Building a Data Lakehouse with GCP: Lessons Learned and Architecture

As I embarked on building Looker dashboards on Delta Lake, I realized that a data warehouse is not necessary to achieve success. In fact, a lakehouse can serve as the single source of truth for all enterprise data products. By implementing the architecture outlined in this tutorial, you can ensure atomicity, consistency, isolation, and durability of your data without copying it between your data lake and other systems.

In this article, I will share my lessons learned while building Looker dashboards on Delta Lake, a reliable, performant, and secure data storage and management layer for your data lake. Delta Lake supports both streaming and batch operations, making it an ideal choice for big data analytics.

The medallion architecture, which describes a series of data layers that denote the quality of data stored in the lakehouse, is particularly noteworthy. This approach ensures that your data is accurate, reliable, and easily accessible. With Databricks on GCP, you can now directly build Looker dashboards on a lakehouse, eliminating the need for data copying or transformation.

For those with a background in business intelligence (BI) or analytics, the combination of Google's Looker and Databricks lakehouse is particularly interesting. This integration enables you to use your data science notebooks, optimized Apache Spark engine, SQL Analytics, and Delta Lake with its open formats on all major clouds. You can now do your analytics where your data is located, eliminating the need for data copying or migration.

Databricks also tightly integrates with GCP cloud services, making it an ideal choice for enterprises looking to leverage the full potential of their data. With Databricks lakehouse architecture available on GCP, you can now build a single source of truth for all your enterprise data products without the need for complex data warehousing solutions.

In conclusion, building a data lakehouse with GCP using Databricks and Looker is a powerful approach to big data analytics that eliminates the need for data copying or transformation. By implementing this architecture, you can ensure atomicity, consistency, isolation, and durability of your data while also leveraging the full potential of your data science notebooks, optimized Apache Spark engine, SQL Analytics, and Delta Lake with its open formats on all major clouds.

Gcp data lakehouse