the rhetoric you choose to spread & subscribe to (i.e., watch how they describe israel. do they refer to the people as Jews or Zionists? it can tell you a lot about how educated they are and their vague stance on the matter)

my own additions as a longstanding ally and friend of those involved:

learn your history! there is a clear attempt to distort the history of Palestine. learn what Palestine was like before israel's occupation. learn about the way pioneering Zionists openly called Zionism "colonialism" and didn't even try to hide it. learn about how discussions of the Zionist project were discussed roughly 80 years before the Holocaust ever happened. this does not mean that some Jews did not, in fact, move to Palestine in response to such a horrific event, but in the words of a Jewish mutual of mine, israel's rhetoric literally weaponizes Jewish trauma by conflating these two dates in history.

BDS movement! stands for boycott, divestment, and sanctions!

when possible, actually speak to people of Palestinian descent. like seriously. posts are great, but actually speaking to people who are knowledgeable in real time can be so helpful for getting your questions addressed, so long as you are respectful, of course. a great place to do this, not even to advertise, is actually our Discord server linked in our bio @bfpnola

know that language matters, as inconsequential as it may seem. in the words of my Palestinian, Kashmiri, and Artsakhi friends and/or mutuals, when speaking of occupations, we capitalize the occupied people's country (ex. Palestine) while not doing so for the occupier's (ex. israel) to delegitimize them.

learn about Hamas and its history/purpose. here are my notes on two podcast episodes let by Palestinians:

thank you for your ask! im sure i may think of other things later but these are my answers for now.

-- reaux (she/they)

[data-housing-solution-12]

"Unlocking Business Intelligence with Data Warehouse Solutions"

Data Warehouse Solution: Boosting Business Intelligence

A data warehouse (DW) is an organized space that enables companies to organize and assess large volumes of information through multiple locations in a consistent way. This is intended to assist with tracking, company analytics, and choices. The data warehouse's primary purpose was to render it possible to efficiently analyze past and present information, offering important conclusions for management as a business strategy. 

A data warehouse normally employs procedures   (Take, convert, load) for combining information coming from several sources, including business tables, operations, and outside data flows.This allows for an advanced level of scrutiny by ensuring data reliability and precision. The information's structure enables complicated searches, which are often achieved using the aid of SQL-based tools, BI (Business Intelligence) software, or information display systems.

Regarding activities requiring extensive research, data storage centers were ideal since they could provide executives with rapid and precise conclusions. Common application cases include accounting, provider direction, customer statistics, and projections of sales. Systems provide connectivity, speed, and easy control of networks, but as cloud computing gained popularity, data warehouses like Amazon's Redshift, Google's Large SEARCH, and Snowflake have remained famous.  

In conclusion, managing information systems is essential for companies that want to make the most out of their information. Gathering information collected in one center allows firms to better understand how they operate and introduce decisions that promote inventiveness and originality.

[womaneng]

Data Science

📌Data scientists use a variety of tools and technologies to help them collect, process, analyze, and visualize data. Here are some of the most common tools that data scientists use:

👩🏻‍💻Programming languages: Data scientists typically use programming languages such as Python, R, and SQL for data analysis and machine learning.

📊Data visualization tools: Tools such as Tableau, Power BI, and matplotlib allow data scientists to create visualizations that help them better understand and communicate their findings.

🛢Big data technologies: Data scientists often work with large datasets, so they use technologies like Hadoop, Spark, and Apache Cassandra to manage and process big data.

🧮Machine learning frameworks: Machine learning frameworks like TensorFlow, PyTorch, and scikit-learn provide data scientists with tools to build and train machine learning models.

☁️Cloud platforms: Cloud platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure provide data scientists with access to powerful computing resources and tools for data processing and analysis.

���Data management tools: Tools like Apache Kafka and Apache NiFi allow data scientists to manage data pipelines and automate data ingestion and processing.

🧹Data cleaning tools: Data scientists use tools like OpenRefine and Trifacta to clean and preprocess data before analysis.

☎️Collaboration tools: Data scientists often work in teams, so they use tools like GitHub and Jupyter Notebook to collaborate and share code and analysis.

For more follow @woman.engineer

[techniktagebuch]

2024

Mein Medien-Menü: Zwölf Jahre später

Im Februar 2012 habe ich für Christoph Kochs Reihe "Mein Medien-Menü" beschrieben, wie meine Mediennutzung damals aussah. Diese Serie ist einer der Gründe, warum es das Techniktagebuch gibt. Bis November 2014 sind insgesamt 89 Folgen im Blog von Christoph Koch erschienen. Danach zog das Medienmenü um zu Krautreporter, wo es so aussieht, als seien bis ungefähr 2017 noch mal ziemlich viele Folgen veröffentlicht worden. Ob man die gesammelt irgendwo lesen kann und ob es nach 2017 noch weiterging, weiß ich nicht, weil ein Krautreporter-Abo nicht zu meiner Mediennutzung gehört. (Ohne besondere Gründe, im ersten Krautreporterjahr war ich Unterstützerin. Ich erinnere mich vage an Unzufriedenheit, weshalb ich es danach nicht mehr war. Aber die Details sind leider undokumentiert geblieben.)

Ich habe lange nicht mehr an diesen Bericht gedacht und sehe heute noch mal nach, wie das eigentlich 2012 war und was sich geändert hat.

"Goodreads ist nicht besonders überzeugend, ich kenne nur wenige Menschen, die es nutzen, und die Buchempfehlungen dort sind nur unwesentlich besser als bei Amazon. Aber ich finde es sehr hilfreich, um eine realistische Vorstellung von meinem Leseverhalten zu bekommen. Bis ich damit anfing, hielt ich mich immer noch für denselben Leser wie 1995."

Ich war damals noch ein Leser und keine Leserin. Mit dem generischen Maskulinum habe ich erst viel später aufgehört. Im Techniktagebuch ist zu sehen, wann das passiert ist, meiner Erinnerung nach vielleicht 2018? Irgendwann sehe ich nach und dann steht es hier genauer. Goodreads fand ich zwischen damals und jetzt sehr überzeugend. Ich kenne zwar immer noch nur wenige Menschen, die es nutzen, und in die automatischen Buchempfehlungen habe ich schon lange nicht mehr reingesehen. Aber ich habe dort in den letzten Jahren sehr viele Rezensionen gelesen und das war der Hauptweg, auf dem ich zu neuen Büchern gefunden habe. Allerdings versuche ich gerade, mich (wegen der Amazon-Zugehörigkeit) von Goodreads zu lösen zugunsten von StoryGraph. Da läuft aber gerade erst der Umzug meiner Daten und ich kann noch nichts dazu sagen.

"Meine Papierbücher habe ich in den letzten paar Jahren mit Hilfe des Berliner Büchertischs stark reduziert, von ungefähr zwölf mehrreihig gefüllten Billyregalen bin ich jetzt runter auf sieben halbvolle."

Im Moment sind es vier ganz volle, davon zwei mehrreihig gefüllt. 2019 waren es auch schon nur vier. Was mit den drei anderen passiert ist, weiß ich nicht mehr. Falls es Zuwachs gegeben hat, ist das unfreiwillig passiert, durch eigene Belegexemplare, ungefragt zugeschickte Bücher und Bücher, die ich auf Papier kaufen musste, weil ich sie für die Arbeit brauchte und nicht auf einem digitalen Weg beschaffen konnte. Ich lese jetzt aber viel mehr Bücher als 2012.

Dann geht es im Text von 2012 einen Absatz lang um RSS-Feedreader. Ich habe damals noch den Google Reader genutzt, den Google anderthalb Jahre später eingestellt hat. Mit Feedly, dem Tool, mit dem ich ihn ab Mitte 2013 zu ersetzen versuchte, bin ich nie so richtig warm geworden, er ist 2016 aus meinem Leben verschwunden. Ich habe ihn nicht ersetzt und lebe seitdem feedreaderlos.

"... das, was ich im Netz lese, speist sich jetzt ungefähr (geraten und nicht gemessen, kann also auch ganz anders sein) zur Hälfte aus dem Feedreader und zur Hälfte aus dem Bekanntenkreis via Google+, Twitter und Facebook. "

"Netz" sage ich nicht mehr, seit ich 2021 erfahren habe, dass es ein altmodisches Wort für Internet ist. Ich dachte bis dahin, es sei umgekehrt.

"Ein oder zwei Jahre lang hatte ich mir für die wichtigsten Feeds eine Weiterleitung nach Twitter gebastelt (via Yahoo Pipes und Twitterfeed), aber seit es Google+ gibt, nutze ich Twitter viel weniger und sehe deshalb auch diese Weiterleitung kaum mehr."

Yahoo Pipes! Das war wirklich schön und ich vermisse es heute noch manchmal. Es wurde 2015 eingestellt. Man konnte damit, so ähnlich wie jetzt mit Zapier, andere Internetdinge zusammenstecken, aber mit einer schönen grafischen Oberfläche. Bei Google+ war ich 2011 und offenbar auch noch Anfang 2012 sehr aktiv, aber irgendwann bald danach war es wieder vorbei. Warum, weiß ich nicht mehr, es ist im Techniktagebuch nicht dokumentiert. In meiner Erinnerung wurde Google+ kurz nach dem Start wieder stillgelegt, aber das scheint nicht zu stimmen, in der Wikipedia steht: Schließung 2019. Ich bin danach zu Twitter zurückgekehrt.

Von den Blogs, die mir damals wichtig waren, gibt es ein paar noch, sie sind mir aber unsympathisch geworden (Marginal Revolution, Less Wrong, Overcoming Bias). Andere gibt es nicht mehr (Stefan Niggemeiers Blog, Penelope Trunk). Ich glaube, dass das nicht weiter besorgniserregend ist, die meisten Blogs haben eine begrenzte Lebenszeit aus inhaltlichen wie aus Verfügbare-Lebenszeit-Gründen und es wachsen ja auch wieder neue nach. Im Überschneidungsbereich von "existiert noch" und "wir haben uns nicht weltanschaulich entfremdet, glaube ich", liegt nur ein einziger der erwähnten Blogs: O'Reilly Radar. Ich lese es trotzdem nie. Das hat auch wieder mit dem Verschwinden des Google Readers zu tun. Ich lese wahrscheinlich immer noch so viel in Blogs wie früher, aber nicht mehr regelmäßig in denselben, sondern eben die Beiträge, die mir bis 2022 Twitter heranspülte und seit meinem Umzug Mastodon. Ich merke mir dann nicht, in welchem Blog die standen, und könnte keine Blognamen nennen. Facebook erwähne ich 2012 noch, 2015 habe ich das Facebook-Browsertab geschlossen und 2017 die App vom Handy gelöscht.

Zeitschriften mit der Post bekam ich 2012 noch mehrere, zum Teil wegen Vereinsmitgliedschaften und zum Teil, weil ich sie abonniert hatte. Eins der Abos habe ich gleich nach der Dokumentation im Medien-Menü-Beitrag gekündigt, ein anderes endete etwas später von allein, und die Mitgliedszeitschriften haben sich in den letzten Jahren entweder selbst auf nur-noch-digital umgestellt oder ich habe darum gebeten, nichts mehr auf Papier zu bekommen. Außerdem wird meine Post seit mehreren Jahren direkt an Nathalie weitergeleitet, die sich um meine Papierverwaltung kümmert.

2024 gehört zur finanziellen Seite meines Medien-Menüs, dass ich einige Leute regelmäßig unterstütze bei Patreon, Steady und ähnlichen Plattformen. Ich müsste das mal in einem gesonderten Beitrag genauer aufschreiben, jedenfalls ist es im Moment der Hauptkanal, auf dem Geld von mir zu Kulturschaffenden fließt. Die Newsletter oder Videos, die zu manchen dieser Abos gehören, schaue ich mir aber eigentlich nie an. Es geht mehr ums Prinzip, ich möchte, dass diese Leute weiter Videos machen, Bücher schreiben oder was sie halt so tun.

"Radio habe ich seit den 80er Jahren nicht mehr gehört (traumatische Schulbus-Erlebnisse mit Bayern 3). Eine Tageszeitung hatte ich zuletzt um 1990 im Abonnement. Ich habe aufgehört, fernzusehen, als im deutschen Kabel das britische MTV Europe durch den deutschen Ableger ersetzt wurde, das muss so um 1995 herum gewesen sein. Über Hörbücher und Podcasts weiß ich nichts, ich schlafe aus technischen Gründen beim Zuhören immer sofort ein."

Daran hat sich seit 2012 wenig geändert. Ich war viel im Haushalt meiner Mutter, und dort wird jeden Tag wenigstens eine Stunde Radio gehört (BR Heimat zwischen 22:00 und 23:00). Außerdem ist es mir gelungen, mittelgroße Teile des "Drinnies"-Podcasts zu hören. Eine Änderung meines Mediennutzungsverhaltens sehe ich darin aber nicht, das eine ist Zufall, das andere eine Ausnahme.

Video kommt im Text von 2012 gar nicht vor. Hier hat sich mehr geändert, 2016 habe ich eingesehen, wozu YouTube gut ist, und inzwischen nutze ich es oft, allerdings vor allem in der kleinen Vorschau-Ansicht auf dem Handy, die ungefähr 6x4 cm groß ist, und ohne Ton. Theoretisch folge ich dort zwar ein paar Leuten aus den Bereichen Handwerk (Schreinerei, Metallbearbeitung, Rohrreinigung) und Schlittenhundehaltung, praktisch mache ich davon aber so gut wie nie Gebrauch, es sind Höflichkeits-Abos zur Erfreuung der Youtuber*innen. Ich bin nur da, wenn ich was Bestimmtes suche und gucke dann vielleicht noch ein paar von den Dingen, die YouTube mir vorschlägt. Dabei bin ich inzwischen besser darin geworden, den Vorschlägen zu widerstehen, weil mir YouTube immer gern Katastrophen und Unglücke zeigen möchte und ich aber wirklich nicht noch mehr über scheußliche Tode beim Höhlentauchen wissen will. Lieber würde ich das vorhandene Wissen darüber wieder aus meinem Kopf löschen lassen. Was mir in meinem Medienmenü 2024 fehlt, ist ein Lösch-YouTube zur Entfernung von Informationen.

(Kathrin Passig)

[raziakhatoon]

 Data Engineering Concepts, Tools, and Projects

All the associations in the world have large amounts of data. If not worked upon and anatomized, this data does not amount to anything. Data masterminds are the ones. who make this data pure for consideration. Data Engineering can nominate the process of developing, operating, and maintaining software systems that collect, dissect, and store the association’s data. In modern data analytics, data masterminds produce data channels, which are the structure armature.

How to become a data engineer:

 While there is no specific degree requirement for data engineering, a bachelor's or master's degree in computer science, software engineering, information systems, or a related field can provide a solid foundation. Courses in databases, programming, data structures, algorithms, and statistics are particularly beneficial. Data engineers should have strong programming skills. Focus on languages commonly used in data engineering, such as Python, SQL, and Scala. Learn the basics of data manipulation, scripting, and querying databases.

 Familiarize yourself with various database systems like MySQL, PostgreSQL, and NoSQL databases such as MongoDB or Apache Cassandra.Knowledge of data warehousing concepts, including schema design, indexing, and optimization techniques.

Data engineering tools recommendations:

    Data Engineering makes sure to use a variety of languages and tools to negotiate its objects. These tools allow data masterminds to apply tasks like creating channels and algorithms in a much easier as well as effective manner.

1. Amazon Redshift: A widely used cloud data warehouse built by Amazon, Redshift is the go-to choice for many teams and businesses. It is a comprehensive tool that enables the setup and scaling of data warehouses, making it incredibly easy to use.

One of the most popular tools used for businesses purpose is Amazon Redshift, which provides a powerful platform for managing large amounts of data. It allows users to quickly analyze complex datasets, build models that can be used for predictive analytics, and create visualizations that make it easier to interpret results. With its scalability and flexibility, Amazon Redshift has become one of the go-to solutions when it comes to data engineering tasks.

2. Big Query: Just like Redshift, Big Query is a cloud data warehouse fully managed by Google. It's especially favored by companies that have experience with the Google Cloud Platform. BigQuery not only can scale but also has robust machine learning features that make data analysis much easier. 3. Tableau: A powerful BI tool, Tableau is the second most popular one from our survey. It helps extract and gather data stored in multiple locations and comes with an intuitive drag-and-drop interface. Tableau makes data across departments readily available for data engineers and managers to create useful dashboards. 4. Looker:  An essential BI software, Looker helps visualize data more effectively. Unlike traditional BI tools, Looker has developed a LookML layer, which is a language for explaining data, aggregates, calculations, and relationships in a SQL database. A spectacle is a newly-released tool that assists in deploying the LookML layer, ensuring non-technical personnel have a much simpler time when utilizing company data.

5. Apache Spark: An open-source unified analytics engine, Apache Spark is excellent for processing large data sets. It also offers great distribution and runs easily alongside other distributed computing programs, making it essential for data mining and machine learning. 6. Airflow: With Airflow, programming, and scheduling can be done quickly and accurately, and users can keep an eye on it through the built-in UI. It is the most used workflow solution, as 25% of data teams reported using it. 7. Apache Hive: Another data warehouse project on Apache Hadoop, Hive simplifies data queries and analysis with its SQL-like interface. This language enables MapReduce tasks to be executed on Hadoop and is mainly used for data summarization, analysis, and query. 8. Segment: An efficient and comprehensive tool, Segment assists in collecting and using data from digital properties. It transforms, sends, and archives customer data, and also makes the entire process much more manageable. 9. Snowflake: This cloud data warehouse has become very popular lately due to its capabilities in storing and computing data. Snowflake’s unique shared data architecture allows for a wide range of applications, making it an ideal choice for large-scale data storage, data engineering, and data science. 10. DBT: A command-line tool that uses SQL to transform data, DBT is the perfect choice for data engineers and analysts. DBT streamlines the entire transformation process and is highly praised by many data engineers.

Data Engineering  Projects:

Data engineering is an important process for businesses to understand and utilize to gain insights from their data. It involves designing, constructing, maintaining, and troubleshooting databases to ensure they are running optimally. There are many tools available for data engineers to use in their work such as My SQL, SQL server, oracle RDBMS, Open Refine, TRIFACTA, Data Ladder, Keras, Watson, TensorFlow, etc. Each tool has its strengths and weaknesses so it’s important to research each one thoroughly before making recommendations about which ones should be used for specific tasks or projects.

  Smart IoT Infrastructure:

As the IoT continues to develop, the measure of data consumed with high haste is growing at an intimidating rate. It creates challenges for companies regarding storehouses, analysis, and visualization. 

  Data Ingestion:

Data ingestion is moving data from one or further sources to a target point for further preparation and analysis. This target point is generally a data storehouse, a unique database designed for effective reporting.

 Data Quality and Testing: 

Understand the importance of data quality and testing in data engineering projects. Learn about techniques and tools to ensure data accuracy and consistency.

 Streaming Data:

Familiarize yourself with real-time data processing and streaming frameworks like Apache Kafka and Apache Flink. Develop your problem-solving skills through practical exercises and challenges.

Conclusion:

Data engineers are using these tools for building data systems. My SQL, SQL server and Oracle RDBMS involve collecting, storing, managing, transforming, and analyzing large amounts of data to gain insights. Data engineers are responsible for designing efficient solutions that can handle high volumes of data while ensuring accuracy and reliability. They use a variety of technologies including databases, programming languages, machine learning algorithms, and more to create powerful applications that help businesses make better decisions based on their collected data.

[mightyaphrodytee]

There were a few times in my life when music changed for me—what I responded to changed slowly over time, but yeah, there were definite infusions of NEW that veered off on paths maybe not so well-trodden, but that nonetheless stood out as touchstones in my ~~~dramatic half-whisper~~~ journey through 🎶MUSIC 🎼

1977: Heard the best of what’s now considered “classic rock” as it existed at the time, when it was just called “Rock” or “Heavy Metal” or “Prog.” Bands like Rush, Boston, Yes, Queen, Led Zeppelin, Black Sabbath, Pink Floyd, that didn’t get a lot of airplay on the Top 40 stations I’d exclusively listened to. It was thrilling. I caught up on ten years of ignorance in like, 9 months. But I kinda missed out on punk because of that immersion, thanks to my new besties.

1982: Heard my first indie/alternative (“new wave” to some) music and fell hard. The Cure, The English Beat, Joy Division, Kim Wilde, Elvis Costello, U2, Talking Heads, etc. when we moved to Colorado. The availability of some truly esoteric indie music via the Boulder station KBCO was legendary. We had three or four stations in addition to that one! Spoiled! The eighties, man. R.E.M.!!! The music in the clubs was what was on the radio was what was on MTV—you couldn’t escape it, so this huge subset of the rock-listening population were all listening to the big hits at the same time. Madonna, Dire Straits, The Eurythmics, Prince, Duran Duran, Pretenders, Bon Jovi. EVERYBODY knew the hits of the eighties.

1991: Heard “Smells Like Teen Spirit” on the car radio driving through Austin, and both my companion and I were immediately silenced by that intro, and by the end, we were like “What just happened?” just in total delight/light shock…did he really just scream about a mulatto? Who talks like that in 1991, sir? But we just immediately knew this was gonna be huge, and it was, and then came grunge and grunge-lite for the rest of the decade. Soundgarden, STP, Bush, Incubus, Alice In Chains, Pearl Jam, Nirvana (for such a goddamned short time, it’s insane to look back and realize we had so few years with him!)

For some people, life is unbearable without having their consciousness altered in some way. Drugs being one of those ways.

2003: Heard “Caring Is Creepy” by The Shins on a 4-hour “New Alternative” loop XM Radio had handed out as a free trial. Those songs on that loop woke me up to the possibility of new sounds that hit that same place in me as the best of the 80’s and 90’s. I remember Doves “Pounding”, which was used in an episode of The Consultant on Amazon Prime just this week (I shrieked!), “Silver Spoon” by Bis, “Shapes” by The Long Winters, The Postal Service, Death Cab For Cutie…wish I could remember them all. Bruce Springsteen’s Magic album had a song that was my most played for a few years in the aughts—“Radio Nowhere”, which I first heard on that XM trial loop and loved so much I bought the whole album. On iTunes. Still have it. Saw Garden State, heard “Caring Is Creepy” on the soundtrack (again—i shrieked!), and “New Slang,” and fell for them even harder.

Now I listen to what I used to hate (classic rock), but my fairly narrow preference window means I don’t SAY I listen to classic rock, because except for YouTube, I only listen to Radiohead, some Tool, some Metallica most days.

My life is now just mainly Radiohead with a few dollops of all the songs I’ve loved before, from every decade that rock and roll has been rock and roll with ALL its subgenres, heavy on Tool and Metallica as of late.

I can’t even tell what popular music today even is. It all sounds like video game background to me.

Will you still need me

Will you still feed me

When I’m 64?

[sejalkumar]

Top Technical Skills to Include on Your Job Application 

In competitive job market, standing out in your job application is crucial. One of the best ways to make your application shine is by highlighting the right skills to put on a job application, especially technical skills. These skills not only demonstrate your proficiency in specific areas but also show potential employers that you’re ready to handle the demands of the role. Whether you’re applying for a tech position or a non-technical role that requires technical know-how, including the right technical skills can make all the difference. In this article, we’ll explore the top technical skills you should consider putting on your job application.

Programming Languages

Programming languages are often at the core of technical roles in the IT and software development industries. Including popular programming languages on your job application demonstrates your ability to write and understand code. Some of the most in-demand programming languages include:

Python: Known for its simplicity and versatility, Python is widely used for web development, data science, automation, and machine learning.

JavaScript: This language is essential for front-end web development, allowing developers to create interactive websites and applications.

Java: A staple in the enterprise environment, Java is used in everything from mobile app development (Android) to large-scale backend systems.

C++: While more complex, C++ is crucial for developing high-performance applications like games and operating systems.

Including these programming languages in your application can significantly boost your chances of being noticed, especially in technology-centric roles.

Data Analysis and Analytics Tools

As companies continue to rely on data to drive decisions, data analysis skills have become increasingly valuable across industries. If you’re applying for a role that involves handling data, mentioning your proficiency with data analysis tools is a must. Some of the most relevant tools include:

Microsoft Excel: A foundational skill for data manipulation, Excel is essential for anyone working with data, from basic calculations to complex data modeling.

SQL: Structured Query Language (SQL) is a fundamental skill for managing and querying databases. Knowing SQL allows you to extract, analyze, and manipulate large datasets.

Google Analytics: For marketing and web analytics roles, experience with Google Analytics shows your ability to measure website performance and customer engagement.

Tableau/Power BI: These business intelligence tools allow you to create interactive data visualizations and reports, which are valuable in decision-making processes.

Including these skills to put on a job application can help you stand out to employers who rely heavily on data to make informed decisions.

Cloud Computing

With the rise of cloud-based technologies, cloud computing has become an essential skill in many industries. Whether you’re in software development, IT, or digital marketing, cloud computing platforms are integral to business operations. Some of the most popular cloud technologies include:

Amazon Web Services (AWS): AWS is a leader in the cloud services market, offering everything from computing power to storage and machine learning tools.

Microsoft Azure: As a competitor to AWS, Azure provides cloud services that are especially important in enterprise environments, particularly for businesses already using Microsoft products.

Google Cloud Platform (GCP): Known for its strong data processing and machine learning capabilities, GCP is another key player in the cloud computing space.

If you have experience working with any of these platforms, including them on your job application can help you stand out, especially in tech-related roles.

Cybersecurity Skills

As cyber threats continue to evolve, cybersecurity expertise has become critical for businesses across industries. Demonstrating proficiency in cybersecurity skills can make you highly attractive to potential employers, especially those in industries like finance, healthcare, and technology. Key cybersecurity skills to highlight include:

Network Security: Understanding how to protect a company’s network infrastructure from attacks and breaches.

Cryptography: Knowledge of encryption methods to protect sensitive data.

Risk Management: Assessing and mitigating security risks within a company’s infrastructure.

Incorporating these skills to put on a job application shows that you understand the importance of securing sensitive information and can contribute to an organization’s data protection efforts.

Software Development Tools

If you’re applying for a software development role, familiarity with certain tools and environments is crucial. Tools like version control systems and integrated development environments (IDEs) are foundational in most programming environments. Some popular tools include:

Git/GitHub: Git is a version control system that tracks changes in code, while GitHub is a platform for collaborating on projects. These tools are essential for developers working in teams.

Docker: A tool used to create, deploy, and run applications inside containers, making development more efficient and consistent across different environments.

JIRA: JIRA is a project management tool used in software development to track tasks, bugs, and features, especially in agile development environments.

These tools demonstrate your ability to work collaboratively, stay organized, and manage complex development workflows, all of which are highly valued by employers.

Conclusion

Including the right skills to put on a job application can significantly impact your chances of landing the job. Whether you’re applying for a technical role or a position that requires some technical knowledge, showcasing your proficiency in programming languages, data analysis tools, cloud computing, cybersecurity, and software development tools is key to standing out in today’s job market. By strategically highlighting these technical skills, you can demonstrate your qualifications and increase your chances of securing your desired role. Remember to tailor your application to the specific job description, ensuring that you emphasize the most relevant skills for the position.

[itjobboard789]

Data Scientist Jobs in the UK: Your Gateway to a Thriving Career

Why Data Scientist Jobs Are in High Demand

The Data Scientist jobs UK has positioned data science as a critical field for businesses across industries. Companies in the UK are leveraging vast amounts of data to gain a competitive edge, and data scientists play a pivotal role in this transformation. Skilled professionals analyze complex datasets, derive actionable insights, and contribute to data-backed decisions.

Benefits of Pursuing a Data Scientist Career in the UK

1. Lucrative Salaries

Data scientist roles consistently rank among the top-paying jobs in the tech sector. According to industry reports, the average salary for a data scientist in the UK is around £55,000 annually, with experienced professionals earning upwards of £90,000.

2. Job Security and Growth

With the growing reliance on data analytics, the demand for skilled data scientists is unlikely to diminish. Industries such as healthcare, finance, e-commerce, and technology are actively seeking experts to harness the power of data.

3. Cutting-Edge Technology

Data scientists have access to advanced tools like Python, R, machine learning models, and AI frameworks. Working in this field allows you to stay at the forefront of technological innovation.

4. Versatile Career Opportunities

Data science opens doors to roles such as machine learning engineer, data analyst, big data engineer, and AI specialist. Each offers unique challenges and rewards.

Key Skills Required for Data Scientist Jobs in the UK

1. Technical Proficiency

Programming Languages: Master Python, R, or Java for data manipulation.

Data Visualization Tools: Tools like Tableau or Power BI enhance communication of insights.

Big Data Frameworks: Expertise in Hadoop, Spark, or Hive is a strong asset.

2. Mathematics and Statistics

A solid foundation in linear algebra, probability, and statistics is crucial for developing machine learning algorithms and interpreting data accurately.

3. Business Acumen

Understanding industry-specific challenges ensures that data-driven solutions align with business objectives.

4. Problem-Solving Skills

Analytical thinking and creativity enable data scientists to develop innovative solutions to complex problems.

Industries Hiring Data Scientists in the UK

1. Healthcare

Predictive analytics helps in diagnosing diseases and improving patient care. The NHS and private hospitals are key employers.

2. Finance

Banks and financial institutions leverage data science for fraud detection, risk analysis, and algorithmic trading.

3. E-Commerce

Platforms like Amazon and eBay use data science to enhance customer experiences and optimize supply chains.

4. Technology

Companies like Google and Microsoft in the UK are heavily investing in AI-driven projects.

Steps to Launch Your Career as a Data Scientist in the UK

1. Pursue Relevant Education

Earn a degree in data science, computer science, mathematics, or related fields. Many UK universities offer specialized programs tailored to data science careers.

2. Gain Certifications

Enhance your profile with certifications in machine learning, data analytics, or specific tools like AWS or TensorFlow.

3. Build a Strong Portfolio

Showcase your expertise by working on real-world projects. Participate in hackathons or contribute to open-source data science initiatives.

4. Network and Apply

Leverage platforms like LinkedIn and attend industry conferences to connect with recruiters. Job boards such as Indeed, Glassdoor, and specialized UK-based platforms are excellent for finding openings.

Challenges in the Field

Data Privacy Concerns: Navigating GDPR compliance is essential.

Skill Gaps: Staying updated with evolving technologies is crucial.

Data Quality Issues: Ensuring clean and accurate data remains a persistent challenge.

Diagram: Data Science Workflow

mermaid

Copy code

graph TD A[Collect Data] --> B[Clean Data] B --> C[Explore Data] C --> D[Build Model] D --> E[Evaluate Model] E --> F[Deploy Model] F --> G[Monitor and Update]

[jcmarchi]

John Forstrom, Co-Founder & CEO of Zencore – Interview Series

New Post has been published on https://thedigitalinsider.com/john-forstrom-co-founder-ceo-of-zencore-interview-series/

John Forstrom, Co-Founder & CEO of Zencore – Interview Series

Zencore is a premier Google Cloud consulting and engineering partner, empowering organizations to succeed through expert guidance, comprehensive services, and a relentless focus on risk reduction and client success.

John Forstrom is Zencore’s C-Founder and CEO, he is focused on helping companies make the transformation to cloud based services.

An early believer in Cloud, John joined AWS cloud management software company RightScale in 2009. While many were doubting the use of cloud computing beyond startups, this experience provided him with a front row seat to the shadow adoption of AWS and value of IaaS in large organizations.

In 2013 John joined Google Cloud as part of the initial business team working with product and engineering on the strategy for large enterprises and digital natives.

When John is not making all the connections between Zencore’s customers, partners and Google he can be found on the nearest body of water (surfing, fishing, swimming, paddling).

For over 5 years you worked at Google Cloud, what were some of your responsibilities and what were some of the key highlights from this period?

I joined Google Cloud in September of 2013 when the Cloud division was just a small startup inside of Google. I was one of the first external hires for a business development team that worked with product and engineering to acquire the initial large, strategic customers.

It was a pretty unique time at Google Cloud in which a few hundred of us (now the business is 35k+ employees) were working hand in hand to compete against AWS, which at the time had a much more mature offering. We were 100% focused on the customer and acted as trusted advisors to the early adopters. These companies knew Google Cloud didn’t have feature parity with Amazon, but found value in having a seat at the table as Google built their products and prioritized features.

The highlight for me was in 2015 when I secured a contract for one of the first billion dollar revenue Google Cloud customers.

Can you share more about the genesis of Zencore and what motivated you as a former Google insider to start a company focused exclusively on Google Cloud services?

I think what we have created at Zencore is pretty special, but the concept is rather simple. More than half the company is ex-Google and we have lived and breathed the complexity of clients going from zero to having a significant footprint in Google Cloud.

We took that experience from inside the machine and created a company to solve the major challenges that clients face as they start their journey or ramp on Google Cloud. For me personally and many of us at Zencore it’s refreshing to not have any limitations between us and doing the right thing for customers every time. We make fast decisions and get the right people involved. Zencore is designed to be a throwback to those early days of Google Cloud.

Additionally, our experience with the partner ecosystem during our time at Google consisted mainly of partners who didn’t start with Cloud. So many of Google’s partners started with Workspace, AWS or IT services and extended that to a Google Cloud practice. The ecosystem has definitely matured, but the opportunity for us was to create a business focused only on Google Cloud engineering from the beginning. Our premise was a partner organization that does one thing really really well would make the biggest impact for Google and its customers.

Zencore has chosen to specialize solely in Google Cloud from its inception. What unique opportunities and challenges does this specialization present in the rapidly evolving cloud market?

When you align your company to a single vendor, there is inherent risk in that approach. However, the risk is not significant given Google Cloud’s growth, broad data and infrastructure product portfolio and investment in Gen AI. We are still relatively early in the global adoption of public cloud services and we are very comfortable betting on Google as one of the two long term winners.

The upside to having an entire company focusing on one thing is we are all rowing in the same direction all day, every day. The collaboration between our engineers is such a powerful part of our culture and that only comes from everyone working to solve similar challenges with our clients. When you have delivered hundreds of Google Cloud infrastructure, data and Gen AI projects, there’s not a lot that we haven’t seen which is really powerful when you are working on a complex, high risk engagement.

You are right that the market moves very quickly and we feel like that singular focus on Google allows us to stay current and provide the most value to our clients.

You emphasize a customer-centric and opinionated approach in your services. How does this philosophy translate into tangible benefits for your clients, especially when considering the integration of open-source solutions?

Zencore’s clients are buying experience from a trusted advisor. When they start a project that has significant risk, they want to know that we are 100% aligned with their interests and sometimes that includes sharing some hard truths. Many times the recommendations we make are to not use a Google Cloud native product because an open source option is the best solution. I think that scenario is more rare than you would think. Google has done a really good job of building managed products on top of widely adopted open source solutions that have low operational overhead and are integrated well with the rest of the platform.

But in each one of these conversations we lay out the benefits and challenges of all the options based on real life experience. The client benefits from this approach when speed is critical. There are so many decisions to make and when we recommend a Google Cloud native product for example, the client doesn’t need to spend time second guessing the decision or wasting cycles doing an evaluation. They know we bring an independent, experienced lens to every decision we make.

Your innovative support model that bypasses traditional ticketing systems has been praised by many. Could you elaborate on how this model enhances operational efficiency and client satisfaction?

I like to joke that one of the biggest benefits of working with Zencore is that none of us have a professional services background. The reality is that we don’t do things because that’s the way they have always been done. Our reseller support offering is a great example of one area in which we have taken an innovative approach.

Many of our clients are mid-to-large size software companies. They have experienced engineers, want to move fast, but sometimes they get stuck.

The last thing they want to do when they have a consultative question is to open a ticket, get triaged by an inexperienced support rep, escalate and have that process take a day or two. It’s a total waste of their time and they end up not engaging with a partner’s support offering.

So we created a model to fit into how they work today. Every client get’s a dedicated Slack channel. On the backend of that channel is the entire engineering staff at Zencore.

So when you ask us a deeply technical question, in 15-30 minutes you are Slacking with an experienced cloud engineer or architect directly who will help to unblock your challenge. In addition, many of the questions we receive are less Google Cloud related than they are about the technology that the customer is connecting to Google like Terraform or a particular CI/CD product. It’s that intersection of the customer’s stack and Google Cloud that can be the most complex.

Direct access to our engineers is like gold to our clients. Rather than struggle with an issue, search stack overflow and get frustrated, they ping a channel and immediately get help from an engineer who has worked on dozens of complex projects.

Our clients have described it as “the next best thing to having direct Slack access with Google.”

What are the most common pitfalls companies face when migrating to cloud technologies, and how does Zencore help navigate these challenges?

We have thought a lot about this question and last year came up with five of the most common pitfalls to cloud migrations.

Not understanding workload needs and insufficient application assessment. Existing workloads might behave unpredictably in a new cloud environment. This can lead to performance issues and failed application migrations.

Insufficient implementation and strategy development. Improper implementation or strategy development can lead to downtime, cost overruns, and a mismatch between an organization’s goals and the outcomes from its cloud implementation.

Security and compliance considerations. Insufficient security and compliance considerations can lead to breaches and fines, as well as a loss of customer goodwill, revenue, and data.

Lack of cost optimization and poor resource management. Without a proper understanding of billing, costs, and how to maximize the return on cloud resource spending, cloud costs can fail to align with business objectives.

Skill gaps. Skill gaps can lead to a domino effect of problems, including poorly designed architecture, inefficient resource allocation, security vulnerabilities, and, ultimately, project failure.

Zencore prioritizes an outcome-based approach that focuses on quickly getting hands-ons with our clients. We want the strategy and architecture to be well thought out, but you cannot spend your time in endless workshops run by consultants. These five pillars best describe our overall methodology.

A deep understanding of the cloud platform. We know Google Cloud inside and out, including key areas like data cloud, machine learning, AI, and Kubernetes.

Proven methodologies. Our streamlined assessment, planning, and migration processes minimize unplanned downtime and reduce the impact on your staff.

The ability to guide the selection of the right intial cloud project tailored for success. We guide you in selecting and planning cloud projects that are set up for success, especially during early phases like evaluating workload migrations.

Expertise in cloud security. We help minimize risks with our deep knowledge of cloud security, protecting you from data breaches and other costly issues.

Hands on development capabilities. We are outcome oriented, and bring the engineering resources needed to get your solution deployed and running in production

With the cloud technology landscape continuously evolving, what emerging trends do you believe will significantly impact how organizations adopt and utilize Google Cloud in the next few years?

I think we are on a journey here in the constantly evolving cloud space. I’ll describe it in 3 steps, and I believe we’re somewhere in between step 2 and 3.

First, we all experienced the shift from Infrastructure as a Service (IaaS) to Platform as a Service (PaaS). Companies are increasingly favoring PaaS solutions because they simplify the development process, reduce the need for managing underlying infrastructure, and accelerate time-to-market. Google Cloud’s PaaS offerings, such as Cloud Run, allow developers to focus more on coding and less on maintenance, which fosters innovation and efficiency.

Second, the rise of managed services is transforming the way organizations handle their cloud operations. Managed services like Google Kubernetes Engine (GKE), Cloud SQL and BigQuery take the burden of routine management tasks off the shoulders of IT teams. This shift not only improves operational efficiency but also ensures higher levels of reliability and security. By leveraging these managed services, organizations can allocate more resources towards strategic initiatives rather than routine upkeep.

Lastly, the integration of generative AI is set to revolutionize business operations across various industries. Google Cloud’s AI and machine learning services, including the new generative AI models, empower businesses to harness advanced analytics, automate complex processes, and enhance customer experiences. For example, tools like Vertex AI make it easier for companies to develop and deploy sophisticated AI models, driving innovation and creating new value propositions.

This is just the beginning of the age of AI in everyday life for organizations running on Google Cloud and it’s definitely where we see a lot of momentum. To that end we built a set of services at Zencore we call Zen AI to help companies building AI applications or integrating AI into their existing processes.

How has your background at Google influenced your leadership style at Zencore, and what key qualities do you look for when assembling your team of cloud experts?

It’s a great question. When you look at the SRE organization at Google the Individual Contributors (ICs) are the most important part of the organization, not the managers. The ICs are highly paid, well respected and make things work without a lot of oversight. They are truly the special forces inside of Google.

What I learned is that if you hire the right people things actually work very well without a dedicated people management layer at our size. I think that one of the most unique things about Zencore is that there are no individuals whose only job is to manage people. We are an assembly of ICs who are still pretty good at their area of expertise that lead others who may be a little less experienced. Creating a company of leaders instead of a company of managers has become a key component to the culture we have created. You respect your manager because in most cases he or she is more experienced at their job and still performing it at a very high level. It’s a very collaborative approach.

From an engineering perspective, we have very high standards. We review so many resumes and they all look similar with the standard Google Cloud professional certifications listed. We generally don’t care how many certs you have obtained. What matters to us when we are hiring an architect or engineer is significant practical experience with Google Cloud. Your experience with migrations, ML ops, building a Kubernetes Operator or your depth with complex data environments leveraging BigQuery are what’s meaningful to Zencore and its clients.

Could you share a case study where Zencore’s approach significantly improved a client’s business outcomes through cloud adoption?

Although migration work is a key component of our business, it’s the data platform engagements that really stand out when you’re talking about value to the business.

One project that really stands out is a complex engagement that involved working with a company that was made up of a diverse portfolio of software brands. They were struggling with operational inefficiencies and an incomplete view of their business due to data being siloed across their various brands. This led to inconsistent data standards and made it difficult for them to gain actionable insights.

When Zencore came on board, our primary goal was to consolidate these disparate data sources and build a highly scalable data platform on Google Cloud Platform. We tackled this challenge through several key initiatives:

First, we migrated their various databases, including Redshift and SQL Server, to BigQuery. This step unified their data landscape, making it easier and more efficient for them to access and analyze their data.

Next, we focused on enhancing their data ingestion and validation processes. By implementing and automating their data job orchestration and integrating CI/CD pipelines, we ensured that their data ingestion was reliable and timely. This setup also improved the data validation checks, which are crucial for maintaining data integrity.

We also standardized their data modeling using DBT, which is an open source tool that allows you to develop data transformation models in a version controlled, easy to understand manner. . This allowed a standardization of data models across the many disparate brands, which made data analysis and reporting much easier for their teams across their portfolio.

Additionally, we consolidated multiple BI tools into a single Looker environment on GCP. This move streamlined their reporting processes and provided a unified platform for generating insights across all their portfolio companies.

The impact of these efforts was transformative. Our client now has a consolidated data environment, which gives them a comprehensive view of their business operations. This unified data platform has significantly improved their strategic decision-making capabilities and operational efficiency. Furthermore, this transformation enabled them to develop a new strategy to monetize their data, creating a new revenue stream and providing them with a strategic advantage in the market.

Looking ahead, what are your long-term goals for Zencore, and how do you plan to evolve your services to meet the future needs of your clients?

The market moves so fast that I’m not joking when I say six months is long term. I think the biggest opportunity for both Zencore and Google Cloud is with Generative AI. We have moved quickly past the hype phase and are now working on projects with real operational value that will go into production. And the value of Gen AI is so compelling that it’s putting massive pressure on organizations to get their data house in order to leverage the technology. The risk of not engaging and understanding the value of Gen AI is that your competition will use it to leapfrog you in the market.

So Zencore is doing several things to address this opportunity. One is to continue to invest in the right architects and engineers that have experience across a broad set of industries and use cases focused on things like RAG, enterprise search and of course Google products like Vertex AI.

You will also see us take a much more vertical approach, which is something historically we have not done. When you solve a specific challenge for one client in an industry using Gen AI, the reality is that you have done 80% of the work to solve the challenge for a significant number of clients in the industry. This is a unique advantage for us when time to market is critical.

Finally you will see us make a significant investment in our data cloud practice. Zencore will always have a 360 degree approach to Gen AI projects and be ready to focus on the infrastructure, security, data pipelines and ml ops to ensure a successful end to end production solution.

Thank you for the great interview, readers who wish to learn more should visit Zencore.

[amazonquicksighttraining]

Amazon QuickSight Training | AWS QuickSight Training in Hyderabad

Amazon QuickSight Training: 10 QuickSight Tips & Tricks to Boost Your Data Analysis Skills

Amazon QuickSight is a powerful business intelligence (BI) tool that empowers organizations to create interactive dashboards and gain valuable insights from their data. Whether you’re new to Amazon QuickSight or looking to enhance your existing skills, mastering its advanced features can significantly improve your data analysis capabilities. This guide focuses on ten essential tips and tricks to elevate your expertise in Amazon QuickSight Training and make the most out of this tool. 

1. Understanding the Basics with Amazon QuickSight Training 

To effectively use QuickSight, start by understanding its fundamentals. Amazon QuickSight Training provides a thorough overview of key functionalities such as connecting data sources, creating datasets, and building visualizations. By mastering the basics, you set the foundation for leveraging more advanced features and gaining meaningful insights. 

2. Optimize Data Preparation 

One of the first steps in data analysis is data preparation. Use Amazon QuickSight's in-built tools to clean, transform, and model your data before creating visualizations. Features like calculated fields and data filters are especially useful for creating precise datasets. AWS QuickSight Online Training covers these capabilities in detail, helping you streamline the preparation process. 

3. Mastering Visual Customization 

Effective data presentation is critical for analysis. With QuickSight, you can create highly customized visuals that align with your specific needs. Learn to adjust colors, axes, and data labels to make your charts more intuitive and visually appealing. Amazon QuickSight Training emphasizes the importance of tailoring visuals to improve storytelling and engagement. 

4. Utilize Advanced Calculations 

QuickSight supports advanced calculations, such as percentiles, running totals, and custom metrics. Leveraging these features allows you to derive deeper insights from your data. AWS QuickSight Training provides step-by-step guidance on creating advanced formulas, which can save time and add value to your analysis. 

5. Enable Auto-Narratives for Insights 

Auto-narratives in QuickSight use natural language processing (NLP) to generate textual summaries of your data. This feature is particularly useful for highlighting trends, anomalies, and key performance indicators (KPIs). AWS QuickSight Online Training teaches how to enable and customize auto-narratives for improved decision-making. 

6. Take Advantage of SPICE Engine 

QuickSight’s Super-fast, Parallel, In-memory Calculation Engine (SPICE) is designed for speed and efficiency. It enables users to analyze massive datasets without relying on external databases. Learning how to optimize SPICE usage is an integral part of Amazon QuickSight Training and ensures you can work with data at scale. 

7. Implement Conditional Formatting 

Conditional formatting helps draw attention to critical data points. You can set rules to highlight values based on specific conditions, making your dashboards more actionable. AWS QuickSight Training explores how to implement these rules to enhance the interpretability of your visuals. 

8. Share and Collaborate Effectively 

QuickSight makes it easy to share dashboards and reports with stakeholders. By learning best practices for sharing, including granting permissions and scheduling email reports, you ensure that insights reach the right audience. AWS QuickSight Online Training includes collaboration techniques to improve team workflows. 

9. Use Embedded Analytics 

Embedding QuickSight dashboards into applications or websites is a game-changer for businesses. This feature allows organizations to provide real-time insights to users within their existing platforms. Amazon QuickSight Training delves into embedding analytics, offering practical examples to integrate dashboards seamlessly. 

10. Stay Updated with New Features 

AWS QuickSight regularly updates its features to enhance user experience and functionality. Staying informed about these updates through AWS QuickSight Training ensures you are always utilizing the latest tools and capabilities to boost productivity and efficiency. 

Conclusion: Amazon QuickSight is a versatile and user-friendly BI tool that caters to a wide range of data analysis needs. By leveraging these tips and tricks, you can unlock the full potential of QuickSight and deliver impactful insights. Whether you're new to BI or an experienced analyst, Amazon QuickSight Training equips you with the skills to excel in data visualization and reporting. 

AWS QuickSight Online Training and AWS QuickSight Training courses are invaluable resources for professionals looking to stay competitive in today’s data-driven world. From mastering visual customization to utilizing SPICE and embedding analytics, QuickSight offers endless opportunities for growth and innovation. With continuous learning and practice, you can transform raw data into actionable intelligence, empowering your organization to make informed decisions and drive success.

Visualpath is a top institute in Hyderabad offering AWS QuickSight Online Training with real-time expert instructors and hands-on projects. Our Amazon QuickSight Course Online, from industry experts and gain experience. We provide to individuals globally in the USA, UK, etc. To schedule a demo, call +91-9989971070. 

Key Points: AWS, Amazon S3, Amazon Redshift, Amazon RDS, Amazon Athena, AWS Glue, Amazon DynamoDB, AWS IoT Analytics, ETL Tools.

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[cloudastra1]

Unlocking Big Data Potentials with AWS EMR

AWS EMR: Unlocking Big Data Potential with Scalable Cloud Solutions

Amazon Web Services (AWS) Elastic MapReduce (EMR) is a powerful cloud-based service that simplifies processing vast amounts of data. By leveraging scalable computing power and integrated tools, AWS EMR enables organizations to perform big data analysis and processing efficiently and cost-effectively. This blog explores the core features, benefits, and use cases of AWS EMR, highlighting its role in transforming how businesses handle big data.

1. Understanding AWS EMR

AWS EMR is a cloud-native platform designed to process and analyze large data sets using open-source tools like Apache Hadoop, Spark, HBase, and Presto. It provides a managed environment where users can easily set up, operate, and scale big data frameworks, eliminating the complexity associated with on-premises infrastructure management.

2. Core Features of AWS EMR

a. Scalability: AWS EMR offers automatic scaling capabilities, allowing clusters to expand or shrink based on the workload. This flexibility ensures optimal resource utilization and cost savings.

b. Managed Service: As a fully managed service, AWS EMR handles cluster provisioning, configuration, and tuning. It also provides automatic software updates and security patches, freeing users from administrative burdens.

c. Integration with AWS Services: EMR integrates seamlessly with other AWS services like S3 (Simple Storage Service) for data storage, EC2 (Elastic Compute Cloud) for computing power, and IAM (Identity and Access Management) for secure access control.

d. Cost Efficiency: With EMR’s pay-as-you-go pricing model, users only pay for the resources they consume. This approach significantly reduces costs compared to maintaining on-premises infrastructure.

e. Flexibility: EMR supports a variety of open-source frameworks, giving users the flexibility to choose the right tools for their specific data processing needs.

3. Benefits of AWS EMR

a. Speed and Performance: EMR’s distributed computing model accelerates data processing tasks, enabling faster insights and decision-making. High-performance frameworks like Apache Spark further enhance processing speeds.

b. Simplified Management: The managed nature of EMR reduces operational complexity, allowing data engineers and scientists to focus on analysis and innovation rather than infrastructure management.

c. Security and Compliance: AWS EMR offers robust security features, including data encryption at rest and in transit, IAM policies for access control, and compliance with industry standards like HIPAA and GDPR.

d. Versatility: EMR is versatile enough to handle a wide range of data processing tasks, from batch processing and data transformations to machine learning and real-time analytics.

4. Common Use Cases for AWS EMR

a. Data Warehousing: Organizations can use EMR to transform raw data into structured formats, enabling efficient data warehousing and reporting. Integrations with AWS Redshift and other BI tools facilitate advanced analytics and business intelligence.

b. Log and Event Analysis: EMR is ideal for analyzing large volumes of log data generated by applications, systems, and devices. By processing this data, organizations can identify trends, detect anomalies, and enhance operational visibility.

c. Machine Learning: Data scientists can leverage EMR to preprocess and analyze data sets, train machine learning models, and perform feature engineering. Integration with AWS SageMaker simplifies the deployment and management of these models.

d. Genomics and Life Sciences: EMR’s powerful processing capabilities support complex bioinformatics workflows, such as genomic sequencing and analysis. This enables researchers to accelerate scientific discoveries and medical advancements.

5. Getting Started with AWS EMR

a. Creating an EMR Cluster: To get started, users can create an EMR cluster through the AWS Management Console, AWS CLI, or SDKs. They can specify the number and type of instances, select the desired applications, and configure security settings.

b. Data Ingestion: Data can be ingested into EMR clusters from various sources, including S3, RDS (Relational Database Service), and Kinesis. EMR’s integration with AWS Glue simplifies data cataloging and ETL (Extract, Transform, Load) processes.

c. Running Jobs: Users can submit data processing jobs to EMR clusters using frameworks like Apache Hadoop MapReduce, Apache Spark, or Apache Hive. EMR handles job scheduling, monitoring, and error recovery.

d. Monitoring and Optimization: AWS provides tools like CloudWatch and the EMR Console to monitor cluster performance and resource utilization. Users can optimize costs and performance by adjusting instance types, cluster size, and job parameters.

6. Best Practices for AWS EMR

a. Optimize Storage: Utilize S3 for data storage to take advantage of its scalability, durability, and cost-effectiveness. Configure EMR to use S3 as a data source and sink.

b. Right-size Instances: Choose appropriate instance types based on workload requirements. Use spot instances for cost savings, and reserve instances for predictable, long-term workloads.

c. Secure Clusters: Implement IAM policies to control access to EMR resources. Enable encryption for data at rest and in transit. Regularly review security configurations and apply updates.

d. Automate Workflows: Use AWS Step Functions or Apache Airflow to automate and orchestrate data processing workflows. This improves efficiency and ensures consistency in data pipelines.

Conclusion

AWS EMR empowers organizations to harness the power of big data without the complexity of managing on-premises infrastructure. By offering scalable, flexible, and cost-effective data processing capabilities, EMR enables businesses to gain valuable insights, enhance operational efficiency, and drive innovation. As big data continues to grow in volume and importance, AWS EMR will remain a critical tool for organizations seeking to stay competitive in a data-driven world.

[techniktagebuch]

April 2023

Sechs Jahre Nichtstun, eine schöne Lösung für so viele Probleme

Vor fast genau sechs Jahren habe ich beschlossen, auch mal dieses Machine Learning auszuprobieren:

Gleich kann es losgehen, ich muss nur erst “Getting Started before your first lesson” lesen. Von dort schickt man mich weiter zum AWS deep learning setup video. Das Video ist 13 Minuten lang.

(Es folgen Probleme und Verwicklungen beim Setup, die Details kann man hier nachlesen.)

In Minute 12:45 sagt der Erzähler im Video: “Ok! It looks like everything is set up correctly and you’re ready to start using it.” Aber statt 12 Minuten und 45 Sekunden sind zwei Wochen vergangen, mein anfänglicher Enthusiasmus ist aufgebraucht und mein Interesse an Deep Learning erlahmt. Ich bin nicht einmal bis “Lesson 1” gekommen.

Im April 2023 sagt Aleks, dass er gerade einen sehr guten Onlinekurs über Machine Learning macht. Ich frage nach der Adresse, und sie kommt mir bekannt vor. Es ist derselbe Kurs!

“Das Setup war kein Problem?”, frage ich. Nein, sagt Aleks, Sache von ein paar Minuten.

Ich sehe mir "Practical Deep Learning for Coders 2022” an. Man braucht für den Kurs bestimmte Hardware. Generell benötigt Machine Learning Grafikprozessoren wegen der höheren Rechenleistung, und aus der Einleitung zum Kurs weiß ich jetzt, dass die aktuell verfügbaren Tools Nvidia-Grafikprozessoren voraussetzen*. Den Zugang zu dieser Hardware soll man mieten. Das war vor sechs Jahren auch schon so, nur dass das Mieten der Rechenleistung bei Amazon Web Services eine komplizierte und teure Sache war.

* Ich hatte an dieser Stelle schon “Grafikkarten” geschrieben, dann kam es mir aber wieder so vor, als müsste ich meinen Sprachgebrauch renovieren. In meiner Vorstellung handelt es sich um eine Steckkarte, ungefähr 10 x 20 cm groß, die in ein PC-Gehäuse eingebaut wird. So war das, als ich meine Computer noch in Einzelteilen kaufte, aber das ist zwanzig Jahre her. Deshalb habe ich mich für das unverbindliche Wort “Grafikprozessoren” entschieden. Aber wenn ich nach nvidia gpu machine learning suche, sehe ich sperrige Dinge, die nicht weit von meiner Erinnerung an Grafikkarten entfernt sind. Die große Rechenleistung braucht auch große Kühlleistung, deshalb sind zwei Lüfter auf der ... naja, Karte. Die Ergebnisse der Bildersuche sind etwas uneindeutig, aber es kommt mir so vor, als enthielte das Rechenzentrum, dessen Leistung ich gleich nutzen werde, wahrscheinlich große Gehäuse, in denen große Grafikkarten drin sind, vom Format her immer noch ungefähr wie vor zwanzig Jahren. Nur viel schneller.

2018 brauchte man AWS schon nicht mehr für den fast.ai-Onlinekurs. Stattdessen konnte man sich die Arbeitsumgebung bei Paperspace einrichten, einem anderen Cloud-Anbieter. Die Anleitung von 2018 klingt so, als hätte meine Geduld wahrscheinlich auch dafür nicht gereicht.

In der Version von 2019 hat der Kurs auf Google Colab gesetzt. Das heißt, dass man Jupyter Notebooks auf Google-Servern laufen lassen kann und keine eigene Python-Installation braucht, nur einen Browser. Colab gab es 2017 noch nicht, es wurde erst ein paar Monate nach meinem Scheitern, im Herbst 2017, für die Öffentlichkeit freigegeben. Allerdings klingt die Anleitung von 2019 immer noch kompliziert.

2020 wirkt es schon schaffbarer.

Auch die aktuelle Version des Kurses basiert auf Colab. Man muss sich dafür einen Account bei Kaggle einrichten. Soweit ich es bisher verstehe, dient dieser Kaggle-Zugang dazu, die Sache kostenlos zu machen. Colab würde ansonsten Geld kosten, weniger als ich 2017 bezahlt habe, aber eben Geld. Oder vielleicht liegen auch die Jupyter Notebooks mit den Kurs-Übungen bei Kaggle, keine Ahnung, man braucht es eben. (Update: In Kapitel 2 des Kurses merke ich, dass es noch mal anders ist, man hätte sich zwischen Colab und Kaggle entscheiden können. Zusammengefasst: Ich verstehe es nicht.)

Ich lege mir einen Kaggle-Account an und betrachte das erste Python-Notebook des Kurses. Es beginnt mit einem Test, der nur überprüft, ob man überhaupt Rechenleistung bei Kaggle in Anspruch nehmen darf. Das geht nämlich erst, wenn man eine Telefonnummer eingetragen und einen Verifikationscode eingetragen hat, der an diese Telefonnummer verschickt wird. Aber das Problem ist Teil des Kursablaufs und deshalb genau an der Stelle erklärt, an der es auftritt. Es kostet mich fünf Minuten, die vor allem im Warten auf die Zustellung der SMS mit dem Code bestehen.

Danach geht es immer noch nicht. Beim Versuch, die ersten Zeilen Code laufen zu lassen, bekomme ich eine Fehlermeldung, die mir sagt, dass ich das Internet einschalten soll:

“STOP: No internet. Click ‘>|’ in top right and set ‘Internet’ switch to on.”

Ich betrachte lange alles, was mit “top right” gemeint sein könnte, aber da ist kein solcher Schalter. Schließlich google ich die Fehlermeldung. Andere haben das Problem auch schon gehabt und gelöst. Der Schalter sieht weder so aus wie in der Fehlermeldung angedeutet, noch befindet er sich oben rechts. Man muss ein paar Menüs ein- und ein anderes ausklappen, dann wird er unten rechts sichtbar.

Ich bin also im Internet und muss erst das Internet einschalten, damit ich Dinge im Internet machen kann.

Aleks meint, wenn ich ihm gestern dabei zugehört hätte, wie er eine Viertelstunde lang laut fluchte, hätte ich schon gewusst, wie es geht. Hatte ich aber nicht.

Nach dem Einschalten des Internets kann ich das erste Jupyter-Notebook des Kurses betrachten und selbst ausprobieren, ob es wohl schwer ist, Frösche von Katzen zu unterscheiden. Für die Lösung aller Startprobleme von 2017 habe ich zwei Wochen gebraucht. 2023 noch eine Viertelstunde, und ich bin zuversichtlich, dass man um 2025 direkt in den Kurs einsteigen können wird.

(Kathrin Passig)

[hobbyhorizonte]

Freizeitspaß Online: Die besten digitalen Aktivitäten für zuhause

Das Wichtigste zu freizeitspass online

- Vielfalt von digitalen Aktivitäten, von Lern-Apps bis zu virtuellen Erlebnissen. - Ideal für alle Altersgruppen und Interessen. - Erfordert oft nur minimale Ausrüstung, wie ein Smartphone oder einen Computer. - Perfekt zur Entspannung, zum Lernen oder zum sozialen Austausch. - Wichtig, eine Balance zu finden, um Offline-Aktivitäten nicht zu vernachlässigen.

Warum digitale Freizeitaktivitäten?

Digitale Freizeitaktivitäten bieten eine breite Palette von Möglichkeiten, die bequem von Zuhause aus zugänglich sind. Sie sind flexibel, vielseitig und oft kostengünstig.

Beliebte digitale Aktivitäten

1. Online-Gaming Spiele wie Fortnite, Minecraft und Among Us bieten stundenlangen Spielspaß und soziale Interaktion. Hier stürzt man sich in virtuelle Welten und erlebt aufregende Abenteuer. 2. Virtuelle Museumsbesuche Viele Museen weltweit bieten virtuelle Rundgänge an, darunter das Louvre in Paris. So kann man Kultur bequem von der Couch aus genießen. 3. Online-Kurse Plattformen wie Coursera oder Udemy bieten Kurse zu nahezu jedem Thema an, vom Kochen bis zur Programmierung. 4. Streaming von Filmen und Serien Dienste wie Netflix und Amazon Prime bieten Tausende von Filmtiteln und Serienepisoden für jeden Geschmack. 5. Virtuelle Begegnungen mit Freunden Mithilfe von Videokonferenz-Tools kann man sich mit Freunden treffen und gemeinsam in Echtzeit Spaß haben, ohne physisch anwesend zu sein.

Vorteile und Herausforderungen

Der größte Vorteil digitaler Freizeitaktiviäten ist ihre Zugänglichkeit. Aber es ist wichtig, die Bildschirmzeit zu moderieren, um eine gesunde Balance zu gewährleisten. Vorteile: - Einfacher Zugang zu einer Vielzahl von Interessen - Oft kostengünstig oder kostenlos - Flexibilität bei der Planung Herausforderungen: - Bildschirmzeit-Management - Mangel an körperlicher Aktivität - Soziale Isolation durch fehlenden physischen Kontakt

Schlussgedanken

Digitale Aktivitäten bieten eine tolle Möglichkeit, die Freizeit zu gestalten. Sie sind unterhaltsam und lehrreich, solange ein ausgewogenes Verhältnis bewahrt wird. Erfahren Sie mehr über Online-Lernen und seine Vorteile. Virtuelle Touren im MoMA in New York

Kritische Fragen und Antworten

Denkt ihr, wir werden irgendwann alle in der virtuellen Realität leben? Die Vorstellung, in einer virtuellen Welt zu leben, ist faszinierend und beängstigend zugleich. Während Virtual Reality schon jetzt erstaunliche Erfahrungen ermöglicht, bleiben viele Fragen hinsichtlich der sozialen und psychologischen Auswirkungen offen. Langfristig könnte VR unsere Lebensweise drastisch verändern, doch der Übergang wird wahrscheinlich langsamer erfolgen, als viele erwarten. Es gibt natürliche Grenzen und ethische Überlegungen, die den vollständigen Übergang in ein rein virtuelles Leben verhindern. Bedürfnisse wie zwischenmenschliche Interaktionen und physische Erfahrungen sind schwer digital zu ersetzen. Daher wird die virtuelle Realität eher als Erweiterung unserer realen Welt denn als Ersatz dienen. Wie langweilt man sich in einer Welt voller digitaler Möglichkeiten eigentlich noch? Mit dem enormen Zugang zu digitalen Inhalten könnte man vermuten, dass Langeweile ein Relikt der Vergangenheit ist. Doch paradoxerweise kann die schiere Menge an Optionen auch überwältigend sein und zur "Entscheidungsmüdigkeit" führen. Nicht jede digitale Aktivität bietet die Tiefe oder persönliche Erfüllung, die ein Hobby im analogen Kontext haben kann. Manchmal besteht auch ein Bedürfnis nach einfachen, offline Erfahrungen, die das Gefühl der Zufriedenheit mit sich bringen. Die Herausforderung ist, die Balance zu finden und die richtigen Aktivitäten für sich selbst zu entdecken. Sollten wir besorgt sein über die wachsende Abhängigkeit von Bildschirmen? Die Nutzung von Bildschirmen ist zweifellos ein wachsendes Thema in unserer Gesellschaft. Während sie uns verbinden und informieren, gibt es berechtigte Bedenken über gesundheitliche Auswirkungen wie Augenbelastung und die verringerte Fähigkeit, sich offline zu verbinden. Forscher raten zur Achtsamkeit und empfehlen, regelmäßige bildschirmfreie Zeiten in den Alltag zu integrieren. Die Herausforderung besteht darin, den positiven Nutzen der Technologie zu maximieren, während mögliche negative Auswirkungen minimiert werden. In der Zukunft könnte die Integration von Technologien in unseren Lebensstil analysiert und modifiziert werden müssen, um gesundheitliche sowie soziale Schäden zu vermeiden. Lässt uns digitale Unterhaltung verblöden oder stimuliert sie unsere Intelligenz? Dieses Thema hat Debatten entfacht, in denen sowohl positive als auch negative Aspekte digitaler Unterhaltung diskutiert werden. Digitales Spielen kann kognitive Fähigkeiten wie Reaktionszeit und Problemlösungsfähigkeiten fördern. Gleichzeitig kann passiver Medienkonsum zu einer Verflachung der intellektuellen Fähigkeiten führen, wenn er nicht strategisch mit aktiveren und fordernden Aktivitäten kombiniert wird. Der Schlüssel liegt in der Auswahl des Inhalts: Lernplattformen, Dokumentationen und kreative Software bieten intellektuelle Stimulanz. Letztendlich könnte der Einfluss davon abhängen, wie bewusst und ausgewogen digitale Inhalte konsumiert werden. Was, wenn digitale Freizeitaktivitäten zu einer neuen Form von Einsamkeit führen? Einsamkeit trotz Verbindung scheint in der digitalen Ära ein wachsendes Phänomen zu sein. Während wir immer virtuell verbunden sind, fehlt oft die Tiefe in diesen Interaktionen. Die Gesichtslosigkeit des digitalen Austauschs kann ein Gefühl des isolierten Erlebens schaffen. Um dieser Art von Einsamkeit entgegenzuwirken, ist es notwendig, gelegentlich den digitalen Raum zu verlassen und physische zwischenmenschliche Beziehungen zu pflegen. Eine Mischung aus online und offline Aktivitäten könnte der Schlüssel sein, um echte soziale Bindungen aufrechtzuerhalten und moderne Einsamkeit zu vermeiden. Read the full article

[graymattersoftware]

Transform the way you manage and leverage your data. With GrayMatter's cutting-edge Data Warehousing Solutions, gain a centralized, scalable, and secure foundation to drive smarter business decisions. Our solutions integrate with leading tools like Informatica, Microsoft Power BI, and Amazon web services, ensuring seamless data consolidation and real-time analytics.