#data engineering architecture

[innovaticsblog]

Optimize your business with Innovatics' cutting-edge data engineering services, turning raw data into actionable insights. Drive innovation and unlock your business's full potential with our data engineering specialists.

[atozofsoftwareengineering]

Demystifying Data Engineering: The Backbone of Modern Analytics

Hey friends! Check out this in-depth blog on #DataEngineering that explores its role in building robust data pipelines, ensuring data quality, and optimizing performance. Discover emerging trends like #cloudcomputing, #realtimeprocessing, and #DataOps

In the era of big data, data engineering has emerged as a critical discipline that underpins the success of data-driven organizations. Data engineering encompasses the design, construction, and maintenance of the infrastructure and systems required to extract, transform, and load (ETL) data, making it accessible and usable for analytics and decision-making. This blog aims to provide an in-depth…

[aakarshanstar]

Innovative Data Engineering for Strategic Decision-Making

Unlocking the Power of Data: The Role of Data Engineering in Modern Businesses

In today's data-driven world, businesses are increasingly relying on vast amounts of data to make informed decisions, streamline operations, and drive growth. However, the true potential of data can only be harnessed when it is efficiently collected, processed, and analyzed. This is where Data Engineering comes into play—a critical component that forms the backbone of any successful data strategy. At aakarshansedge.com, our Data Engineering services are designed to transform raw data into actionable insights, empowering businesses to thrive in the digital age.

Key Benefits of Our Data Engineering Services Scalability: For scalability in our Data Engineering Services, we ensure that our solutions can seamlessly adapt to increasing data volumes and complexity. Our infrastructure is designed to handle growth efficiently, providing robust performance and flexibility as your data needs evolve. Data Quality: Poor data quality can lead to inaccurate insights and misguided decisions. We implement rigorous data cleaning and validation processes to ensure that your data is accurate, consistent, and trustworthy. Efficiency: In the corporate world, time is of the essence. Our efficient data pipelines and optimized processing techniques minimize latency, allowing you to access and analyze data in real-time. Security and Compliance: With data privacy regulations becoming increasingly stringent, we prioritize security and compliance in all our data engineering projects. We implement robust encryption, access controls, and monitoring systems to protect your data. Cost-Effectiveness: We help you optimize your data storage and processing costs by leveraging cloud platforms and modern data architectures, ensuring you get the most value out of your investment.

Technologies Used in Data Engineering

Big Data Frameworks - The Big Data frameworks at Aakarshan Edge include cutting-edge tools designed for scalable data processing and analytics, such as Apache Hadoop, Apache Spark, and Apache Flink.

Data Warehousing Solutions - Transform your data into actionable insights with our cutting-edge Data Warehousing Solutions, designed for scalability and efficiency at Aakarshan Edge."

Data Integration Tools - Discover top-tier data integration tools at Aakarshan Edge, designed to streamline and enhance your data management processes.

Database Technologies - The website Aakarshan Edge, utilizes advanced database technologies to ensure robust, scalable, and secure data management.

ETL Tools - The website Aakarshan Edge, utilizes cutting-edge ETL (Extract, Transform, Load) tools to streamline data processing and integration, ensuring efficient data management and insights.

Cloud Platforms - Aakarshan Edge offers innovative solutions across leading cloud platforms to enhance scalability and performance for your business.

Data Governance & Quality Tools - Implement robust Data Governance and Quality Tools to ensure the accuracy, consistency, and security of your data assets.

Data Visualization Tools - Transform complex data into clear, actionable insights with our advanced data visualization tools. From interactive dashboards to customizable charts, we empower your business to make data-driven decisions with ease.

Programming Languages - The website Aakarshan Edge, uses a combination of programming languages including HTML, CSS, JavaScript, and potentially server-side languages like PHP or Python.

Machine Learning Libraries - The website Aakarshan Edge, features cutting-edge machine learning libraries to enhance data analytics and predictive modeling.

Why Choose Aakarshan Edge for Data Engineering?

At Aakarshan Edge, we understand that every business is unique, and so are its data challenges. Our approach to data engineering Solutions is highly customized, focusing on understanding your specific needs and delivering solutions that align with your business objectives. Our team of experienced data engineers is well-versed in the latest technologies and best practices, ensuring that your data infrastructure is future-proof and capable of driving innovation.

Conclusion

our Data Engineering Services at Aakarshan Edge are designed to empower your business with robust data solutions that drive efficiency and innovation. By leveraging advanced technologies and tailored strategies, we ensure that your data infrastructure is not only scalable but also aligned with your strategic goals. Partner with us to transform your data into a powerful asset that enhances decision-making and fuels growth.

Contact us (+91-8860691214) (E-Mail: sales@akarshans.com)

[kk]

AI as Your Creative Co-pilot: A Down-to-Earth Guide for the Architecture, Engineering & Construction Industries

Through experiments with generative design, simulations and human-AI partnerships, I've gained insights and surprising discoveries that have expanded my view of what's possible. In this post, I share lessons learned in the hope it inspires other architect

Hey there, friends and fellow explorers of the digital frontier. If you recall, I recently had the honor of giving the keynote presentation at the Canadian Society for Marketing Professional Services (CSMPS) Annual General Meeting about how Artificial Intelligence (AI) is revolutionizing the Architecture, Engineering, and Construction (AEC) industries. I’ve talked about how AI has revolutionized…

[prototechsolutionsblog]

Unveiling the Power of 3D Visualization: Revolutionizing Engineering Applications

In the world of engineering, complex concepts and intricate designs often require effective means of communication to convey ideas, identify potential issues, and foster innovation. 3D visualization has emerged as a powerful tool that not only aids in comprehending intricate engineering concepts but also fuels creativity and enhances collaboration among multidisciplinary teams. This blog dives deep into the realm of 3D visualization for engineering applications, exploring its benefits, applications, and the technologies driving its evolution.

 The Power of 3D Visualization

1. Enhanced Understanding: Traditional 2D drawings and diagrams can sometimes fall short in capturing the full complexity of engineering designs. 3D visualization empowers engineers, architects, and designers to create realistic and immersive representations of their ideas. This level of detail allows stakeholders to grasp concepts more easily and make informed decisions.

2. Identification of Design Flaws: One of the primary advantages of 3D visualization is its ability to identify potential design flaws before physical prototyping begins. Engineers can simulate real-world conditions, test stress points, and analyze the behavior of components in various scenarios. This process saves both time and resources that would have been wasted on rectifying issues post-construction.

3. Efficient Communication: When working on multidisciplinary projects, effective communication is essential. 3D visualization simplifies the sharing of ideas by presenting a clear visual representation of the design. This reduces the chances of misinterpretation and encourages productive discussions among team members from diverse backgrounds.

4. Innovation and Creativity: 3D visualization fosters creativity by enabling engineers to experiment with different design variations quickly. This flexibility encourages out-of-the-box thinking and exploration of unconventional ideas, leading to innovative solutions that might not have been considered otherwise.

5. Client Engagement: For projects involving clients or stakeholders who might not have technical expertise, 3D visualization serves as a bridge between complex engineering concepts and layman understanding. Clients can visualize the final product, making it easier to align their expectations with the project's goals.

 Applications of 3D Visualization in Engineering

1. Architectural Visualization: In architectural engineering, 3D visualization brings blueprints to life, allowing architects to present realistic walkthroughs of structures before construction. This helps clients visualize the final appearance and make informed decisions about design elements.

2. Product Design and Prototyping: Engineers can use 3D visualization to create virtual prototypes of products, enabling them to analyze the functionality, ergonomics, and aesthetics. This process accelerates the design iteration phase and reduces the number of physical prototypes required.

3. Mechanical Engineering: For mechanical systems, 3D visualization aids in simulating motion, stress analysis, and assembly processes. Engineers can identify interferences, optimize part arrangements, and predict system behavior under different conditions.

4. Civil Engineering and Infrastructure Projects: From bridges to roadways, 3D visualization facilitates the planning and execution of large-scale infrastructure projects. Engineers can simulate traffic flow, assess environmental impacts, and optimize structural design for safety and efficiency.

5. Aerospace and Automotive Engineering: In these industries, intricate designs and high-performance requirements demand rigorous testing. 3D visualization allows engineers to simulate aerodynamics, structural integrity, and other critical factors before manufacturing.

 Technologies Driving 3D Visualization

1. Computer-Aided Design (CAD): CAD software forms the foundation of 3D visualization. It enables engineers to create detailed digital models of components and systems. Modern CAD tools offer parametric design, enabling quick modifications and iterative design processes.

2. Virtual Reality (VR) and Augmented Reality (AR): VR and AR technologies enhance the immersive experience of 3D visualization. VR headsets enable users to step into a digital environment, while AR overlays digital content onto the real world, making it ideal for on-site inspections and maintenance tasks.

3. Simulation Software: Simulation tools allow engineers to analyze how a design will behave under various conditions. Finite element analysis (FEA) and computational fluid dynamics (CFD) simulations help predict stress, heat transfer, and fluid flow, enabling design optimization.

4. Rendering Engines: Rendering engines create photorealistic images from 3D models, enhancing visualization quality. These engines simulate lighting, materials, and textures, providing a lifelike representation of the design.

 Future Trends and Challenges

As technology evolves, so will the field of 3D visualization for engineering applications. Here are some anticipated trends and challenges:

1. Real-time Collaboration: With the rise of cloud-based tools, engineers worldwide can collaborate on 3D models in real time. This facilitates global teamwork and accelerates project timelines.

2. Artificial Intelligence (AI) Integration: AI could enhance 3D visualization by automating design tasks, predicting failure points, and generating design alternatives based on predefined criteria.

3. Data Integration: Integrating real-time data from sensors and IoT devices into 3D models will enable engineers to monitor performance, identify anomalies, and implement preventive maintenance strategies.

4. Ethical Considerations: As 3D visualization tools become more sophisticated, ethical concerns might arise regarding the potential misuse of manipulated visualizations to deceive stakeholders or obscure design flaws.

In conclusion, 3D visualization is transforming the engineering landscape by enhancing understanding, fostering collaboration, and driving innovation. From architectural marvels to cutting-edge technological advancements, 3D visualization empowers engineers to push the boundaries of what is possible. As technology continues to advance, the future of engineering will undoubtedly be shaped by the dynamic capabilities of 3D visualization.

[technicalfika]

What is the difference between Data Scientist and Data Engineers ?

In today’s data-driven world, organizations harness the power of data to gain valuable insights, make informed decisions, and drive innovation. Two key players in this data-centric landscape are data scientists and data engineers. Although their roles are closely related, each possesses unique skills and responsibilities that contribute to the successful extraction and utilization of data. In…

[nnctales]

Leveraging GPT AI for Seismic Design in Structural Engineering: A Technical Perspective

In the contemporary landscape of structural engineering, seismic design is a critical consideration. The constant threat of seismic events requires innovative methods that factor in such disturbances to ensure robust and resilient built environments. The latest advancement reshaping seismic design in this field is GPT (Generative Pretrained Transformer) AI, developed by OpenAI. GPT AI, an…

[nitor-infotech]

Modern Data Architecture Engineering Services 

Today, data is considered as the new fuel and a vital asset for businesses all over the globe. As customer preferences have been changing at a rapid pace, data systems can help organizations tap into the accurate needs of these customers and deliver excellent products.

That is, it has become important for organizations to have a well-designed data architecture that can support their current and future data needs and modern data architecture engineering services can help businesses design, build, and maintain a data architecture that is scalable, flexible, and secure. 

Let’s get acquainted with modern data architecture engineering. 

What is modern data architecture engineering? 

Modern data architecture engineering is the process of designing, building, and maintaining a data architecture that meets the needs of an organization. A data architecture is the overall design of an organization's data systems and infrastructure, including the way data is collected, stored, and accessed. So, by leveraging the latest technologies and best practices, modern data architecture engineering helps to turn raw data into actionable insights.  

Now, let’s get familiar with some of its key features.  

Key features of modern data architecture engineering services 

Here are some of the key features: 

Data Integration - This involves the process of combining data from various sources, such as databases, applications, and sensors, to create a single, comprehensive view of an organization's data. 

Data Modelling - The creation of a logical and physical data model that defines data relationships, entities, and attributes, and supports the data requirements of the organization. 

Data Governance - This involves establishing policies and procedures for managing and protecting an organization's data, including issues such as access control, data quality, and data retention. 

Data Security - It refers to the ability to secure data from unauthorized access, breaches, and other data-related threats. 

Data Lineage - The ability to track and trace data from its origin to the point of consumption, ensures data lineage, quality, and governance.  

There are several other key features too that makes such data systems reliable for businesses. Now allow me to share the benefits of modern data architecture engineering.  

Benefits of modern data architecture engineering services 

Here are a few benefits: 

Organized Data Management 

Increased Scalability 

Enhanced Security 

Improved Data Integration 

Increased Efficiency 

Improvised Data Quality 

With such a diverse range of advantages on the plate, I’m pretty sure that if you are running a business, you might just want to avail such data architecture engineering services soon. 

Therefore, to increase your scalability, efficiency, and other business operations, data management through modern data architecture engineering plays a critical role in digital transformation. 

Read more about Nitor Infotech’s modern data architecture engineering services with Nitor Infotech.  

[just-ornstein]

[JK]  My first job was as an Assistant Producer for a video game company called Interplay in Irvine, CA. I had recently graduated from Boston University's School of Fine Arts with an MFA in Directing (I started out as a theatre nerd), but also had some limited coding experience and a passion for computers. It didn't look like I'd be able to make a living directing plays, so I decided to combine entertainment and technology (before it was cool!) and pitched myself to Brian Fargo, Interplay's CEO. He gave me my first break. I packed up and moved out west, and I've been producing games ever since.

[JK] I loved my time at EA. I was there for almost a full decade, and learned a tremendous amount about game-making, and met the most talented and driven people, who I remain in touch with today. EA gave me many opportunities, and never stopped betting on me. I worked on The Sims for nearly 5 years, and then afterwards, I worked on console action games as part of the Visceral studio. I was the Creative Director for the 2007 game "The Simpsons", and was the Executive Producer and Creative Director for the 2009 game "Dante's Inferno".

[JK] I haven't played in a long while, but I do recall that after the game shipped, my wife and I played the retail version for some time -- we created ourselves, and experimented with having a baby ahead of the actual birth of our son (in 2007). Even though I'd been part of the development team, and understood deeply how the simulation worked, I was still continually surprised at how "real" our Sims felt, and how accurate their responses were to having a baby in the house. It really felt like "us"!

Now for some of the development and lore related questions:

[JK] So I ended up in the incredibly fortunate position of creating the shipping neighborhoods for The Sims 2, and recruiting a few teammates to help me as we went along. 

Around the same time, we started using the Buy/Build tools to make houses we could save, and also bring them into each new build of the game (correcting for any bugs and incompatibilities). With the import tool, we could load Sims into these houses. In time, this "vanguard QA" process turned into a creative endeavor to define the "saved state" of the neighborhoods we would actually end up shipping with the game.

On playtesting & the leftover sims data on various lots:

Basically, we were in the late stages of development, and the Save Game functionality wasn't quite working. In order to test the game properly, you really needed to have a lot of assets, and a lot of Sims with histories (as if you'd been playing them for weeks) to test out everything the game had to offer. So I started defining a set of characters in a spreadsheet, with all their tuning variables, and worked with engineering to create an importer, so that with each new build, I could essentially "load" a kind of massive saved game, and quickly start playing and testing. 

It was fairly organic, and as the game's functionality improved, so did our starter houses and families. 

The thought process behind the creation of the iconic three neighborhoods:

I would not say it was particularly planned out ahead of time. We knew we needed a few saved houses to ship with the game; Sims 1, after all, had the Goth house, and Bob Newbie's house. But there wasn't necessarily a clear direction for what the neighborhood would be for Sims 2. We needed the game to be far enough along, so that the neighborhood could be a proper showcase for all the features in the game. With each new feature that turned alpha, I had a new tool in my toolbox, and I could expand the houses and families I was working on. Once we had the multi-neighborhood functionality, I decided we would not just have 1 starter neighborhood, but 3. With the Aging feature, Memories, a few wacky objects, plus a huge catalog of architectural and decorative content, I felt we had enough material for 3 truly distinct neighborhoods. And we added a couple of people to what became the "Neighborhood Team" around that time.

Later, when we created Strangetown, and eventually Veronaville, I believe we went back and changed Pleasantville to Pleasantview... because I liked the alliteration of "Verona-Ville", and there was no sense in having two "villes". (To this day, by the way, I still don't know whether to capitalize the "V" -- this was hotly debated at the time!)

Pleasantview:

Anyway, to answer your question, we of course started with Pleasantview. As I recall, we were not quite committed to multiple neighborhoods at first, and I think it was called Pleasantville initially, which was kind of a nod to Simsville... but without calling it Simsville, which was a little too on the nose. (There had also been an ill-fated game in development at Maxis at the time, called SimsVille, which was cancelled.) It's been suggested that Pleasantville referred to the movie, but I don't think I ever saw that movie, and we just felt that Pleasantville kind of captured the feeling of the game, and the relaxing, simple, idyllic world of the Sims.

Pleasantview started as a place to capture the aging feature, which was all new to The Sims 2. We knew we had toddlers, teens, and elders to play with, so we started making families that reflected the various stages of family life: the single mom with 3 young kids, the parents with two teens, the old rich guy with two young gold-diggers, etc. We also had a much greater variety of ethnicity to play with than Sims 1, and we had all new variables like sexual orientation and memories. All these things made for rich fodder for a great diversity of families. Then, once we had family trees, and tombstones that carried the actual data for the dead Sims, the doors really blew open. We started asking ourselves, "What if Bella and Mortimer Goth could be characters in Sims 2, but aged 25 years? And what if Cassandra is grown up? And what if Bella is actually missing, and that could be a fun mystery hanging over the whole game?" And then finally the "Big Life Moments" went into the game -- like weddings and birthdays -- and we could sort of tee these up in the Save Game, so that they would happen within the first few minutes of playing the families. This served both as a tutorial for the features, but also a great story-telling device.

Anyway, it all just flowed from there, as we started creating connections between families, relationships, histories, family trees, and stories that we could weave into the game, using only the simulation features that were available to us. It was a really fun and creative time, and we wrote all of the lore of Sims 2 within a couple of months, and then just brought it to life in the game.

Strangetown:

Strangetown was kind of a no-brainer. We needed an alternate neighborhood for all the paranormal stuff the Sims was known for: alien abduction, male pregnancy, science experiments, ghosts, etc. We had the desert terrain, which created a nice contrast to the lush Pleasantville, and gave it an obvious Area 51 vibe.

The fact that Veronaville is the oldest file probably reflects the fact that it was finished first, not that it was started first. That's my guess anyway. It was the simplest neighborhood, in many ways, and didn't have as much complexity in terms of features like staged big life moments, getting the abduction timing right, the alien DNA thing (which I think was somewhat buggy up until the end), etc.  So it's possible that we simply had Veronaville "in the can", while we put the last polish on Pleasantville (which was the first and most important neighborhood, in terms of making a good impression) and Strangeville (which was tricky technically).

Veronaville:

But my personal favorite was Veronaville. We had this cool Tudor style collection in the Build mode catalog, and I wanted to ship some houses that showed off those assets. We also had the teen thing going on in the aging game, plus a lot of romance features, as well as enemies. I have always been a Shakespeare buff since graduate school, so putting all that together, I got the idea that our third neighborhood should be a modern-day telling of the Romeo and Juliet story. It was Montys and Capps (instead of Montagues and Capulets), and it just kind of wrote itself. We had fun creating the past family trees, where everyone had died young because they kept killing each other off in the ongoing vendetta.

[JK] You know, I have never seen The Lone Gunmen, and I don't remember making any kind of direct references with the Strangetown Sims, other than the general Area 51 theme, as you point out. Charles London helped out a lot with naming Sims, and I'm pretty sure we owe "Vidcund" and "Lazlo" to him ... though many team members pitched in creatively. He may have had something in mind, but for me, I largely went off of very generic and stereotypical ideas when crafting these neighborhoods. I kind of wanted them to be almost "groaners" ... they were meant to be tropes in every sense of the word. And then we snuck in some easter eggs. But largely, we were trying to create a completely original lore.

[JK] Well, I think we kind of pushed it with The Sims 2, to be honest, and I remember getting a little blow-back about Bunny Broke, for example. Bunny Broke was the original name for Brandi Broke. Not everyone found that funny, as I recall, and I can understand that. It must have been changed before we shipped.

We also almost shipped the first outwardly gay Sims in those neighborhoods, which was bold for EA back in 2004. My recollection was that we had set up the Dreamers to be gay (Dirk and Darren), but I'm looking back now and see that's not the case. So I'm either remembering incorrectly (probably) or something changed during development.

In general we just did things that we found funny and clever, and we just pulled from all the tropes of American life.

[JK] The alien abduction started in Sims 1, with a telescope object that was introduced in the "Livin' Large" expansion pack. That's when some of the wackier ideas got introduced into the Sims lore. That pack shipped just before I joined Maxis in 2001; when I got there, the team had shipped "House Party" and was underway on "Hot Date". So I couldn't tell you how the original idea came about, but The Sims had this 50's Americana vibe from the beginning, and UFOs kind of played right into that. So the alien abduction telescope was a no-brainer to bring back in Sims 2. The male pregnancy was a new twist on the Sims 1 telescope thing. It must have been that the new version (Sims 2) gave us the tech and flexibility to have male Sims become pregnant, so while this was turned "off" for the core game, we decided to take advantage of this and make a storyline out of it. I think this really grew out of the fact that we had aliens, and alien DNA, and so it was not complicated to pre-bake a baby that would come out as an alien when born. The idea of a bunch of guys living together, and then one gets abducted, impregnated, and then gives birth to an alien baby ... I mean, I think we just all thought that was hilarious, in a sit-com kind of way. Not sure there was much more to it than that. Everything usually came from the designers discovering ways to tweak and play with the tech, to get to funny outcomes.

[JK] Possibly we were just testing the functionality of the Wants/Fears and Memories systems throughout development, and some stuff got left over.

[JK] I can't remember, but that sounds like something we would have done! I'm pretty sure we laid the groundwork for more stories that we ended up delivering :) But The Sims 2 was a great foundation for a lot of continued lore that followed.

--

I once again want to thank Jonathan Knight for granting me this opportunity and taking the time from his busy schedule to answer my questions.

[nolijconsulting]

RPA in Healthcare- How RPA is Changing the Healthcare Industry

Robotic Process Automation (RPA) is among the most promising and efficient solutions across the world. It helps in reducing expenses from financial institutions to manufacturing sectors and also decreases overall production time. It is software that can automate tasks in the healthcare industry and provide better services to patients.

RPA is changing the way healthcare is provided. It has been seen as a solution for hospitals and clinics to improve their efficiency and reduce costs. It has been used in hospitals, clinics, and research centers to automate tasks such as scheduling appointments, ordering lab tests, and even patient monitoring. With the help of RPA, it has now become possible to streamline the healthcare system.

How Is RPA Changing the Healthcare Industry

1. Enhancing patient satisfaction

A satisfied patient is a key part of any thriving healthcare system. Without being able to effectively schedule appointments and process check-ins, patients will become frustrated, which can lead to other issues. With an automated healthcare process, patients can schedule their appointment anytime they want through their devices, without waiting around in a waiting room. RPA or robotic process automation improves efficiency by eliminating human error, allowing staff to focus on patient-centered activities, and increasing the quality of the job.

2. Improving Healthcare Cycles

Healthcare institutions collect a lot of data from patients, which is stored in databases. They're important to digest and analyze, as extracting & optimizing such data can be difficult. By implementing RPA emerging technology services in healthcare, bots can collect and keep track of all patients’ data. Additionally, automation in medical records has made it incredibly easy for doctors by eliminating the need to handle data manually and letting automated bots do this. That time allows doctors more time to attend to things like human assistance with patients.

3. Cutting human-labor costs

Once healthcare service providers automated repetitive tasks, they could reduce the cost of labor and make processes more efficient. Apart from reduced cost, repetitive tasks are being vastly simplified in the healthcare industry due to the implementation of RPA. Automation can help improve efficiency and productivity for healthcare professionals and make them focus on higher-value portions of their tasks.

4. Enhancing Precision and Speedy Medical Diagnosis

Time is vital in healthcare. If a problem is not diagnosed quickly, it can lead to a less-than-optimal outcome. With RPA emerging technology services in healthcare, it's easy for doctors to make an appointment for a patient and make quick diagnoses. They also can get a patient's medical history, current diagnosis, and personal preferences to help them diagnose what the patient might be facing.

Technology has made it much easier to save time, minimize errors, & make sure accuracy. This is why hospitals are turning to RPA.

Conclusion:

Technology is improving healthcare which saves time, effort, and money. Robotic Process Automation (RPA) provides emerging technology services that can help healthcare providers focus on the right people, tackle problems more quickly, and increase patient satisfaction. In addition, it helps to enhance data collection and analysis, which supports evidence-based data in a healthcare organization.

[jcmarchi]

Anais Dotis-Georgiou, Developer Advocate at InfluxData – Interview Series

New Post has been published on https://thedigitalinsider.com/anais-dotis-georgiou-developer-advocate-at-influxdata-interview-series/

Anais Dotis-Georgiou, Developer Advocate at InfluxData – Interview Series

Anais Dotis-Georgiou is a Developer Advocate for InfluxData with a passion for making data beautiful with the use of Data Analytics, AI, and Machine Learning. She takes the data that she collects, does a mix of research, exploration, and engineering to translate the data into something of function, value, and beauty. When she is not behind a screen, you can find her outside drawing, stretching, boarding, or chasing after a soccer ball.

InfluxData is the company building InfluxDB, the open source time series database used by more than a million developers around the world. Their mission is to help developers build intelligent, real-time systems with their time series data.

Can you share a bit about your journey from being a Research Assistant to becoming a Lead Developer Advocate at InfluxData? How has your background in data analytics and machine learning shaped your current role?

I earned my undergraduate degree in chemical engineering with a focus on biomedical engineering and eventually worked in labs performing vaccine development and prenatal autism detection. From there, I began programming liquid-handling robots and helping data scientists understand the parameters for anomaly detection, which made me more interested in programming.

I then became a sales development representative at Oracle and realized that I really needed to focus on coding. I took a coding boot camp at the University of Texas in data analytics and was able to break into tech, specifically developer relations.

I came from a technical background, so that helped shape my current role. Even though I didn’t have development experience, I could relate to and empathize with people who had an engineering background and mind but were also trying to learn software. So, when I created content or technical tutorials, I was able to help new users overcome technical challenges while placing the conversation in a context that was relevant and interesting to them.

Your work seems to blend creativity with technical expertise. How do you incorporate your passion for making data ‘beautiful’ into your daily work at InfluxData?

Lately, I’ve been more focused on data engineering than data analytics. While I don’t focus on data analytics as much as I used to, I still really enjoy math—I think math is beautiful, and will jump at an opportunity to explain the math behind an algorithm.

InfluxDB has been a cornerstone in the time series data space. How do you see the open source community influencing the development and evolution of InfluxDB?

InfluxData is very committed to the open data architecture and Apache ecosystem. Last year we announced InfluxDB 3.0, the new core for InfluxDB written in Rust and built with Apache Flight, DataFusion, Arrow, and Parquet–what we call the FDAP stack. As the engineers at InfluxData continue to contribute to those upstream projects, the community continues to grow and the Apache Arrow set of projects gets easier to use with more features and functionality, and wider interoperability.

What are some of the most exciting open-source projects or contributions you’ve seen recently in the context of time series data and AI?

It’s been cool to see the addition of LLMs being repurposed or applied to time series for zero-shot forecasting. Autolab has a collection of open time series language models, and TimeGPT is another great example.

Additionally, various open source stream processing libraries, including Bytewax and Mage.ai, that allow users to leverage and incorporate models from Hugging Face are pretty exciting.

How does InfluxData ensure its open source initiatives stay relevant and beneficial to the developer community, particularly with the rapid advancements in AI and machine learning?

InfluxData initiatives remain relevant and beneficial by focusing on contributing to open source projects that AI-specific companies also leverage. For example, every time InfluxDB contributes to Apache Arrow, Parquet, or DataFusion, it benefits every other AI tech and company that leverages it, including Apache Spark, DataBricks, Rapids.ai, Snowflake, BigQuery, HuggingFace, and more.

Time series language models are becoming increasingly vital in predictive analytics. Can you elaborate on how these models are transforming time series forecasting and anomaly detection?

Time series LMs outperform linear and statistical models while also providing zero-shot forecasting. This means you don’t need to train the model on your data before using it. There’s also no need to tune a statistical model, which requires deep expertise in time series statistics.

However, unlike natural language processing, the time series field lacks publicly accessible large-scale datasets. Most existing pre-trained models for time series are trained on small sample sizes, which contain only a few thousand—or maybe even hundreds—of samples. Although these benchmark datasets have been instrumental in the time series community’s progress, their limited sample sizes and lack of generality pose challenges for pre-training deep learning models.

That said, this is what I believe makes open source time series LMs hard to come by. Google’s TimesFM and IBM’s Tiny Time Mixers have been trained on massive datasets with hundreds of billions of data points. With TimesFM, for example, the pre-training process is done using Google Cloud TPU v3–256, which consists of 256 TPU cores with a total of 2 terabytes of memory. The pre-training process takes roughly ten days and results in a model with 1.2 billion parameters. The pre-trained model is then fine-tuned on specific downstream tasks and datasets using a lower learning rate and fewer epochs.

Hopefully, this transformation implies that more people can make accurate predictions without deep domain knowledge. However, it takes a lot of work to weigh the pros and cons of leveraging computationally expensive models like time series LMs from both a financial and environmental cost perspective.

This Hugging Face Blog post details another great example of time series forecasting.

What are the key advantages of using time series LMs over traditional methods, especially in terms of handling complex patterns and zero-shot performance?

The critical advantage is not having to train and retrain a model on your time series data. This hopefully eliminates the online machine learning problem of monitoring your model’s drift and triggering retraining, ideally eliminating the complexity of your forecasting pipeline.

You also don’t need to struggle to estimate the cross-series correlations or relationships for multivariate statistical models. Additional variance added by estimates often harms the resulting forecasts and can cause the model to learn spurious correlations.

Could you provide some practical examples of how models like Google’s TimesFM, IBM’s TinyTimeMixer, and AutoLab’s MOMENT have been implemented in real-world scenarios?

This is difficult to answer; since these models are in their relative infancy, little is known about how companies use them in real-world scenarios.

In your experience, what challenges do organizations typically face when integrating time series LMs into their existing data infrastructure, and how can they overcome them?

Time series LMs are so new that I don’t know the specific challenges organizations face. However, I imagine they’ll confront the same challenges faced when incorporating any GenAI model into your data pipeline. These challenges include:

Data compatibility and integration issues: Time series LMs often require specific data formats, consistent timestamping, and regular intervals, but existing data infrastructure might include unstructured or inconsistent time series data spread across different systems, such as legacy databases, cloud storage, or real-time streams. To address this, teams should implement robust ETL (extract, transform, load) pipelines to preprocess, clean, and align time series data.

Model scalability and performance: Time series LMs, especially deep learning models like transformers, can be resource-intensive, requiring significant compute and memory resources to process large volumes of time series data in real-time or near-real-time. This would require teams to deploy models on scalable platforms like Kubernetes or cloud-managed ML services, leverage GPU acceleration when needed, and utilize distributed processing frameworks like Dask or Ray to parallelize model inference.

Interpretability and trustworthiness: Time series models, particularly complex LMs, can be seen as “black boxes,” making it hard to interpret predictions. This can be particularly problematic in regulated industries like finance or healthcare.

Data privacy and security: Handling time series data often involves sensitive information, such as IoT sensor data or financial transaction data, so ensuring data security and compliance is critical when integrating LMs. Organizations must ensure data pipelines and models comply with best security practices, including encryption and access control, and deploy models within secure, isolated environments.

Looking forward, how do you envision the role of time series LMs evolving in the field of predictive analytics and AI? Are there any emerging trends or technologies that particularly excite you?

A possible next step in the evolution of time series LMs could be introducing tools that enable users to deploy, access, and use them more easily. Many of the time series LMs  I’ve used require very specific environments and lack a breadth of tutorials and documentation. Ultimately, these projects are in their early stages, but it will be exciting to see how they evolve in the coming months and years.

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

[devsgames]

Okay at this point I've seen so many students feeling doomed for taking a course where a teacher uses Unity or like they're wasting time learning the engine, and while understandably the situation at Unity sucks and is stressful for everyone: y'all need to stop thinking learning Unity a waste of your time.

Learning a game engine does not dictate your abilities as a dev, and the skills you learn in almost any engine are almost all transferrable skills when moving to other engines. Almost every new job you get in the games industry will use new tools, engines and systems no matter where you work, whether that be proprietary, enterprise or open-source. Skills you learn in any engine are going to be relevant even if the software is not - especially if you're learning development for the first time. Hell, even the act of learning a game engine is a transferrable skill.

It's sort of like saying it's a waste to learn Blender because people use 3DS Max, or why bother learning how to use a Mac when many people use Windows; it's all the same principals applied differently. The knowledge is still fundamental and applicable across tools.

Many engines use C-adjacent languages. Many engines use similar IDE interfaces. Many engines use Object Oriented Programming. Many engines have component-based architecture. Many objects handle data and modular prefabs and inheritence in a similar way. You are going to be learning skills that are applicable everywhere, and hiring managers worth their weight will be well aware of this.

The first digital game I made was made in Flash in 2009. I'm still using some principles I learned then. I used Unity for almost a decade and am now learning Godot and finding many similarities between the two. If my skills and knowledge are somehow still relevant then trust me: you are going to learn a lot of useful skills using Unity.

[jovial-thunder]

Pre-alpha Lancer Tactics changelog

(cross-posting the full gif changelog here because folks seemed to like it last time I did)

We're aiming for getting the first public alpha for backers by the end of this month! Carpenter and I scoped out mechanics that can wait until after the alpha (e.g. grappling, hiding) in favor of tying up the hundred loose threads that are needed for something that approaches a playable game. So this is mostly a big ol changelog of an update from doing that.

But I also gave a talent talk at a local Portland Indie Game Squad event about engine architecture! It'll sound familiar if you've been reading these updates; I laid out the basic idea for this talk almost a year ago, back in the June 2023 update.

We've also signed contracts & had a kickoff meeting with our writers to start on the campaigns. While I've enjoyed like a year of engine-work, it'll be so so nice to start getting to tell stories. Data structures don't mean anything beyond how they affect humans & other life.

New Content

Implemented flying as a status; unit counts as +3 spaces above the current ground level and ignores terrain and elevation extra movement costs. Added hover + takeoff/land animations.

Gave deployables the ability to have 3D meshes instead of 2D sprites; we'll probably use this mostly when the deployable in question is climbable.

Related, I fixed a bug where after terrain destruction, all units recheck the ground height under them so they'll move down if the ground is shot out from under them. When the Jerichos do that, they say "oh heck, the ground is taller! I better move up to stand on it!" — not realizing that the taller ground they're seeing came from themselves.

Fixed by locking some units' rendering to the ground level; this means no stacking climbable things, which is a call I'm comfortable making. We ain't making minecraft here (I whisper to myself, gazing at the bottom of my tea mug). 

Block sizes are currently 1x1x0.5 — half as tall as they are wide. Since that was a size I pulled out of nowhere for convenience, we did some art tests for different block heights and camera angles. TLDR that size works great and we're leaving it.

Added Cone AOE pattern, courtesy of an algorithm NMcCoy sent me that guarantees the correct number of tiles are picked at the correct distance from the origin.

pick your aim angle

for each distance step N of your cone, make a list ("ring") of all the cells at that distance from your origin

sort those cells by angular distance from your aim angle, and include the N closest cells in that ring in the cone's area

Here's a gif they made of it in Bitsy:

Units face where you're planning on moving/targeting them.

Got Walking Armory's Shock option working. Added subtle (too subtle, now that I look at it) electricity effect.

Other things we've added but I don't have gifs for or failed to upload. You'll have to trust me. :)

disengage action

overcharge action

Improved Armament core bonus

basic mine explosion fx

explosion fx on character dying

Increase map elevation cap to 10. It's nice but definitely is risky with increasing the voxel space, gonna have to keep an eye on performance.

Added Structured + Stress event and the associated popups. Also added meltdown status (and hidden countdown), but there's not animation for this yet so your guy just abruptly disappears and leaves huge crater.

UI Improvements

Rearranged the portrait maker. Auto-expand the color picker so you don't have to keep clicking into a submenu.

Added topdown camera mode by pressing R for handling getting mechs out of tight spaces.

The action tooltips have been bothering me for a while; they extend up and cover prime play-area real estate in the center of the screen. So I redesigned them to be shorter and have a max height by putting long descriptions in a scrollable box. This sounds simple, but the redesign, pulling in all the correct data for the tags, and wiring up the tooltips took like seven hours. Game dev is hard, yo.

Put the unit inspect popups in lockable tooltips + added a bunch of tooltips to them.

Implemented the rest of Carpenter's cool hex-y action and end turn readout. I'm a big fan of whenever we can make the game look more like a game and less like a website (though he balances out my impulse for that for the sake of legibility).

Added a JANKY talent/frame picker. I swear we have designs for a better one, but sometimes you gotta just get it working. Also seen briefly here are basic level up/down and HASE buttons.

Other no-picture things:

Negated the map-scaling effect that happens when the window resizes to prevent bad pixel scaling of mechs at different resolutions; making the window bigger now just lets you see more play area instead of making things bigger.

WIP Objectives Bullets panel to give the current sitrep info

Wired up a buncha tooltips throughout the character sheet.

Under the Hood

Serialization: can save/load games! This is the payoff for sticking with that engine architecture I've been going on about. I had to add a serialization function to everything in the center layer which took a while, but it was fairly straightforward work with few curveballs.

Finished replacement of the kit/unit/reinforcement group/sitrep pickers with a new standardized system that can pull from stock data and user-saved data.

Updated to Godot 4.2.2; the game (and editor) has been crashing on exit for a LONG time and for the life of me I couldn't track down why, but this minor update in Godot completely fixed the bug. I still have no idea what was happening, but it's so cool to be working in an engine that's this active bugfixing-wise! 

Other Bugfixes

Pulled straight from the internal changelog, no edits for public parseability:

calculate cover for fliers correctly

no overwatch when outside of vertical threat

fixed skirmisher triggering for each attack in an AOE

fixed jumpjets boost-available detection

fixed mines not triggering when you step right on top of them // at a different elevation but still adjacent

weapon mods not a valid target for destruction

made camera pan less jumpy and adjust to the terrain height

better Buff name/desc localization

Fixed compcon planner letting you both boost and attack with one quick action.

Fix displayed movement points not updating

Prevent wrecks from going prone

fix berserkers not moving if they were exactly one tile away

hex mine uses deployer's save target instead of 0

restrict weapon mod selection if you don't have the SP to pay

fix deployable previews not going away

fix impaired not showing up in the unit inspector (its status code is 0 so there was a check that was like "looks like there's no status here")

fix skirmisher letting you move to a tile that should cost two movement if it's only one space away

fix hit percent calculation

fix rangefinder grid shader corner issues (this was like a full day to rewrite the shader to be better)

Teleporting costs the max(spaces traveled, elevation change) instead of always 1

So um, yeah, that's my talk, any questions? (I had a professor once tell us to never end a talk like this, so now of course it's the phrase that first comes to mind whenever I end a talk)

[theambitiouswoman]

The Best Degrees for High Paying Jobs

I want to preface this with saying that getting a degree that aligns with career options that pay above average salaries does not guarantee than you will actually get those jobs, or those salaries. Several factors like demand, location, your skills, and work experience play a big role. In some cases, advanced degrees can also increase your earning potential.

However, if you want to get a degree to align you with a high paying job, these are the jobs/degrees that typically pay the best salaries.

Medicine: Doctors, Surgeons, Psychiatrists.

Dental: Dentist, Oral surgeons.

Law: Corporate lawyers, IP attorneys, Litigators.

IT and computer science: Engineer, IT Manager, Architect, Data scientist.

Engineers: Computer engineer, Chemical, Aerospace, Electrical.

MBA: CEO, Consultant, Development Manager.

Finance: Finance Manager, Analyst.

Statistics: Research Analyst.

Aviation: Pilot.

Pharmaceutical: Research Scientist, Sales Rep.

Architecture.

Physics: Physicist, Scientist.

Nurse: Anethetist, Nurse Practitioner, Hospital Admin.

Marketing.

[technicalfika]

Who is Data Engineer and what they do? : 10 key points

In today’s data-driven world, the demand for professionals who can organize, process, and manage vast amounts of information has grown exponentially. Enter the unsung heroes of the tech world – Data Engineers. These skilled individuals are instrumental in designing and constructing the data pipelines that form the backbone of data-driven decision-making processes. In this article, we’ll explore…

[nnctales]

Transforming Brooklyn Bridge: A Revolution in Infrastructure Renovation

One of the most enduring symbols of human architectural brilliance is the Brooklyn Bridge in New York City. Since its completion in 1883, the Brooklyn Bridge has stood as a testament to engineering prowess and urban resilience. Today, however, we stand on the precipice of a new era for this venerable structure, as modern civil engineering techniques and technologies are transforming the Brooklyn…