Building Auditable Spark Pipelines At Capital One
Data Engineering Podcast
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How Do You Deal With High Level Data?
The primary business case is computing urn and suffessing that information for customers indifferent means. We deal with lod of jasons, and we pass this lot of information in the word enriching or filtering. Dot also gets intote more into the parke side of probably tosal high level data types we deal with. That brings us to the work that you're doing to day. And im wondering if you can just give a bit of an over view about some of the types of data that you're dealing with and some of the work fow irresponsible for in the work you're doing.
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Summary
Spark is a powerful and battle tested framework for building highly scalable data pipelines. Because of its proven ability to handle large volumes of data Capital One has invested in it for their business needs. In this episode Gokul Prabagaren shares his use for it in calculating your rewards points, including the auditing requirements and how he designed his pipeline to maintain all of the necessary information through a pattern of data enrichment.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Gokul Prabagaren about how he is using Spark for real-world workflows at Capital One
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you start by giving an overview of the types of data and workflows that you are responsible for at Capital one?
- In terms of the three "V"s (Volume, Variety, Velocity), what is the magnitude of the data that you are working with?
- What are some of the business and regulatory requirements that have to be factored into the solutions that you design?
- Who are the consumers of the data assets that you are producing?
- Can you describe the technical elements of the platform that you use for managing your data pipelines?
- What are the various ways that you are using Spark at Capital One?
- You wrote a post and presented at the Databricks conference about your experience moving from a data filtering to a data enrichment pattern for segmenting transactions. Can you give some context as to the use case and what your design process was for the initial implementation?
- What were the shortcomings to that approach/business requirements which led you to refactoring the approach to one that maintained all of the data through the different processing stages?
- What are some of the impacts on data volumes and processing latencies working with enriched data frames persisted between task steps?
- What are some of the other optimizations or improvements that you have made to that pipeline since you wrote the post?
- What are some of the limitations of Spark that you have experienced during your work at Capital One?
- How have you worked around them?
- What are the most interesting, innovative, or unexpected ways that you have seen Spark used at Capital One?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on data engineering at Capital One?
- What are some of the upcoming projects that you are focused on/excited for?
- How has your experience with the filtering vs. enrichment approach influenced your thinking on other projects that you work on?
Contact Info
- @gocool_p 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.
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Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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