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.
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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
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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 Soumyadeb Mitra about his experience as the founder of Rudderstack and its role in your data platform
- 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 designed and implemented?
- How have the goals/design/use cases of Rudderstack changed or evolved since you first started working on it?
- What are the different extension points available for engineers to extend and customize Rudderstack?
- Working with customer data is a core capability in Rudderstack. How do you manage the identity resolution of users as they transition back and forth between anonymous and identified?
- What are some of the data privacy primitives that you include to assist with data security/regulatory concerns?
- What is the process of getting started with Rudderstack as a software or data platform engineer?
- What are some of the operational challenges related to running your own deployment of Rudderstack?
- What are some of the overlooked/underemphasized capabilities of Rudderstack?
- How have you approached the governance model/boundaries between OSS and commercial for Rudderstack?
- What are the most interesting, innovative, or unexpected ways that you have seen Rudderstack used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Rudderstack?
- When is Rudderstack the wrong choice?
- What do you have planned for the future of Rudderstack?
- 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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