We talked about:

Juan Pablo's Backround Data engineering resources Teaching calculus Transitioning to Analytics Data Analytics bootcamp Getting money while studying Going to meetups to get a job Looking for uncrowded doors Using LinkedIn Portfolio Talking to people on meetups Eight tips to get your first analytics job Consider contracts and temporary roles Getting experience with non-profits Create your own internship Networking Website for hosting a portfolio I’m a math teacher. What should I learn first? Analytics engineering Best suggestion: keep showing up Networking on online conferences Communication skills and being organized

Links:

Website: https://www.thatjuanpablo.com/ Twitter: https://twitter.com/thatjuanpablo BROKE teacher to FAANG engineer Twitter thread: https://twitter.com/thatjuanpablo/status/1475806246317875203 LinkedIn: https://www.linkedin.com/in/thatjuanpablo/

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

Our events: https://datatalks.club/events.html

Summary The modern data stack is a constantly moving target which makes it difficult to adopt without prior experience. In order to accelerate the time to deliver useful insights at organizations of all sizes that are looking to take advantage of these new and evolving architectures Tarush Aggarwal founded 5X Data. In this episode he explains how he works with these companies to deploy the technology stack and pairs them with an experienced engineer who assists with the implementation and training to let them realize the benefits of this architecture. He also shares his thoughts on the current state of the ecosystem for modern data vendors and trends to watch as we move into the future.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy. So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at dataengineeringpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan. Your host is Tobias Macey and today I’m interviewing Tarush Agarwal about how he and his team are helping organizations streamline adoption of the modern data stack

Interview

Introduction How did you get involved in the area of data management? Can you describe what you are doing at 5x and the story behind it? How has your focus and operating model shifted since we spoke a year ago?

What are the biggest shifts in the market for data management that you have seen in that time?

What are the main challenges that your customers are facing when they start working with you? What are the components that you are relying on to build repeatable data platforms for your customers?

What are the sharp edges that you have had to smooth out to scale your implementation of those

Summary Data observability is a term that has been co-opted by numerous vendors with varying ideas of what it should mean. At Acceldata, they view it as a holistic approach to understanding the computational and logical elements that power your analytical capabilities. In this episode Tristan Spaulding, head of product at Acceldata, explains the multi-dimensional nature of gaining visibility into your running data platform and how they have architected their platform to assist in that endeavor.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. TimescaleDB, from your friends at Timescale, is the leading open-source relational database with support for time-series data. Time-series data is time stamped so you can measure how a system is changing. Time-series data is relentless and requires a database like TimescaleDB with speed and petabyte-scale. Understand the past, monitor the present, and predict the future. That’s Timescale. Visit them today at dataengineeringpodcast.com/timescale Your host is Tobias Macey and today I’m interviewing Tristan Spaulding about Acceldata, a platform offering multidimensional data observability for modern data infrastructure

Interview

Introduction How did you get involved in the area of data? Can you describe what Acceldata is and the story behind it? What does it mean for a data observability platform to be "multidimensional"? How do the architectural characteristics of the "modern data stack" influence the requirements and implementation of data observability strategies? The data observability ecosystem has seen a lot of activity over the past ~2-3 years. What are the unique capabilities/use cases that Acceldata supports? Who are your target users and how does that focus influence the way that you have approached feature and design priorities? What are some of the ways that you are using the Acceldata platform to run Acceldata? Can you describe how the Acceldata platform is implemented?

How have the design and goals of the system changed or evolved since you started working on it?

How are you man

We talked about:

Ellen’s background Why Ellen switched from data science to data engineering The overlap between data science and data engineering Skills to learn and improve for data engineering Ways to pick up and improve skills (advice for making the transition) What makes a data engineering course “good” Languages to know for data engineering The easiest part of transitioning into data engineering The hardest part of transitioning into data engineering Common data engineering team distributions People who are both data scientists and data engineers Pet projects and other ways to pick up development skills Dealing with cloud processing costs (alerts, billing reports, trial periods) Advice for getting into entry level positions Which cloud platform should data engineers learn?

Links:

Twitter: https://twitter.com/ellen_koenig LinkedIn: https://www.linkedin.com/in/ellenkoenig/

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

Our events: https://datatalks.club/events.html

Summary When you think about selecting a database engine for your project you typically consider options focused on serving multiple concurrent users. Sometimes what you really need is an embedded database that is blazing fast for single user workloads. DuckDB is an in-process database engine optimized for OLAP applications to speed up your analytical queries that meets you where you are, whether that’s Python, R, Java, even the web. In this episode, Hannes Mühleisen, co-creator and CEO of DuckDB Labs, shares the motivations for creating the project, the myriad ways that it can be used to speed up your data projects, and the detailed engineering efforts that go into making it adaptable to any environment. This is a fascinating and humorous exploration of a truly useful piece of technology.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. Your host is Tobias Macey and today I’m interviewing Hannes Mühleisen about DuckDB, an in-process embedded database engine for columnar analytics

Interview

Introduction How did you get involved in the area of data management? Can you describe what DuckDB is and the story behind it? Where did the name come from? What are some of the use cases that DuckDB is designed to support? The interface for DuckDB is similar (at least in spirit) to SQLite. What are the deciding factors for when to use one vs. the other?

How might they be used in concert to take advantage of their relative strengths?

What are some of the ways that DuckDB can be used to better effect than options provided by different language ecosystems? Can you describe how DuckDB is implemented?

How has the design and goals of the project changed or evolved since you began working on it? What are some of the optimizations that you have had to make in order to support performant access to data that exceeds available memory?

Can you describe a typical workflow of incorporating DuckDB into an analytical project? What are some of the libraries/tools/systems that DuckDB might replace in the scope of a project or team? What are some of the

Summary Databases are an important component of application architectures, but they are often difficult to work with. HarperDB was created with the core goal of being a developer friendly database engine. In the process they ended up creating a scalable distributed engine that works across edge and datacenter environments to support a variety of novel use cases. In this episode co-founder and CEO Stephen Goldberg shares the history of the project, how it is architected to achieve their goals, and how you can start using it today.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy. So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at dataengineeringpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan. Are you looking for a structured and battle-tested approach for learning data engineering? Would you like to know how you can build proper data infrastructures that are built to last? Would you like to have a seasoned industry expert guide you and answer all your questions? Join Pipeline Academy, the worlds first data engineering bootcamp. Learn in small groups with likeminded professionals for 9 weeks part-time to level up in your career. The course covers the most relevant and essential data and software engineering topics that enable you to start your journey as a professional data engineer or analytics engineer. Plus we have AMAs with world-class guest speakers every week! The next cohort starts in April 2022. Visit dataengineeringpodcast.com/academy and apply now! Your host is Tobias Macey and today I’m interviewing Stephen Goldberg about HarperDB, a developer-friendly distributed database engine designed to scale acros

We talked about:

Rahul’s background What do data engineering managers do and why do we need them? Balancing engineering and management Rahul’s transition into data engineering management The importance of updating your skill set Planning the transition to manager and other challenges Setting expectations for the team and measuring success Data reconciliation GDPR compliance Data modeling for Big Data Advice for people transitioning into data engineering management Staying on top of trends and enabling team members The qualities of a good data engineering team The qualities of a good data engineer candidate (interview advice) The difference between having knowledge and stuffing a CV with buzzwords Advice for students and fresh graduates An overview of an end-to-end data engineering process

Links:

Rahul's LinkedIn: https://www.linkedin.com/in/16rahuljain/

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

Our events: https://datatalks.club/events.html

Summary There are a wealth of options for managing structured and textual data, but unstructured binary data assets are not as well supported across the ecosystem. As organizations start to adopt cloud technologies they need a way to manage the distribution, discovery, and collaboration of data across their operating environments. To help solve this complicated challenge Krishna Subramanian and her co-founders at Komprise built a system that allows you to treat use and secure your data wherever it lives, and track copies across environments without requiring manual intervention. In this episode she explains the difficulties that everyone faces as they scale beyond a single operating environment, and how the Komprise platform reduces the burden of managing large and heterogeneous collections of unstructured files.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy. So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at dataengineeringpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan. Your host is Tobias Macey and today I’m interviewing Krishna Subramanian about her work at Komprise to generate value from unstructured file and object data across storage formats and locations

Interview

Introduction How did you get involved in the area of data management? Can you describe what Komprise is and the story behind it? Who are the target customers of the Komprise platform?

What are the core use cases that you are focused on supporting?

How would you characterize the common approaches to managing file storage solutions for hybrid cloud environments?

What are some of the shortcomings of the enterprise storage providers’ met

Summary Building a data platform is a complex journey that requires a significant amount of planning to do well. It requires knowledge of the available technologies, the requirements of the operating environment, and the expectations of the stakeholders. In this episode Tobias Macey, the host of the show, reflects on his plans for building a data platform and what he has learned from running the podcast that is influencing his choices.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription TimescaleDB, from your friends at Timescale, is the leading open-source relational database with support for time-series data. Time-series data is time stamped so you can measure how a system is changing. Time-series data is relentless and requires a database like TimescaleDB with speed and petabyte-scale. Understand the past, monitor the present, and predict the future. That’s Timescale. Visit them today at dataengineeringpodcast.com/timescale RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. I’m your host, Tobias Macey, and today I’m sharing the approach that I’m taking while designing a data platform

Interview

Introduction How did you get involved in the area of data management? What are the components that need to be considered when designing a solution?

Data integration (extract and load)

What are your data sources? Batch or streaming (acceptable latencies)

Data storage (lake or warehouse)

How is the data going to be used? What other tools/systems will need to integrate with it? The warehouse (Bigquery, Snowflake, Redshift) has become the focal point of the "modern data stack"

Data orchestration

Who will be managing the workflow logic?

Metadata repository

Types of metadata (catalog, lineage, access, queries, etc.)

Semantic layer/reporting Data applications

Implementation phases

Build a single end-to-end workflow of a data application using a single category of data across sources Validate the ability for an analyst/data scientist to self-serve a notebook powered analysis Iterate

Risks/unknowns

Data modeling requirements Specific implementation details as integrations acros

The "Azure Data Engineer Associate Certification Guide" is a comprehensive resource tailored for professionals preparing for the DP-203 exam. This book not only equips you with the theoretical knowledge needed to pass the certification but also provides hands-on experience with Azure's data engineering services. By the end of the book, you'll feel confident in tackling the certification exam and applying these skills on the job. What this Book will help me do Understand the core concepts of Azure data engineering and their practical applications. Gain proficiency in designing and deploying data storage and processing solutions using Azure services. Develop expertise in securing, monitoring, and optimizing Azure data solutions. Prepare effectively for the DP-203 certification exam with sample questions and practical exercises. Acquire skills to contribute to and excel in real-world Azure Data Engineering projects. Author(s) None Alex is a seasoned data engineer and cloud computing expert with years of experience designing, implementing, and optimizing data solutions. They have spent significant time working with Azure's ecosystem and have crafted this guide to share their insights and best practices. With a passion for teaching and mentoring, they aim to make complex technical concepts accessible to learners. Who is it for? This book caters to data engineering professionals aiming to achieve the DP-203 Azure Data Engineer Associate certification and advance their careers. It's ideal for individuals with fundamental knowledge of cloud-based data solutions and databases, seeking specialized expertise in Azure's data engineering tools. Whether you're upskilling or transitioning to a cloud-native environment, this guide serves as the roadmap to success.

Summary Python has grown to be one of the top languages used for all aspects of data, from collection and cleaning, to analysis and machine learning. Along with that growth has come an explosion of tools and engines that help power these workflows, which introduces a great deal of complexity when scaling from single machines and exploratory development to massively parallel distributed computation. In answer to that challenge the Fugue project offers an interface to automatically translate across Pandas, Spark, and Dask execution environments without having to modify your logic. In this episode core contributor Kevin Kho explains how the slight differences in the underlying engines can lead to big problems, how Fugue works to hide those differences from the developer, and how you can start using it in your own work today.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses. Every data project starts with collecting the information that will provide answers to your questions or inputs to your models. The web is the largest trove of information on the planet and Oxylabs helps you unlock its potential. With the Oxylabs scraper APIs you can extract data from even javascript heavy websites. Combined with their residential proxies you can be sure that you’ll have reliable and high quality data whenever you need it. Go to dataengineeringpodcast.com/oxylabs today and use code DEP25 to get your special discount on residential proxies. Your host is Tobias Macey and today I’m interviewing Kevin Kho about Fugue, a library that offers a unified interface for distributed computing that lets users execute Python, pandas, and SQL code on Spark and Dask without rewrites

Interview

Introduction How did you get involved in the area of data management? Can you describe what Fugue is and the story behind it? What are the core goals of the Fugue project? Who are the target users for Fugue and how does that influence the feature priorities and API design? How does Fugue compare to projects such as Modin, etc. for abst

Summary The life sciences as an industry has seen incredible growth in scale and sophistication, along with the advances in data technology that make it possible to analyze massive amounts of genomic information. In this episode Guy Yachdav, director of software engineering for ImmunAI, shares the complexities that are inherent to managing data workflows for bioinformatics. He also explains how he has architected the systems that ingest, process, and distribute the data that he is responsible for and the requirements that are introduced when collaborating with researchers, domain experts, and machine learning developers.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy. Your host is Tobias Macey and today I’m interviewing Guy Yachdav, Director of Software Engineering at Immunai, about his work at Immunai to wrangle biological data for advancing research into the human immune system.

Interview

Introduction (see Guy’s bio below) How did you get involved in the area of data management? Can you describe what Immunai is and the story behind it? What are some of the categories of information that you are working with?

What kinds of insights are you trying to power/questions that you are trying to answer with that data?

Who are the stakeholders that you are working with and how does that influence your approach to the integration/transformation/presentation of the data? What are some of the challenges unique to the biological data domain that you have had to address?

What are some of the limitations in the off-the-shelf tools when applied to biological data? How have you approached the selection of tools/techniques/technologies to make your work maintainable for your engineers and accessible for your end users?

Can

Summary Streaming data sources are becoming more widely available as tools to handle their storage and distribution mature. However it is still a challenge to analyze this data as it arrives, while supporting integration with static data in a unified syntax. Deephaven is a project that was designed from the ground up to offer an intuitive way for you to bring your code to your data, whether it is streaming or static without having to know which is which. In this episode Pete Goddard, founder and CEO of Deephaven shares his journey with the technology that powers the platform, how he and his team are pouring their energy into the community edition of the technology so that you can use it freely in your own work.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription StreamSets DataOps Platform is the world’s first single platform for building smart data pipelines across hybrid and multi-cloud architectures. Build, run, monitor and manage data pipelines confidently with an end-to-end data integration platform that’s built for constant change. Amp up your productivity with an easy-to-navigate interface and 100s of pre-built connectors. And, get pipelines and new hires up and running quickly with powerful, reusable components that work across batch and streaming. Once you’re up and running, your smart data pipelines are resilient to data drift. Those ongoing and unexpected changes in schema, semantics, and infrastructure. Finally, one single pane of glass for operating and monitoring all your data pipelines. The full transparency and control you desire for your data operations. Get started building pipelines in minutes for free at dataengineeringpodcast.com/streamsets. The first 10 listeners of the podcast that subscribe to StreamSets’ Professional Tier, receive 2 months free after their first month. Your host is Tobias Macey and today I’m interviewing Pete Goddard about his work at Deephaven, a query engine optimized for manipulating and merging streaming and static data

Interview

Introduction How did you get involved in the area of data management? Can you describe what Deephaven is and the story behind it? What is the role of Deephaven in the context of an organization’s data platform?

What are the upstream and downstream systems and teams that it is likely to be integrated with?

Who are the target users of Deephaven and how does that influence the feature priorities and design of the platform? comparison of use cases/experience with Materialize What are the different components that comprise the suite of functionality in Deephaven? How have you architected the system?

What are some of the ways t

Summary Collecting, integrating, and activating data are all challenging activities. When that data pertains to your customers it can become even more complex. To simplify the work of managing the full flow of your customer data and keep you in full control the team at Rudderstack created their eponymous open source platform that allows you to work with first and third party data, as well as build and manage reverse ETL workflows. In this episode CEO and founder Soumyadeb Mitra explains how Rudderstack compares to the various other tools and platforms that share some overlap, how to set it up for your own data needs, and how it is architected to scale to meet demand.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy. The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses. Your host is Tobias Macey and today I’m interviewing Soumyadeb Mitra about his experience as the founder of Rudderstack and its role in your data platform

Interview

Introduction How did you get involved in the area of data management? Can you describe what Rudderstack is and the story behind it? What are the main use cases that Rudderstack is designed to support? Who are the target users of Rudderstack?

How does the availability of the managed cloud service change the user profiles that you can target? How do these user profiles influence your focus and prioritization of features and user experience?

How would you characterize the position of Rudderstack in the current data ecosystem?

What other tools/systems might you replace with Rudderstack?

How do you think about the application of Rudderstack compared to tools for data integration (e.g. Singer, Stitch, Fivetran) and reverse ETL (e.g. Grouparoo, Hightouch, Census)? Can you describe how the Rudderstack platform is desig

Summary Along with globalization of our societies comes the need to analyze the geospatial and geotemporal data that is needed to manage the growth in commerce, communications, and other activities. In order to make geospatial analytics more maintainable and scalable there has been an increase in the number of database engines that provide extensions to their SQL syntax that supports manipulation of spatial data. In this episode Matthew Forrest shares his experiences of working in the domain of geospatial analytics and the application of SQL dialects to his analysis.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription StreamSets DataOps Platform is the world’s first single platform for building smart data pipelines across hybrid and multi-cloud architectures. Build, run, monitor and manage data pipelines confidently with an end-to-end data integration platform that’s built for constant change. Amp up your productivity with an easy-to-navigate interface and 100s of pre-built connectors. And, get pipelines and new hires up and running quickly with powerful, reusable components that work across batch and streaming. Once you’re up and running, your smart data pipelines are resilient to data drift. Those ongoing and unexpected changes in schema, semantics, and infrastructure. Finally, one single pane of glass for operating and monitoring all your data pipelines. The full transparency and control you desire for your data operations. Get started building pipelines in minutes for free at dataengineeringpodcast.com/streamsets. The first 10 listeners of the podcast that subscribe to StreamSets’ Professional Tier, receive 2 months free after their first month. Your host is Tobias Macey and today I’m interviewing Matthew Forrest about doing spatial analysis in SQL

Interview

Introduction How did you get involved in the area of data management? Can you describe what spatial SQL is and some of the use cases that it is relevant for? compatibility with/comparison to syntax from PostGIS What is involved in implementation of spatial logic in database engines mapping geospatial concepts into declarative syntax foundational data types data modeling workflow for analyzing spatial data sets outside of database engines translating from e.g. geopandas to SQL level of support in database engines for spatial data types What are the most interesting, innovative, or unexpected ways that you have seen spatial SQL used? What are the most interesting, unexpected, or challenging lessons that you have learned while working with spatial SQL? When is SQL the wrong choice for spatial analysis? What do you have planned for the future o

Summary There are many dimensions to the work of protecting the privacy of users in our data. When you need to share a data set with other teams, departments, or businesses then it is of utmost importance that you eliminate or obfuscate personal information. In this episode Will Thompson explores the many ways that sensitive data can be leaked, re-identified, or otherwise be at risk, as well as the different strategies that can be employed to mitigate those attack vectors. He also explains how he and his team at Privacy Dynamics are working to make those strategies more accessible to organizations so that you can focus on all of the other tasks required of you.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy. The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses. Your host is Tobias Macey and today I’m interviewing Will Thompson about managing data privacy concerns for data sets used in analytics and machine learning

Interview

Introduction How did you get involved in the area of data management? Data privacy is a multi-faceted problem domain. Can you start by enumerating the different categories of privacy concern that are involved in analytical use cases? Can you describe what Privacy Dynamics is and the story behind it?

Which categor(y|ies) are you focused on addressing?

What are some of the best practices in the definition, protection, and enforcement of data privacy policies?

Is there a data security/privacy equivalent to the OWASP top 10?

What are some of the techniques that are available for anonymizing data while maintaining statistical utility/significance?

What are some of the engineering/systems capabilities that are required for data (platform) engineers to incorporate these practices in their platforms?

What are the tradeoffs of encryption vs. obfuscation when anonymizing data? What are some of the types of PII that are non-obvious? What are the risks associated with data re-identification, and what are some of the vectors that might be exploited to achieve that?

How can privacy risks mitigation be maintained as new data sources are introduced that might contribute to these re-identification vectors?

Can you describe how Privacy Dynamics is implemented?

What are the most challenging engineering problems that you are dealing with?

How do you approach validation of a data set’s privacy? What have you found to be useful heuristics for identifying private data?

What are the risks of false positives vs. false negatives?

Can you describe what is involved in integrating the Privacy Dynamics system into an existing data platform/warehouse?

What would be required to integrate with systems such as Presto, Clickhouse, Druid, etc.?

What are the most interesting, innovative, or unexpected ways that you have seen Privacy Dynamics used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Privacy Dynamics? When is Privacy Dynamics the wrong choice? What do you have planned for the future of Privacy Dynamics?

Contact Info

LinkedIn @willseth 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 show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. 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 iTunes and tell your friends and co-workers

Links

Privacy Dynamics Pandas

Podcast Episode – Pandas For Data Engineering

Homomorphic Encryption Differential Privacy Immuta

Podcast Episode

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

Support Data Engineering Podcast

Join Simo & friends at the fireside to discuss topics ranging from analytics to data engineering, from career advice to data-related life hacks, and anything and everything you can think of. Audience participation is (almost) mandatory.

Summary The Data Engineering Podcast has been going for five years now and has included conversations and interviews with a huge number of guests, covering a broad range of topics. In addition to that, the host curated the essays contained in the book "97 Things Every Data Engineer Should Know", using the knowledge and context gained from running the show to inform the selection process. In this episode he shares some reflections on producing the podcast, compiling the book, and relevant trends in the ecosystem of data engineering. He also provides some advice for those who are early in their career of data engineering and looking to advance in their roles.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription StreamSets DataOps Platform is the world’s first single platform for building smart data pipelines across hybrid and multi-cloud architectures. Build, run, monitor and manage data pipelines confidently with an end-to-end data integration platform that’s built for constant change. Amp up your productivity with an easy-to-navigate interface and 100s of pre-built connectors. And, get pipelines and new hires up and running quickly with powerful, reusable components that work across batch and streaming. Once you’re up and running, your smart data pipelines are resilient to data drift. Those ongoing and unexpected changes in schema, semantics, and infrastructure. Finally, one single pane of glass for operating and monitoring all your data pipelines. The full transparency and control you desire for your data operations. Get started building pipelines in minutes for free at dataengineeringpodcast.com/streamsets. The first 10 listeners of the podcast that subscribe to StreamSets’ Professional Tier, receive 2 months free after their first month. Your host is Tobias Macey and today I’m doing something a bit different. I’m going to talk about some of the lessons that I have learned while running the podcast, compiling the book "97 Things Every Data Engineer Should Know", and some of the themes that I’ve observed throughout.

Interview

Introduction How did you get involved in the area of data management? Overview of the 97 things book

How the project came about Goals of the book

What are the paths into data engineering? What are some of the macroscopic themes in the industry? What are some of the microscopic details that are useful/necessary to succeed as a data engineer? What are some of the career/team/organizational details that are helpful for data engineers? What are the most interesting, innovative, or unexpected outcomes/feedback that I have seen from running the podcast and working on the book

Summary Pandas is a powerful tool for cleaning, transforming, manipulating, or enriching data, among many other potential uses. As a result it has become a standard tool for data engineers for a wide range of applications. Matt Harrison is a Python expert with a long history of working with data who now spends his time on consulting and training. He recently wrote a book on effective patterns for Pandas code, and in this episode he shares advice on how to write efficient data processing routines that will scale with your data volumes, while being understandable and maintainable.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy. The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses. Your host is Tobias Macey and today I’m interviewing Matt Harrison about useful tips for using Pandas for data engineering projects

Interview

Introduction How did you get involved in the area of data management? What are the main tasks that you have seen Pandas used for in a data engineering context? What are some of the common mistakes that can lead to poor performance when scaling to large data sets? What are some of the utility features that you have found most helpful for data processing? One of the interesting add-ons to Pandas is its integration with Arrow. What are some of the considerations for how and when to use the Arrow capabilities vs. out-of-the-box Pandas? Pandas is a tool that spans data processing and data science. What are some of the ways that data engineers should think about writing their code to make it accessible to data scientists for supporting collaboration across data workflows? Pandas is often used for transformation logic. What are some of the ways that engineers should approach the design of their code to make it understandable and maint

Summary Data engineering is a relatively young and rapidly expanding field, with practitioners having a wide array of experiences as they navigate their careers. Ashish Mrig currently leads the data analytics platform for Wayfair, as well as running a local data engineering meetup. In this episode he shares his career journey, the challenges related to management of data professionals, and the platform design that he and his team have built to power analytics at a large company. He also provides some excellent insights into the factors that play into the build vs. buy decision at different organizational sizes.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Today’s episode is Sponsored by Prophecy.io – the low-code data engineering platform for the cloud. Prophecy provides an easy-to-use visual interface to design & deploy data pipelines on Apache Spark & Apache Airflow. Now all the data users can use software engineering best practices – git, tests and continuous deployment with a simple to use visual designer. How does it work? – You visually design the pipelines, and Prophecy generates clean Spark code with tests on git; then you visually schedule these pipelines on Airflow. You can observe your pipelines with built in metadata search and column level lineage. Finally, if you have existing workflows in AbInitio, Informatica or other ETL formats that you want to move to the cloud, you can import them automatically into Prophecy making them run productively on Spark. Create your free account today at dataengineeringpodcast.com/prophecy. The only thing worse than having bad data is not knowing that you have it. With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders. With complete API access, a user-friendly interface, and automated yet flexible alerting, you’ve got everything you need to establish and maintain trust in your data. Go to dataengineeringpodcast.com/bigeye today to sign up and start trusting your analyses. Your host is Tobias Macey and today I’m interviewing Ashish Mrig about his path as a data engineer

Interview

Introduction How did you get involved in the area of data management? You currently lead a data engineering team at a relatively large company. What are the topics that account for the majority of your time and energy? What are some of the most valuable lessons that you’ve learned about managing and motivating teams of data professionals? What has been your most consistent challenge across the different generations of the data ecosystem? How is your current data platform architected? Given the current state of the technology and services landscape, how would you approach the design and implementation of a greenfield rebuild of your platform? What are some of the pitfalls that you have seen data teams encounter most frequently? You are running a data engineering meetup for your local community in the Boston area. What have been some of the recurring themes that are discussed in those events?

Contact Info

Medium Blog LinkedIn