#big data tools

[recenttrendingtopics]

Did You Know that according to Gartner, 70% of organizations will shift their focus from big data to wide data by 2025? Stay updated with the 6 most popular big data tools in 2023. Enroll with USDSI™ today and future-proof your skills!

[emily-joe]

BI tools deal with the collection, transformation, and presentation of data. The top business intelligence tools for data visualization are Tableau, Microsoft Power BI, QlikView.

[sprinkledata12]

Top 30 Data Analytics Tools for 2023

Top 30 Data Analytics Tools

Data is the new oil. It has become a treasured commodity today for data analytics and has taken on a serious status. With the daily growing data volume, it is now at a scale that no human can deal with the amount manually. Businesses worldwide have found growth in their organizations by incorporating data analytics into their existing technology platforms.

The concept of data analytics has evolved over time and will continue to rise. Data analytics has become an important part of managing a business today, where every business owner wants their business to grow and increase its revenue in order to maintain a competitive edge in this ever-changing marketplace, they need to be able to use data effectively.

What is Data Analytics?

Data analytics is the science of studying raw data with the intent of drawing conclusions from it. It is used in multiple industries to allow companies and organizations to make more promising data-driven business decisions.

Data analytics covers an entire spectrum of data usage, from collection to analysis to reporting. Understanding the process of data analytics is the ultimate power and it will be the future of almost every industry.

There are multiple types of data analytics including descriptive, diagnostic, predictive, and prescriptive analytics.

Let’s learn about the different types of data analytics in detail.

‍Types of Data Analytics:

Descriptive Data Analytics:

Descriptive data analytics is the process of examining data to summarize what is actually happening. It provides a basic understanding of how the business operates and helps to identify which factors are affecting the business and which aren't. It supports the exploration and discovery of insights from your existing data and based on that provides a basic understanding of your business.

Diagnostic Data Analytics:

Diagnostic Data Analytics is used to diagnose any business problems. It generally answers the question: why did it happen? Data can be examined manually, or used by an automated system to generate a warning. Diagnostic data analytics is an advanced analytical approach that is used to find the cause of the problem faced by a business.

Predictive Data Analytics:

Predictive data analytics is a form of analytics that uses both new and historical data to forecast activities, behavior, and trends. It is used to analyze current data to make predictions about future events. One important use case for predictive analysis is to help retailers understand customer buying patterns and optimize inventory levels to maximize revenues.

Prescriptive Data Analytics:

Prescriptive data analytics is the last level of analytics that is performed on the outcome of other types of analytics. It is the process of defining an action based on descriptive and predictive data analytics results. In this stage, different scenarios are analyzed to determine how each scenario is likely to play out given past data. This can help businesses know what action to take for a good outcome.

These four types of data analysis techniques can help you find hidden patterns in your data and make sense of it. All these types of data analytics are important in other ways and can be used in different business scenarios.

Importance of Data Analytics:

Data analytics is extremely important for any enterprise and has become a crucial part of every organization's strategy in the past decade. The reason for this is simple: Big data has opened up a world of opportunities for businesses. Data analysts have become essential in helping companies process their huge sets of data for making meaningful decisions.

The benefits offered by analyzing data are numerous some of them are mentioned below:

It helps businesses to determine hidden trends and patterns.

Improves efficiency and productivity of the business by helping them to take data-driven decisions.

Identifies weaknesses and strengths in the current approach.

Enhances decision-making, which helps businesses to boost their revenue and helps solve business problems.

It helps to perform customer behavior analysis accurately to increase customer satisfaction

Data analytics lets you know what is working and what can improve. According to experts, the lack of data analysis and usage can result in failed business strategies and also cause loss of customers. So in order to take your business to the next level, one must always adopt data analytics techniques and should be familiar with the steps involved in it.

Data Analysis Process: Steps involved in Data Analytics

Steps in data analytics are a set of actions that can be performed to create useful and functional data. In this section, we will detail the stages involved in data analytics.

Understanding Business Requirements

One of the most important factors behind successful data analysis is a proper understanding of the business requirements. An analyst needs to have a clear idea about what kind of problem the business is facing and what can be done to overcome the problem. The other important task is to understand what type of data needs to be collected to solve the given problem.

Collecting Data

When it comes to data analytics, it is very important that the right kind of data is collected. After understanding the business problem the analyst should be aware of the type of data to be collected to solve the problem. Data can be collected in many ways, including survey forms, interviews, market research, web crawlers, log files, event log files, and even through social media monitoring apps.

Data wrangling

In data wrangling, data is cleaned and managed so that it can be utilized in order to perform data analysis. This process can involve converting data from one format to another, filtering out invalid or incorrect data, and transforming data so that it can be more easily analyzed. Data wrangling is an important step in data analysis because it can help ensure that the data used in the analysis is of high quality and is in a suitable format.

There are many steps involved in data wrangling, including

1. Gathering data from a variety of sources.

2. Cleaning and standardizing the data.

3. Exploring the data to identify patterns and relationships.

4. Transforming the data into a format that can be used for different tasks.

5. Saving the wrangled data in a format that can be easily accessed and used in the future.

The steps involved in data wrangling can vary depending on the type and format of data you are working with, but the final goal is always the same, to transform raw data into a format that is more useful for performing accurate analysis.

Exploratory Data Analysis (EDA):

Exploratory Data Analysis (EDA) is a statistical approach used to achieve insights into data by summarizing its major features. This procedure is used to comprehend the data’s distribution, outliers, trends, and other factors. EDA can be used to select the best-fitting statistical models and input variables for a dataset.

A typical EDA process might begin with a series of questions, such as

What are the primary components of the dataset?

What are the most significant variables?

Are there any outliers or unusual observations or behaviors?

After asking these basic questions, the analyst should then investigate the data visually, using charts such as histograms, scatter plots, and box plots. These visual methods can help to identify features such as trends, and unusual observations. This process of EDA can help to reveal important insights into the data, and can be used to guide further analysis.

EDA can provide insights that may not be obvious from merely looking at the data itself. Overall, it is an essential tool for data analysis and should be used whenever possible.

Communicating Results:

Communicating results is the final and the most vital aspect of the data analysis life cycle because it allows others to understand the conclusions of the analysis. Results also need to be communicated in a clear and concise way so they can be easily understood by people with less technical acumen as well. Additionally, conveying results allows for feedback and discussion to improve the quality of the findings during the analysis procedure.

The data analytics life cycle generally goes through these five-step procedures that help to find precise conclusions. But apart from the benefits, some challenges are faced during the data analytics process.

Overall Challenges in Data Analysis:

There can be many types of challenges encountered during the data analysis journey but the two most common challenges are mentioned below:

Data issues

Data analysis-related issues.

1. Data Issues:

Data-related problems are one such type of issue encountered during the data analysis journey. Some data-related issues are mentioned below:

Incorrect or inaccurate data

Incomplete data

Data that is not timely ingested

Unorganized data

Irrelevant data

Data integration issues

Handling large datasets

The data team needs to guarantee to provide the correct data and a good and reliable data integration platform should be preferred to ensure correct and timely ingestion of data. A proper ETL tool that provides safe and secure data storage should be selected.

2. Data Analysis Related Issues:

The data analysis process can be challenging if the data is not well-organized, some challenges are mentioned below:

Absence of skills to interpret data.

Data cleaning and preparation can be very time-consuming.

Choosing the right statistical method can be a challenge.

The results of the analysis can be misinterpreted.

Communicating the results in a simpler way can be tough

To overcome these challenges businesses should use low-code data analytics platforms that will help to save manpower and thus reduce costs. With careful planning and execution, one can easily perform analysis without any hassles. By using the right tools and techniques, businesses can overcome these challenges and make better data-driven decisions.

Need for Data Analysis Tools:

In a world where data is continuously being generated, it is becoming hard to make sense of it all without the help of data analysis tools.

There are many reasons why we need data analysis tools. They help us to process, understand, and make use of data effectively. Data analysis tools help us to see patterns and trends in data without actually coding. Nowadays, businesses don't need a highly skilled person to perform the data analysis process in fact they can perform the analysis on their own because of the tools present in the market.

The data analysis tools in the market can also help to enhance communication and collaboration within your organization through alerts and email functionalities. In some cases, they can also help to automate decision-making processes.

Criteria For Choosing the Right Data Analysis Tool:

There is a wide variety of data analysis tools available in the market but the best-fitted tool for you will depend on the specific data set and the desired business outcome. When choosing a data analysis tool, it is essential to assess the specific features and capabilities of the tool, and the user’s needs should also be considered. For example, if you are looking to perform complex statistical analysis, then a statistical software package would be the best choice. On the other hand, if you are looking to create interactive dashboards, then a no-code data analytics platform would be a more suitable fit.

Below listed are some criteria that one should consider before choosing the right data analytics platform according to the requirements.

1. No-code Data Analytics Platform:

No-code data analytics platforms equip users with the capability to quickly analyze data with ease without having to write even a single line of code. This can save users a lot of time and effort by making data analysis more streamlined.

Some benefits provided by such data platforms are mentioned below:

No technical skills required: Analysis of data on these types of platforms can be performed by users of all skill types and different experience levels. Data analysis is made more accessible to individuals which allows them to benefit from it.

Supports Different Data types: Wide variety of data can be analyzed be it structured or unstructured, which makes these platforms more versatile.

Easy Integration: Easy integration with different sources is one of the best features provided by no-code data platforms.

Flexible pricing plans: No-code platforms provide scalability and are proven to be very cost-effective. This feature makes them useful for businesses of all sizes and stature.

If you are looking for a good and reliable no-code data analytics platform that has all these features then Sprinkle Data is the best option.

     2. Availability of Different Types of Charts:

Charts can help to picture data, and spot trends and patterns effortlessly. They help to make intricate data more coherent and can help individuals to make better decisions. Charts used with proper statistical techniques can be useful in making predictions about future behavior as well. They also can be used to interpret and find relationships between different variables and are useful in finding outliers in data. Different types of charts can be used to perform accurate analysis, some important chart types include:

Bar/column charts are one of the most typically used chart types and are especially helpful in comparing data points.

Line charts are used for depicting changes over time.

Pie charts are helpful in determining proportions across various categories

Scatter plots are useful for visualizing relationships between two numerical data points and are primarily used to identify trends and outliers in data.

Histograms are used to give information about the data distribution.

An area chart is based on a line chart and is primarily used to depict quantitative data by covering the area below the line.

Combo Chart is a combination of a line and a bar chart that depicts trends over time.

Funnel charts help to portray linear processes with sequential or interconnected phases in the analysis.

A map is a geographical chart type used to visualize data point density across different locations.

A stacked bar chart is a form of bar chart depicting comparisons of different data categories.

Charts are an integral part of any data analytics tool and can add meaning to the analysis. They help to communicate the conclusions of the analysis in a concise manner. So always choose a data analysis tool that has these charts with additional attributes like labels, a benchmark value, and different colors to easily differentiate.

All the chart types mentioned above are available in the Sprinkle Data analytics tool accessible with just a single click.

    3. Dashboard With a Good Visual Interface

A dashboard is a visual user interface that provides easy access to key metrics and consists of a sequence of charts, tables, and other visual elements that can be customized and systematized to provide insights into specific datasets with advantages like delivering visibility into an organization's performance in real time.

The key features that a dashboard should contain are mentioned below:

Interactivity: Dashboards with good interactivity permit users to filter and drill down into data for more detailed analysis.

Easily Editable layout: Customized dashboard show only the data that is relevant to the analysis.

Easy to share: Dashboards that can be easily shared with others to explore and analyze the data.

Less Runtime: A data analytics platform whose Dashboards take less time to run should be picked.

Monitoring: In case of a dashboard failure proper email alerts should be provided to the user with the reason for the error.

User-Friendly Interface: A dashboard with a user-friendly interface like drag and drop functionality is easy to use.

Live Dashboard: If you need to track data in real-time a live dashboard is the best option for your business.

If you are confused about which data analytics platform should be preferred to get all these features then you should prefer Sprinkle Data.

The best dashboard for your needs is the one that must follow all these criteria and will depend on the type of data you need to track, and the level of detail you need to acquire.

    4. Cost Efficient:

A cost-effective data analytics platform helps to save money on software and hardware. These tools can help organizations save money in a number of ways. By enabling organizations to understand their data better, these tools can help to recognize zones where costs can be decreased. Moreover, a platform with flexible and scalable pricing plans should be adopted to pay a reasonable price according to the requirements.

Sprinkle Data has a flexible pricing plan that is fully customizable according to the needs of users enabling them to save costs while performing high-level analytics.

Ultimately, the best way to choose the right data analysis tool is to consult with experts in the field and try different tools to see which one works best for your specific needs.

Read More Here to know Top 30 Data Analytics Tools for 2023 :https://www.sprinkledata.com/blogs/data-analytics-tools

[cbirt]

TBtools, a toolkit used for data analysis and bioinformatics tasks, was released in 2020 and quickly found a large audience – thousands of researchers worldwide adopted it, and it has been cited more than 5000 times in the three years it has been operational. A new upgrade, TBtools-II, has now been developed, with more than 100 new features to make bioinformatics analysis easier than ever.

In recent years, bioinformatics analysis has become a mainstay of academic research worldwide – with new advances in biotechnology, it has become possible to extract a large amount of biological data from various sources. While this data is often instrumental in uncovering new insights, the quantity makes it impossible to analyze. Further, the variety of bioinformatics tools needed to clean and process the data as required can be numerous and daunting, not least because of different tasks requiring entirely different workflows. Valuable time is lost due to researchers being forced to learn to adapt to different platforms and interfaces before any analysis can be performed. Especially for researchers who work primarily in wet labs and may not have the coding proficiency required to operate these tools, such a lack of accessibility presents a significant challenge.

In 2020, the release of TBtools provided researchers with a viable solution to this problem: featuring more than 130 functions and receiving frequent updates and bug fixes, the toolkit has become ubiquitous in research labs. Despite its utility and functionality, the various needs of different users presented a significant challenge to the developers. Bioinformatics data analysis encompasses a wide variety of applications and tasks, and researchers working in certain fields require highly specific and personalized tools for data analysis. While the addition of these tools helped increase the usefulness of TBtools and helped it serve a wider section of researchers, it also bloated the toolkit significantly and made it harder to use and navigate.

Continue Reading

[newfangled-polusai]

Top 5 Benefits of Low-Code/No-Code BI Solutions

Low-code/no-code Business Intelligence (BI) solutions offer a paradigm shift in analytics, providing organizations with five key benefits. Firstly, rapid development and deployment empower businesses to swiftly adapt to changing needs. Secondly, these solutions enhance collaboration by enabling non-technical users to contribute to BI processes. Thirdly, cost-effectiveness arises from reduced reliance on IT resources and streamlined development cycles. Fourthly, accessibility improves as these platforms democratize data insights, making BI available to a broader audience. Lastly, agility is heightened, allowing organizations to respond promptly to market dynamics. Low-code/no-code BI solutions thus deliver efficiency, collaboration, cost savings, accessibility, and agility in the analytics landscape.

[hazelplaysgames]

i really liked. it's technically still Rolling, off of the wall and onto the basket case. just fun.

oh oh oh, i have an EXCUSE to talk about this now. can you believe the Grizzco brella was actually buffed in 3 from 2? the max damage went from 60 to 80. at 5 shots per second, that's now 400 damage per second! the now buffed Tenta brella, 360 per shot every 51 frames, can peak at 423. G. Brella needs ANOTHER buff in my opinion, the pellets do 15 per, why isn't it 90 instead of 80?

honestly, it's really not a huge lead in the first place, i am exaggerating a bit, but for a Grizzco weapon, i have higher standards.

[ebubekiratabey]

Hello, you know there are a lot of different AI Tools to make our life easier. I want to share them with. The first one is free AI Animation Tools for 3D Masterpieces. You will find 6 different AI Tools.

Take a look at my blog !.

Ebubekir ATABEY

Data Scientist

[jcmarchi]

Generative AI use soars among brits, but is it sustainable?

New Post has been published on https://thedigitalinsider.com/generative-ai-use-soars-among-brits-but-is-it-sustainable/

Generative AI use soars among brits, but is it sustainable?

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A survey by CloudNine PR shows that 83% of UK adults are aware of generative AI tools, and 45% of those familiar with them want companies to be transparent about the environmental costs associated with the technologies.

With data centres burning vast amounts of energy, the growing demand for GenAI has sparked a debate about its sustainability.

The cost of intelligence: Generative AI’s carbon footprint

Behind every AI-generated email, idea, or recommendation are data centres running thousands of energy-hungry servers. Data centres are responsible for both training the large language models that power generative AI and processing individual user queries. Unlike a simple Google search, which uses relatively little energy, a single generative AI request can consume up to ten times as much electricity.

The numbers are staggering. If all nine billion daily Google searches worldwide were replaced with generative AI tasks, the additional electricity demand would match the annual energy consumption of 1.5 million EU residents. According to consultants Morgan Stanley, the energy demands of generative AI are expected to grow by 70% annually until 2027. By that point, the energy required to support generative AI systems could rival the electricity needs of an entire country—Spain, for example, based on its 2022 usage.

UK consumers want greener AI practices

The survey also highlights growing awareness among UK consumers about the environmental implications of generative AI. Nearly one in five respondents said they don’t trust generative AI providers to manage their environmental impact responsibly. Among regular users of these tools, 10% expressed a willingness to pay a premium for products or services that prioritise energy efficiency and sustainability.

Interestingly, over a third (35%) of respondents think generative AI tools should “actively remind” users of their environmental impact. While this appears like a small step, it has the potential to encourage more mindful usage and place pressure on companies to adopt greener technologies.

Efforts to tackle the environmental challenge

Fortunately, some companies and policymakers are beginning to address these concerns. In the United States, the Artificial Intelligence Environmental Impacts Act was introduced earlier this year. The legislation aims to standardise how AI companies measure and report carbon emissions. It also provides a voluntary framework for developers to evaluate and disclose their systems’ environmental impact, pushing the industry towards greater transparency.

Major players in the tech industry are also stepping up. Companies like Salesforce have voiced support for legislation requiring standardised methods to measure and report AI’s carbon footprint. Experts point to several practical ways to reduce generative AI’s environmental impact, including adopting energy-efficient hardware, using sustainable cooling methods in data centres, and transitioning to renewable energy sources.

Despite these efforts, the urgency to address generative AI’s environmental impact remains critical. As Uday Radia, owner of CloudNine PR, puts it: “Generative AI has huge potential to make our lives better, but there is a race against time to make it more sustainable before it gets out of control.”

(Photo by Unsplash)

See also: The AI revolution: Reshaping data centres and the digital landscape 

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: ai, generative ai, research, uk

[tudip123]

The Importance of Data Engineering in Today’s Data-Driven World

In today’s fast-paced, technology-driven world, data has emerged as a critical asset for businesses across all sectors. It serves as the foundation for strategic decisions, drives innovation, and shapes competitive advantage. However, extracting meaningful insights from data requires more than just access to information; it necessitates well-designed systems and processes for efficient data management and analysis. This is where data engineering steps in. A vital aspect of data science and analytics, data engineering is responsible for building, optimizing, and maintaining the systems that collect, store, and process data, ensuring it is accessible and actionable for organizations.

Let's explore how Data Engineering is important in today's world:

1. What is Data Engineering

2. Why is Data Engineering Important

3. Key Components of Data Engineering

4. Trends in Data Engineering

5. The Future of Data Engineering

Let’s examine each one in detail below.

What is Data Engineering?

Data engineering involves creating systems that help collect, store, and process data effectively.It involves creating data pipelines that transport data from its source to storage and analysis systems, implementing ETL processes (Extract, Transform, Load), and maintaining data management systems to ensure data is accessible and secure. It enables organizations to make better use of their data resources for data-driven decision-making.

Why is Data Engineering Important?

Supports Data-Driven Decision-Making: In a competitive world, decisions need to be based on facts and insights. Data engineering ensures that clean, reliable, and up-to-date data is available to decision-makers. From forecasting market trends to optimizing operations, data engineering helps businesses stay ahead.

Manages Big Data Effectively: Big data engineering focuses on handling large and complex datasets, making it possible to process and analyze them efficiently. Industries like finance, healthcare, and e-commerce rely heavily on big data solutions to deliver better results.

Enables Modern Technologies: Technologies like machine learning, artificial intelligence, and predictive analytics depend on well-prepared data. Without a solid modern data infrastructure, these advanced technologies cannot function effectively. Data engineering ensures these systems have the data they need to perform accurately.

Key Components of Data Engineering:

Data Pipelines: Data pipelines move data automatically between systems.They take data from one source, change it into a useful format, and then store it or prepare it for analysis.

ETL Processes: ETL (Extract, Transform, Load) processes are crucial in preparing raw data for analysis. They clean, organize, and format data, ensuring it is ready for use.

Data Management Systems: 

These systems keep data organized and make it easy to access. Examples of these systems are databases, data warehouses, and data lakes.

Data Engineering Tools: From tools like Apache Kafka for real-time data streaming to cloud platforms like AWS and Azure, data engineering tools are essential for managing large-scale data workflows.

Trends in Data Engineering:

The field of data engineering is changing quickly, and many trends are shaping its future:

Cloud-Based Infrastructure: More businesses are moving to the cloud for scalable and flexible data storage.

Real-Time Data Processing: The need for instant insights is driving the adoption of real-time data systems.

Automation in ETL: Automating repetitive ETL tasks is becoming a standard practice to improve efficiency.

Focus on Data Security: With increasing concerns about data privacy, data engineering emphasizes building secure systems.

Sustainability: Energy-efficient systems are gaining popularity as companies look for greener solutions.

The Future of Data Engineering:

The future of data engineering looks bright. As data grows in size and complexity, more skilled data engineers will be needed.Innovations in artificial intelligence and machine learning will further integrate with data engineering, making it a critical part of technological progress. Additionally, advancements in data engineering tools and methods will continue to simplify and enhance workflows.

Conclusion:

Data engineering is the backbone of contemporary data management and analytics. It provides the essential infrastructure and frameworks that allow organizations to efficiently process and manage large volumes of data. By focusing on data quality, scalability, and system performance, data engineers ensure that businesses can unlock the full potential of their data, empowering them to make informed decisions and drive innovation in an increasingly data-driven world.

Tudip Technologies has been a pioneering force in the tech industry for over a decade, specializing in AI-driven solutions. Our innovative solutions leverage GenAI capabilities to enhance real-time decision-making, identify opportunities, and minimize costs through seamless processes and maintenance.

If you're interested in learning more about the Data Engineering related courses offered by Tudip Learning  please visit:  https://tudiplearning.com/course/essentials-of-data-engineering/.

[vastedge330]

Unlock actionable insights and drive data-driven decisions with VastEdge’s advanced data analytics services.

[mehmetyildizmelbourne-blog]

Beware of Cognitive Biases in Generative AI Tools as a Reader, Researcher, or Reporter

Understanding How Human and Algorithmic Biases Shape Artificial Intelligence Outputs and What Users Can Do to Manage Them I have spent over 40 years studying human and machine cognition long before AI reached its current state of remarkable capabilities. Today, AI is leading us into uncharted territories. As a researcher focused on the ethical aspects of technology, I believe it is vital to…

[bicxoseo]

Data isn't just numbers, it's your roadmap to success.

Navigate it wisely with Bicxo!

For free demo visit: www.bicxo.co

[data-analytics-consulting]

With the field of data analytics constantly evolving, organizations are embracing open-source tools due to their flexibility, lower pricing, and solid features. Open-source applications, including data analysis and data visualization tools, are useful for organizations that want to use their data efficiently. This article focuses on the best open-source data analytics tools, their comparison, and tools that will suit organizational requirements best.        

https://www.sganalytics.com/blog/open-source-data-analytics-tools/

[mitsde123]

Data Science Job Market : Current Trends and Future Opportunities

The data science job market is thriving, driven by the explosive growth of data and the increasing reliance on data-driven decision-making across industries. As organizations continue to recognize the value of data, the demand for data scientists has surged, creating a wealth of opportunities for professionals in this field.

[nitor-infotech]

In today's data-driven world, seamless data integration and processing are crucial for informed decision-making. Matillion, a robust ETL (Extract, Transform, Load) tool, has gained popularity for its ability to streamline these processes.

In this blog, you will learn how it efficiently moves and transforms data from various sources to cloud data warehouses, making data management easier. Apart from this, you'll also get a brief understanding of its constraints and best practices for transforming large datasets.

By understanding these aspects, you can maximize your business capabilities and drive forward excellently.