Databricks lakehouse on Google Cloud

Challenges with Data Lake and Data Warehouse:

Data warehouses, lack support for audio/video/text data and these data is being generated and used more and more for Machine Learning and Data Science purpose. To properly execute this type of analytics, data lakes are often used but Data Lakes offer limited BI support and are more complex as compare to data warehouses. To overcome these issues Databricks brings to enable ACID properties on top of Data Lake. The Databricks lakehouse promises the structured, BI and reporting advantages of data warehouses together with the data science, machine learning and artificial intelligence (AI) benefits of data lakes.

Databricks Data Lakehouse and Advantages:

A data lakehouse is a open data management unified analytics platform that combines the capabilities of data lakes and data warehouses, enabling BI and ML on all data then merging them into a single system means that data teams can move faster as they can use data without accessing multiple systems. The main advantages of Data Lakehouse includes Elimination of simple ETL jobs, Reduced Data Redundancy, Data Versioning, Ease of Data Governance, Directly connect to BI tools, Reducing support cost and overall total cost of ownership.

Google BigQuery Lakehouse Architecture:

Users can Build & Deploy "Data lakehouse" on using various PaaS Services like BigQuery, Dataflow, Cloud Datalab, GKE Services and unlock AI-driven insights, enable intelligent decision-making, and ultimately accelerate their digital transformations through data-driven applications. There are pre-built connectors available for integrating Databricks with BigQuery, Google Cloud Storage, Looker, Pub/Sub which makes it easier to extend "AI-driven insights" across data lakes, data warehouses, and multiple business intelligence tools.