Data Engineering Podcast Modern Customer Data Platform Principles
This podcast explores the evolution of database technology, the role of Customer Data Platforms (CDPs) in the business data ecosystem, and architectural approaches to data management. The guest also discusses future plans for ActionIQ and the future of CDPs and data management technology.
01:01:33
Early Database Project
- At 11, Tasso Argyros built a database using FileMaker on a Macintosh to automate his building's management.
- This early experience sparked his lifelong interest in data and database systems.
Database Epochs
- Database architecture shifts every 5-7 years, driven by the pursuit of performance improvements relative to cost.
- This evolution is tied to advancements in hardware, software, and networking.
Hadoop's Legacy
- While Hadoop's processing engines haven't aged well, storage formats like Parquet and Iceberg remain relevant.
- Modern data lakes inherit these storage aspects but rely on entirely new processing engines.
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Introduction
00:00 • 2min
The Role of a Customer Data Platform in the Modern Data Stack
01:46 • 4min
The Evolution of Database Technology
05:58 • 20min
The Role of Customer Data Platforms (CDPs) in the Business Data Ecosystem
25:49 • 20min
Architectural Approaches to Data Management
45:49 • 11min
Future Plans for ActionIQ and Exciting Projects
56:35 • 2min
Future of Customer Data Platforms and Data Management Technology
58:08 • 3min
Summary
Databases and analytics architectures have gone through several generational shifts. A substantial amount of the data that is being managed in these systems is related to customers and their interactions with an organization. In this episode Tasso Argyros, CEO of ActionIQ, gives a summary of the major epochs in database technologies and how he is applying the capabilities of cloud data warehouses to the challenge of building more comprehensive experiences for end-users through a modern customer data platform (CDP).
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
- Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at dataengineeringpodcast.com/miro. That’s three free boards at dataengineeringpodcast.com/miro.
- Your host is Tobias Macey and today I'm interviewing Tasso Argyros about the role of a customer data platform in the context of the modern data stack
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what the role of the CDP is in the context of a businesses data ecosystem?
- What are the core technical challenges associated with building and maintaining a CDP?
- What are the organizational/business factors that contribute to the complexity of these systems?
- The early days of CDPs came with the promise of "Customer 360". Can you unpack that concept and how it has changed over the past ~5 years?
- Recent years have seen the adoption of reverse ETL, cloud data warehouses, and sophisticated product analytics suites. How has that changed the architectural approach to CDPs?
- How have the architectural shifts changed the ways that organizations interact with their customer data?
- How have the responsibilities shifted across different roles?
- What are the governance policy and enforcement challenges that are added with the expansion of access and responsibility?
- What are the most interesting, innovative, or unexpected ways that you have seen CDPs built/used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on CDPs?
- When is a CDP the wrong choice?
- What do you have planned for the future of ActionIQ?
Contact Info
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
- Action IQ
- Aster Data
- Teradata
- Filemaker
- Hadoop
- NoSQL
- Hive
- Informix
- Parquet
- Snowflake
- Spark
- Redshift
- Unity Catalog
- Customer Data Platform
- CDP Market Guide
- Kaizen
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Starburst:  This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data applications to complete analytics. Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place. Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Try Starburst Galaxy today, the easiest and fastest way to get started using Trino, and get $500 of credits free. [dataengineeringpodcast.com/starburst](https://www.dataengineeringpodcast.com/starburst)
- Miro:  Data projects are notoriously complex. With multiple stakeholders to manage across varying backgrounds and toolchains even simple reports can become unwieldy to maintain. Miro is your single pane of glass where everyone can discover, track, and collaborate on your organization's data. I especially like the ability to combine your technical diagrams with data documentation and dependency mapping, allowing your data engineers and data consumers to communicate seamlessly about your projects. Find simplicity in your most complex projects with Miro. Your first three Miro boards are free when you sign up today at [dataengineeringpodcast.com/miro](https://www.dataengineeringpodcast.com/miro).