The document summarizes several data warehousing trends presented by speaker Chris Riccomini. Some of the trends discussed include: real-time data warehousing which handles hard deletes and replay from Kafka; data mesh which advocates for decentralized data ownership and treating data as a product; and headless BI which programs business metrics for use across systems rather than being confined within BI tools. The talk also covered data catalogs, reverse ETL, analytics engineering and other trends.

1. Data Tech Talk
ft. external speaker
Chris Riccomini, Author, Engineer & Manager, …
1. Talk: Data Warehousing Trends
2. Open Dialogue: Q & A

2. Data
Warehousing
Trends 2021/12/09 · Chris Riccomini

3. Hi, I’m Chris
● Engineer & Manager
WePay, LinkedIn, PayPal
● Open Source
Apache Samza, Apache Airflow, Debezium
● Author
The Missing README
● Investor & Advisor
Prefect, Meroxa, StarTree, Amundsen, Anomalo, TopCoat, ...
@criccomini

5. The Trends
● Realtime DWHs
● Analytics Engineering
● Data Mesh
● Data Catalogs
● Reverse ETL
● Headless BI
● Data Integrity
● Data Lakehouses
● DataOps
● White-label Data Viz
https://twitter.com/criccomini/status/1451557884769169412
https://preset.io/blog/reshaping-data-engineering/

6. The Trends
● Realtime DWHs
● Analytics Engineering
● Data Mesh
● Data Catalogs
● Reverse ETL
● Headless BI
● Data Integrity
● Data Lakehouses
● DataOps
● White-label Data Viz
https://twitter.com/criccomini/status/1451557884769169412
https://preset.io/blog/reshaping-data-engineering/

7. You are here

8. Realtime DWHs

9. Batch ETL

10. Pubsub

11. Realtime DWH

12. Realtime DWH

13. Why Realtime DWHs?
● Debugging
○ Investigate application errors
○ Audit log shows how things changed
● Operational
○ Monitoring
○ Scripts that pull from DWH
● Security/Compliance
○ Audit log
● Customer data products
○ Ad hoc customer reports (e.g. Stripe Sigma, WePay txns)
○ Data clean rooms

14. Realtime DWHs Technical Advantages
● Handles hard deletes
● No schema requirements (timestamps)
● Replay from Kafka
● Data integration

15. Realtime DWH Drawbacks
● Operationally complex
● Depends on source DB support (for CDC)
● Inline transformation is harder
● Fixing bad data is harder

16. Data Mesh

17. “A data mesh is a type of data platform architecture that embraces the ubiquity of
data in the enterprise by leveraging a domain-oriented, self-serve design.”
● Domain-oriented decentralized data ownership and architecture
● Data as a product
● Self-serve data infrastructure as a platform
● Federated computational governance
Data Mesh
https://towardsdatascience.com/what-is-a-data-mesh-and-how-not-to-mesh-it-up-210710bb41e0

18. “A data mesh is a type of data platform architecture that embraces the ubiquity of
data in the enterprise by leveraging a domain-oriented, self-serve design.”
● Domain-oriented decentralized data ownership and architecture
● Data as a product
● Self-serve data infrastructure as a platform
● Federated computational governance
Data Mesh
https://towardsdatascience.com/what-is-a-data-mesh-and-how-not-to-mesh-it-up-210710bb41e0
wat.

19. “A product is any item or service you sell to serve a customer's need or want.”
Data is a Product
https://www.aha.io/roadmapping/guide/product-management/what-is-a-product

20. ● Customers
○ Data scientists
○ Business analysts
○ Finance
○ Sales
○ Product managers
○ Engineers
○ External customers
● Products
○ Recommender systems
○ Billing
○ Fraud
○ Reports
○ Dashboards
Data is a Product
https://cnr.sh/essays/what-the-heck-data-mesh

21. ● Versioned
● Compatible
● Documented
● Monitored
● Self-serve
● Secure (AuthN, AuthZ)
Treat Data Models like APIs
https://cnr.sh/essays/what-the-heck-data-mesh

22. ● Microservice
● DevOps
We’ve Done This Before
https://cnr.sh/essays/what-the-heck-data-mesh

23. Headless BI

24. Metrics then
● BI tools to create and visualize metrics
○ Looker
○ Mode
○ Tableau
○ Data Studio
● Answer internal business questions
○ How is a product's health?
○ What does revenue look like?

25. Metrics now
● Metrics matter for external business workflows
○ Predicting when a customer might churn
○ Notifying users when they reach their capacity limit
○ DS wants to create models to optimize certain metrics
○ Computing customer bills
● BI tools aren’t meant for this
○ Walled garden
○ Have to re-implement the same metrics in different systems

26. “For data consumption, we heard complaints from decision makers that different
teams reported different numbers for very simple business questions, and
there was no easy way to know which number was correct.”
"...the teams that own metrics would be able to define them once, in a way
that’s consistent across dashboards, automation tools, sales reporting, and so
on. Let’s call this ‘Headless BI’."
Headless BI
https://medium.com/airbnb-engineering/how-airbnb-achieved-metric-consistency-at-scale-f23cc53dea70
https://basecase.vc/blog/headless-bi

27. Headless BI
● Programmatically manage business metrics
○ Automated
○ Centralized
○ Documented
○ Validated
○ Metadata/lineage
○ Backfills
○ Cost
○ Privacy
○ Access
○ Deprecation
○ Retention
https://medium.com/airbnb-engineering/how-airbnb-achieved-metric-consistency-at-scale-f23cc53dea70

28. Headless BI
https://medium.com/airbnb-engineering/how-airbnb-achieved-metric-consistency-at-scale-f23cc53dea70

29. Q&A
● Realtime DWHs
● Analytics Engineering
● Data Mesh
● Data Catalogs
● Reverse ETL
● Headless BI
● Data Integrity
● Data Lakehouses
● DataOps
● White-label Data Viz

30. Appendix

31. Analytics Engineering
Analytics engineers provide clean data sets to end users, modeling data in a way
that empowers end users to answer their own questions.
While a data analyst spends their time analyzing data, an analytics engineer
spends their time transforming, testing, deploying, and documenting data.
Analytics engineers apply software engineering best practices like version
control and continuous integration to the analytics code base.
https://www.getdbt.com/what-is-analytics-engineering

32. Analytics Engineering
● Job
○ Building
○ Testing
○ Cataloging
● Tools
○ DBT
○ Airflow
● Customers
○ Data science
○ Data analysts
○ BI
○ Reporting

33. Data Catalogs
● Flavor of the month
○ Amundsen
○ DataHub
○ Metaphor
○ Marquez
○ Atlan
○ Collibra
○ Alation
● Use cases
○ Discoverability
○ Operations
○ Governance

34. “Reverse ETL syncs data from a system of records like a warehouse to a system
of actions like CRM, MAP, and other SaaS apps to operationalize data.”
Reverse ETL
https://blog.getcensus.com/what-is-reverse-etl/

35. Reverse ETL
https://blog.getcensus.com/what-is-reverse-etl/

36. ● https://unsplash.com/photos/Lbvi0GGJWY4
● https://unsplash.com/photos/8vTAAFYhFfQ
● https://www.flickr.com/photos/jurgenappelo/5201275209
●
Photos

37. Realtime DWHs