Applications of data have grown well beyond the venerable business intelligence dashboards that organizations have relied on for decades. Now it is being used to power consumer facing services, influence organizational behaviors, and build sophisticated machine learning systems. Given this increased level of importance it has become necessary for everyone in the business to treat data as a product in the same way that software applications have driven the early 2000s. In this episode Brian McMillan shares his work on the book "Building Data Products" and how he is working to educate business users and data professionals about the combination of technical, economical, and business considerations that need to be blended for these projects to succeed.
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.
Sign up for a free account today at dataengineeringpodcast.com/prophecy
At StreamSets, our mission is to make data engineering teams wildly successful. The StreamSets DataOps Platform is a modern, end-to-end data integration platform to build, run, monitor and manage data pipelines, and embraces the DataOps philosophy of continuous data. Only StreamSets provides a single design experience for all design patterns to enable 10x greater developer productivity; smart data pipelines that are resilient to change to reduce breakages by 80%; and a single pane of glass for managing and monitoring all pipelines across hybrid and cloud architectures to eliminate blind spots and control gaps. With StreamSets, you can deliver continuous data for modern analytics, despite constant change.
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- 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.
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- Your host is Tobias Macey and today I’m interviewing Brian McMillan about building data products and his book to introduce the work of data analysts and engineers to non-programmers
- How did you get involved in the area of data management?
- Can you describe what motivated you to write a book about the work of building data products?
- Who is your target audience?
- What are the main goals that you are trying to achieve through the book?
- What was your approach for determining the structure and contents of the book?
- What are the core principles of data engineering that have remained from the original wave of ETL tools and rigid data warehouses?
- What are some of the new foundational elements of data products that need to be codified for the next generation of organizations and data professionals?
- There is a lot of activity and conversation happening in and around data which can make it difficult to understand which parts are signal and which are noise. What, if anything, do you see as being truly new and/or innovative?
- Are there any core lessons or principles that you consider to be at risk of getting drowned out in the current frenzy of activity?
- How do the practices for building products with small teams differ from those employed by larger groups?
- What do you see as the threshold beyond which a team can no longer be considered "small"?
- What are the roles/skills/titles that you view as necessary for building data products in the current phase of maturity for the ecosystem?
- What do you see as the biggest risks to engineering and data teams?
- What are the most interesting, innovative, or unexpected ways that you have seen the principles in the book used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on the book?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- 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.
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