Summary The customer data platform is a category of services that was developed early in the evolution of the current era of cloud services for data processing. When it was difficult to wire together the event collection, data modeling, reporting, and activation it made sense to buy monolithic products that handled every stage of the customer data lifecycle. Now that the data warehouse has taken center stage a new approach of composable customer data platforms is emerging. In this episode Darren Haken is joined by Tejas Manohar to discuss how Autotrader UK is addressing their customer data needs by building on top of their existing data stack. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management 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/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) Your host is Tobias Macey and today I'm interviewing Darren Haken and Tejas Manohar about building a composable CDP and how you can start adopting it incrementally Interview Introduction How did you get involved in the area of data management? Can you describe what you mean by a "composable CDP"? What are some of the key ways that it differs from the ways that we think of a CDP today? What are the problems that you were focused on addressing at Autotrader that are solved by a CDP? One of the promises of the first generation CDP was an opinionated way to model your data so that non-technical teams could own this responsibility. What do you see as the risks/tradeoffs of moving CDP functionality into the same data stack as the rest of the organization? What about companies that don't have the capacity to run a full data infrastructure? Beyond the core technology of the data warehouse, what are the other evolutions/innovations that allow for a CDP experience to be built on top of the core data stack? added burden on core data teams to generate event-driven data models When iterating toward a CDP on top of the core investment of the infrastructure to feed and manage a data warehouse, what are the typical first steps? What are some of the components in the ecosystem that help to speed up the time to adoption? (e.g. pre-built dbt packages for common transformations, etc.) What are the most interesting, innovative, or unexpected ways that you have seen CDPs implemented? What are the most interesting, unexpected, or challenging lessons that you have learned while working on CDP related functionality? When is a CDP (composable or monolithic) the wrong choice? What do you have planned for the future of the CDP stack? Contact Info Darren LinkedIn (https://www.linkedin.com/in/darrenhaken/?originalSubdomain=uk) @DarrenHaken (https://twitter.com/darrenhaken) on Twitter Tejas LinkedIn (https://www.linkedin.com/in/tejasmanohar) @tejasmanohar (https://twitter.com/tejasmanohar) 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. 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To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Autotrader (https://www.autotrader.co.uk/) Hightouch (https://hightouch.com/) Customer Studio (https://hightouch.com/platform/customer-studio) CDP == Customer Data Platform (https://blog.hubspot.com/service/customer-data-platform-guide) Segment (https://segment.com/) Podcast Episode (https://www.dataengineeringpodcast.com/segment-customer-analytics-episode-72/) mParticle (https://www.mparticle.com/) Salesforce (https://www.salesforce.com/) Amplitude (https://amplitude.com/) Snowplow (https://snowplow.io/) Podcast Episode (https://www.dataengineeringpodcast.com/snowplow-with-alexander-dean-episode-48/) Reverse ETL (https://medium.com/memory-leak/reverse-etl-a-primer-4e6694dcc7fb) dbt (https://www.getdbt.com/) Podcast Episode (https://www.dataengineeringpodcast.com/dbt-data-analytics-episode-81/) Snowflake (https://www.snowflake.com/en/) Podcast Episode (https://www.dataengineeringpodcast.com/snowflakedb-cloud-data-warehouse-episode-110/) BigQuery (https://cloud.google.com/bigquery) Databricks (https://www.databricks.com/) ELT (https://en.wikipedia.org/wiki/Extract,_load,_transform) Fivetran (https://www.fivetran.com/) Podcast Episode (https://www.dataengineeringpodcast.com/fivetran-data-replication-episode-93/) DataHub (https://datahubproject.io/) Podcast Episode (https://www.dataengineeringpodcast.com/acryl-data-datahub-metadata-graph-episode-230/) Amundsen (https://www.amundsen.io/) Podcast Episode (https://www.dataengineeringpodcast.com/amundsen-data-discovery-episode-92/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)