Summary

CreditKarma builds data products that help consumers take advantage of their credit and financial capabilities. To make that possible they need a reliable data platform that empowers all of the organization’s stakeholders. In this episode Vishnu Venkataraman shares the journey that he and his team have taken to build and evolve their systems and improve the product offerings that they are able to support.

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 new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show!
• Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. By the time errors have made their way into production, it’s often too late and damage is done. Datafold built automated regression testing to help data and analytics engineers deal with data quality in their pull requests. Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. No more shipping and praying, you can now know exactly what will change in your database! Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold. 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 extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudder
• Data teams are increasingly under pressure to deliver. According to a recent survey by Ascend.io, 95% in fact reported being at or over capacity. With 72% of data experts reporting demands on their team going up faster than they can hire, it’s no surprise they are increasingly turning to automation. In fact, while only 3.5% report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. 85%!!! That’s where our friends at Ascend.io come in. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend automates workloads on Snowflake, Databricks, BigQuery, and open source Spark, and can be deployed in AWS, Azure, or GCP. Go to dataengineeringpodcast.com/ascend and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $5,000 when you become a customer.
• Your host is Tobias Macey and today I’m interviewing Vishnu Venkataraman about building the data platform at CreditKarma and the forces that shaped the design

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you describe what CreditKarma is and the role of data in the business?
• What is the current team topology that you are using to support data needs in the organization?
  • How has that evolved from when you first started with the company?
• What are some of the characteristics of the data that you work with? (e.g. volume/variety/velocity, source of the data, format of the data)
  • What are the aspects of data management and architecture that have posed the greatest challenge?
• What are the data applications that are providing the greatest ROI and/or seeing the most usage?
• How have you approached the design and growth of your data platform?
• CreditKarma was one of the first FinTech companies to migrate to the cloud, specifically GCP. Why migrate? What were some of your early challenges taking the company to the cloud?
• What are the main components of your data platform?
  • What are the most notable evolutions that it has gone through?
• Given your strong focus on applications of data science and ML, how has that influenced the architectural foundations of your data capabilities?
• What is your process for evaluating build vs. buy decisions?
  • What are your triggers for deciding when to re-evaluate components of your platform?
• Given your work with financial institutions how do you address testing and validation of your derived data? How does your team solve for data reliability and quality more broadly?
• What are the most interesting, innovative, or unexpected aspects of your growth as a data-led organization?
• What are the most interesting, unexpected, or challenging lessons that you have learned while building up your data platform and teams?
• When are the most informative mistakes that you have made?
• What do you have planned for the future of your data platform?

Contact Info

• LinkedIn
• @vishnuvram 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 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 hosts@dataengineeringpodcast.com) 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

• CreditKarma
• Games 24×7
• Vertica
• BigQuery
• Google Cloud Dataflow
• Anodot

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