We talked about:

Nemanja’s background

When Nemanja first work as a data person Typical problems that ML Ops folks solve in the financial sector What Nemanja currently does as an ML Engineer The obstacle of implementing new things in financial sector companies Going through the hurdles of DevOps Working with an on-premises cluster “ML Ops on a Shoestring” (You don’t need fancy stuff to start w/ ML Ops) Tactical solutions Platform work and code work Programming and soft skills needed to be an ML Engineer The challenges of transitioning from and electrical engineering and sales to ML Ops The ML Ops tech stack for beginners Working on projects to determine which skills you need

Links:

LinkedIn: https://www.linkedin.com/in/radojkovic/

Free Data Engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Data Engineering with Scala and Spark guides you through building robust data pipelines that process massive datasets efficiently. You will learn practical techniques leveraging Scala and Spark with a hands-on approach to mastering data engineering tasks including ingestion, transformation, and orchestration. What this Book will help me do Set up a data pipeline development environment using Scala Utilize Spark APIs like DataFrame and Dataset for effective data processing Implement CI/CD and testing strategies for pipeline maintainability Optimize pipeline performance through tuning techniques Apply data profiling and quality enforcement using tools like Deequ Author(s) Eric Tome, Rupam Bhattacharjee, and David Radford bring decades of combined experience in data engineering and distributed systems. Their work spans cutting-edge data processing solutions using Scala and Spark. They aim to help professionals excel in building reliable, scalable pipelines. Who is it for? This book is tailored for working data engineers familiar with data workflow processes who desire to enhance their expertise in Scala and Spark. If you aspire to build scalable, high-performance data solutions or transition raw data into strategic assets, this book is ideal.

Summary

Monitoring and auditing IT systems for security events requires the ability to quickly analyze massive volumes of unstructured log data. The majority of products that are available either require too much effort to structure the logs, or aren't fast enough for interactive use cases. Cliff Crosland co-founded Scanner to provide fast querying of high scale log data for security auditing. In this episode he shares the story of how it got started, how it works, and how you can get started with it.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Cliff Crosland about Scanner, a security data lake platform for analyzing security logs and identifying issues quickly and cost-effectively

Interview

Introduction How did you get involved in the area of data management? Can you describe what Scanner is and the story behind it?

What were the shortcomings of other tools that are available in the ecosystem?

What is Scanner explicitly not trying to solve for in the security space? (e.g. SIEM) A query engine is useless without data to analyze. What are the data acquisition paths/sources that you are designed to work with?- e.g. cloudtrail logs, app logs, etc.

What are some of the other sources of signal for security monitoring that would be valuable to incorporate or integrate with through Scanner?

Log data is notoriously messy, with no strictly defined format. How do you handle introspection and querying across loosely structured records that might span multiple sources and inconsistent labelling strategies? Can you describe the architecture of the Scanner platform?

What were the motivating constraints that led you to your current implementation? How have the design and goals of the product changed since you first started working on it?

Given the security oriented customer base that you are targeting, how do you address trust/network boundaries for compliance with regulatory/organizational policies? What are the personas of the end-users for Scanner?

How has that influenced the way that you think about the query formats, APIs, user experience etc. for the prroduct?

For teams who are working with Scanner can you describe how it fits into their workflow? What are the most interesting, innovative, or unexpected ways that you have seen Scanner used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Scanner? When is Scanner the wrong choice? What do you have planned for the future of Scanner?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the s

Summary

Databases and analytics architectures have gone through several generational shifts. A substantial amount of the data that is being managed in these systems is related to customers and their interactions with an organization. In this episode Tasso Argyros, CEO of ActionIQ, gives a summary of the major epochs in database technologies and how he is applying the capabilities of cloud data warehouses to the challenge of building more comprehensive experiences for end-users through a modern customer data platform (CDP).

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro. That’s three free boards at dataengineeringpodcast.com/miro. Your host is Tobias Macey and today I'm interviewing Tasso Argyros about the role of a customer data platform in the context of the modern data stack

Interview

Introduction How did you get involved in the area of data management? Can you describe what the role of the CDP is in the context of a businesses data ecosystem?

What are the core technical challenges associated with building and maintaining a CDP? What are the organizational/business factors that contribute to the complexity of these systems?

The early days of CDPs came with the promise of "Customer 360". Can you unpack that concept and how it has changed over the past ~5 years? Recent years have seen the adoption of reverse ETL, cloud data warehouses, and sophisticated product analytics suites. How has that changed the architectural approach to CDPs?

How have the architectural shifts changed the ways that organizations interact with their customer data?

How have the responsibilities shifted across different roles?

What are the governance policy and enforcement challenges that are added with the expansion of access and responsibility?

What are the most interesting, innovative, or unexpected ways that you have seen CDPs built/used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on CDPs? When is a CDP the wrong choice? What do you have planned for the future of ActionIQ?

Contact Info

LinkedIn @Tasso on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being us

In this episode of the Data Career Podcast, we include a variety of listener questions, shedding light on topics like the future of data engineering, requirements for becoming a data analyst, showcasing data cleaning proficiency in Excel, and securing data analyst internships.

Also discusses the significance of storytelling and views on Power BI versus Tableau & the impact of AI on data analysis roles.

Tune in now!

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🏫 Check out my 10-week data analytics bootcamp

Timestamps:

(02:10) - What’s the future of data engineering in 2024? (03:06) - Do you need a degree to become a data analyst? (04:57) - How to showcase Excel skills? (07:22) - How to land data analyst internships? (10:10) - What are the main technical skills required to land your first data job? (14:40) - Have you worked with many teachers looking to make a career transition? (16:24) - How to get a data analyst job for people with no work experience? (25:13) - Can you suggest SQL and Excel videos for data analysis? (28:46) - Do you think the data analysis industry is saturated? (28:21) - Do you find data analysts transferring to becoming a data scientist or a data engineer?

Connect with Avery:

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Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

What am I seeing in data engineering in 2024? Listen and find out.

Summary

Data processing technologies have dramatically improved in their sophistication and raw throughput. Unfortunately, the volumes of data that are being generated continue to double, requiring further advancements in the platform capabilities to keep up. As the sophistication increases, so does the complexity, leading to challenges for user experience. Jignesh Patel has been researching these areas for several years in his work as a professor at Carnegie Mellon University. In this episode he illuminates the landscape of problems that we are faced with and how his research is aimed at helping to solve these problems.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Jignesh Patel about the research that he is conducting on technical scalability and user experience improvements around data management

Interview

Introduction How did you get involved in the area of data management? Can you start by summarizing your current areas of research and the motivations behind them? What are the open questions today in technical scalability of data engines?

What are the experimental methods that you are using to gain understanding in the opportunities and practical limits of those systems?

As you strive to push the limits of technical capacity in data systems, how does that impact the usability of the resulting systems?

When performing research and building prototypes of the projects, what is your process for incorporating user experience into the implementation of the product?

What are the main sources of tension between technical scalability and user experience/ease of comprehension? What are some of the positive synergies that you have been able to realize between your teaching, research, and corporate activities?

In what ways do they produce conflict, whether personally or technically?

What are the most interesting, innovative, or unexpected ways that you have seen your research used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on research of the scalability limits of data systems? What is your heuristic for when a given research project needs to be terminated or productionized? What do you have planned for the future of your academic research?

Contact Info

Website LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tel

Summary

Working with financial data requires a high degree of rigor due to the numerous regulations and the risks involved in security breaches. In this episode Andrey Korchack, CTO of fintech startup Monite, discusses the complexities of designing and implementing a data platform in that sector.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Your host is Tobias Macey and today I'm interviewing Andrey Korchak about how to manage data in a fintech environment

Interview

Introduction How did you get involved in the area of data management? Can you start by summarizing the data challenges that are particular to the fintech ecosystem? What are the primary sources and types of data that fintech organizations are working with?

What are the business-level capabilities that are dependent on this data?

How do the regulatory and business requirements influence the technology landscape in fintech organizations?

What does a typical build vs. buy decision process look like?

Fraud prediction in e.g. banks is one of the most well-established applications of machine learning in industry. What are some of the other ways that ML plays a part in fintech?

How does that influence the architectural design/capabilities for data platforms in those organizations?

Data governance is a notoriously challenging problem. What are some of the strategies that fintech companies are able to apply to this problem given their regulatory burdens? What are the most interesting, innovative, or unexpected approaches to data management that you have seen in the fintech sector? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data in fintech? What do you have planned for the future of your data capabilities at Monite?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

Monite ISO 270001 Tesseract GitOps SWIFT Protocol

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By: Starburst:

This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data applications to complete analytics.

Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place. Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Try Starburst Galaxy today, the easiest and fastest way to get started using Trino, and get $500 of credits free. dataengineeringpodcast.com/starburstRudderstack:

Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstackMaterialize:

You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date.

That is Materialize, the only true SQL streaming database built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI. Built on Timely Dataflow and Differential Dataflow, open source frameworks created by cofounder Frank McSherry at Microsoft Research, Materialize is trusted by data and engineering teams at Ramp, Pluralsight, Onward and more to build real-time data products without the cost, complexity, and development time of stream processing.

Go to materialize.com today and get 2 weeks free!Support Data Engineering Podcast

"Data Observability for Data Engineering" introduces you to the foundational concepts of observing and validating data pipeline health. With real-world projects and Python code examples, you'll gain hands-on experience in improving data quality and minimizing risks, enabling you to implement strategies that ensure accuracy and reliability in your data systems. What this Book will help me do Master data observability techniques to monitor and validate data pipelines effectively. Learn to collect and analyze meaningful metrics to gauge and improve data quality. Develop skills in Python programming specific to applying data concepts such as observable data state. Address scalability challenges using state-of-the-art observability frameworks and practices. Enhance your ability to manage and optimize data workflows ensuring seamless operation from start to end. Author(s) Authors Michele Pinto and Sammy El Khammal bring a wealth of experience in data engineering and observing scalable data systems. Pinto specializes in constructing robust analytics platforms while Khammal offers insights into integrating software observability into massive pipelines. Their collaborative writing style ensures readers find both practical advice and theoretical foundations. Who is it for? This book is geared toward data engineers, architects, and scientists who seek to confidently handle pipeline challenges. Whether you're addressing specific issues or wish to introduce proactive measures in your team, this guide meets the needs of those ready to leverage observability as a key practice.

Regardless of profession, the work we do leaves behind a trace of actions that help us achieve our goals. This is especially true for those that work with data. For large enterprises where there are seemingly countless processes happening at any one time, keeping track of these processes is crucial. Given the scale of these processes, one small efficiency gain can leads to a staggering amount of time and money saved. Process mining is a data-driven approach to process analysis that uses event logs to extract process-related information. It can separate inferred facts, from exact truths, and uncover what really happens in a variety of operations.  Wil van der Aalst is a full professor at RWTH Aachen University, leading the Process and Data Science (PADS) group. He is also the Chief Scientist at Celonis, part-time affiliated with the Fraunhofer FIT, and a member of the Board of Governors of Tilburg University.  His research interests include process mining, Petri nets, business process management, workflow management, process modeling, and process analysis. Wil van der Aalst has published over 275 journal papers, 35 books (as author or editor), 630 refereed conference/workshop publications, and 85 book chapters. Cong Yu leads the CeloAI group at Celonis focusing on bringing advanced AI technologies to EMS products, building up capabilities for their knowledge platform, and ultimately helping enterprises in reducing process inefficiencies and achieving operational excellence. Previously, Cong was Principal (Research) Scientist / Research Director at Google Research NYC from September 2010 to July 2022, leading the NYSD/Beacon Research Group, and also taught at NYU Courant Institute of Mathematical Sciences.  In the episode, Wil, Cong, and Richie explore process mining and its development over the past 25 years, the differences between process mining and ML, AI, and data mining, popular use cases of process mining, adoption from large enterprises like BMW, HP, and Dell, the requirements for an effective process mining system, the role of predictive analytics and data engineering in process mining, how to scale process mining systems, prospects within the field and much more. Links Mentioned in the Show: CelonisGartner’s Magic Quadrant for Process MiningPM4PyProcess Query Language (PQL)[Couse] Business Process Analytics in R

Summary

Kafka has become a ubiquitous technology, offering a simple method for coordinating events and data across different systems. Operating it at scale, however, is notoriously challenging. Elad Eldor has experienced these challenges first-hand, leading to his work writing the book "Kafka: : Troubleshooting in Production". In this episode he highlights the sources of complexity that contribute to Kafka's operational difficulties, and some of the main ways to identify and mitigate potential sources of trouble.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Elad Eldor about operating Kafka in production and how to keep your clusters stable and performant

Interview

Introduction How did you get involved in the area of data management? Can you describe your experiences with Kafka?

What are the operational challenges that you have had to overcome while working with Kafka? What motivated to write a book about how to manage Kafka in production?

There are many options now for persistent data queues. What are the factors to consider when determining whether Kafka is the right choice?

In the case where Kafka is the appropriate tool, there are many ways to run it now. What are the considerations that teams need to work through when determining whether/where/how to operate a cluster?

When provisioning a Kafka cluster, what are the requirements that need to be considered when determining the sizing?

What are the axes along which size/scale need to be determined?

The core promise of Kafka is that it is a durable store for continuous data. What are the mechanisms that are available for preventing data loss?

Under what circumstances can data be lost?

What are the different failure conditions that cluster operators need to be aware of?

What are the monitoring strategies that ar

Summary

The "modern data stack" promised a scalable, composable data platform that gave everyone the flexibility to use the best tools for every job. The reality was that it left data teams in the position of spending all of their engineering effort on integrating systems that weren't designed with compatible user experiences. The team at 5X understand the pain involved and the barriers to productivity and set out to solve it by pre-integrating the best tools from each layer of the stack. In this episode founder Tarush Aggarwal explains how the realities of the modern data stack are impacting data teams and the work that they are doing to accelerate time to value.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm welcoming back Tarush Aggarwal to talk about what he and his team at 5x data are building to improve the user experience of the modern data stack.

Interview

Introduction How did you get involved in the area of data management? Can you describe what 5x is and the story behind it?

We last spoke in March of 2022. What are the notable changes in the 5x business and product?

What are the notable shifts in the data ecosystem that have influenced your adoption and product direction?

What trends are you most focused on tracking as you plan the continued evolution of your offerings?

What are the points of friction that teams run into when trying to build their data platform? Can you describe design of the system that you have built?

What are the strategies that you rely on to support adaptability and speed of onboarding for new integrations?

What are some of the types of edge cases that you have to deal with while integrating and operating the platform implementations that you design for your customers? What is your process for selection of vendors to support?

How would you characte

Data modeling is a core skill of data engineering, but it is missing or inadequate in many data engineering teams. These teams focus on moving data with little attention to shaping the data. They engineer processes, not products. Full data engineering is both process and product engineering, and that calls for data modeling. Published at: https://www.eckerson.com/articles/a-fresh-look-at-data-modeling-part-2-rediscovering-the-lost-art-of-data-modeling

Ben Rogojan, aka Seattle Data Guy, needs no introduction. He's a force of nature in the data engineering content world.

We chat about his origin story (sort of like a superhero), content creation, consulting, cooking, and much more.

Summary

If your business metrics looked weird tomorrow, would you know about it first? Anomaly detection is focused on identifying those outliers for you, so that you are the first to know when a business critical dashboard isn't right. Unfortunately, it can often be complex or expensive to incorporate anomaly detection into your data platform. Andrew Maguire got tired of solving that problem for each of the different roles he has ended up in, so he created the open source Anomstack project. In this episode he shares what it is, how it works, and how you can start using it today to get notified when the critical metrics in your business aren't quite right.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro. That’s three free boards at dataengineeringpodcast.com/miro. Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Andrew Maguire about his work on the Anomstack project and how you can use it to run your own anomaly detection for your metrics

Interview

Introduction How did you get involved in the area of data management? Can you describe what Anomstack is and the story behind it?

What are your goals for this project? What other tools/products might teams be evaluating while they consider Anom

With the shift from data warehouses to data lakes, data now lands in repositories before it's been transformed, enabling engineers to model raw data into clean, well-defined datasets. dbt (data build tool) helps you take data further. This practical book shows data analysts, data engineers, BI developers, and data scientists how to create a true self-service transformation platform through the use of dynamic SQL. Authors Rui Machado from Monstarlab and Hélder Russa from Jumia show you how to quickly deliver new data products by focusing more on value delivery and less on architectural and engineering aspects. If you know your business well and have the technical skills to model raw data into clean, well-defined datasets, you'll learn how to design and deliver data models without any technical influence. With this book, you'll learn: What dbt is and how a dbt project is structured How dbt fits into the data engineering and analytics worlds How to collaborate on building data models The main tools and architectures for building useful, functional data models How to fit dbt into data warehousing and laking architecture How to build tests for data transformations

Embark on your data engineering journey with Ricardo Sueiras and learn the essentials of Apache Airflow in 'Getting Started with Apache Airflow - Building Your First Workflow.' 🚀🐍 Discover the architecture and create your first workflow, perfect for beginners eager to explore this open-source powerhouse! 💻📊 #ApacheAirflow #WorkflowCreation

✨ H I G H L I G H T S ✨

🙌 A huge shoutout to all the incredible participants who made Big Data Conference Europe 2023 in Vilnius, Lithuania, from November 21-24, an absolute triumph! 🎉 Your attendance and active participation were instrumental in making this event so special. 🌍

Don't forget to check out the session recordings from the conference to relive the valuable insights and knowledge shared! 📽️

Once again, THANK YOU for playing a pivotal role in the success of Big Data Conference Europe 2023. 🚀 See you next year for another unforgettable conference! 📅 #BigDataConference #SeeYouNextYear

Effective data management has become a cornerstone of success in our digital era. It involves not just collecting and storing information but also organizing, securing, and leveraging data to drive progress and innovation. Many organizations turn to tools like Snowflake for advanced data warehousing capabilities. However, while Snowflake enhances data storage and access, it's not a complete solution for all data management challenges. To address this, tools like Capital One’s Slingshot can be used alongside Snowflake, helping to optimize costs and refine data management strategies. Salim Syed is a VP, Head of engineering for Capital One Slingshot product. He led Capital One’s data warehouse migration to AWS and is a specialist in deploying Snowflake to a large enterprise. Salim’s expertise lies in developing Big Data (Lake) and Data Warehouse strategy on the public cloud. He leads an organization of more than 100 data engineers, support engineers, DBAs and full stack developers in driving enterprise data lake, data warehouse, data management and visualization platform services. Salim has more than 25 years of experience in the data ecosystem. His career started in data engineering where he built data pipelines and then moved into maintenance and administration of large database servers using multi-tier replication architecture in various remote locations. He then worked at CodeRye as a database architect and at 3M Health Information Systems as an enterprise data architect. Salim has been at Capital One for the past six years. In this episode, Adel and Salim explore cloud data management and the evolution of Slingshot into a major multi-tenant SaaS platform, the shift from on-premise to cloud-based data governance, the role of centralized tooling, strategies for effective cloud data management, including data governance, cost optimization, and waste reduction as well as insights into navigating the complexities of data infrastructure, security, and scalability in the modern digital era. Links Mentioned in the Show: Capital One SlingshotSnowflakeCourse: Introduction to Data WarehousingCourse: Introduction to Snowflake

Summary

The first step of data pipelines is to move the data to a place where you can process and prepare it for its eventual purpose. Data transfer systems are a critical component of data enablement, and building them to support large volumes of information is a complex endeavor. Andrei Tserakhau has dedicated his careeer to this problem, and in this episode he shares the lessons that he has learned and the work he is doing on his most recent data transfer system at DoubleCloud.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues for every part of your data workflow, from migration to deployment. Datafold has recently launched a 3-in-1 product experience to support accelerated data migrations. With Datafold, you can seamlessly plan, translate, and validate data across systems, massively accelerating your migration project. Datafold leverages cross-database diffing to compare tables across environments in seconds, column-level lineage for smarter migration planning, and a SQL translator to make moving your SQL scripts easier. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold today! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Andrei Tserakhau about operationalizing high bandwidth and low-latency change-data capture

Interview

Introduction How did you get involved in the area of data management? Your most recent project involves operationalizing a generalized data transfer service. What was the original problem that you were trying to solve?

What were the shortcomings of other options in the ecosystem that led you to building a new system?

What was the design of your initial solution to the problem?

What are the sharp edges that you had to deal with to operate and use that i