Lakehouse

Databricks implemented the data Lakehouse architecture through its open-source project Delta Lake.

Lakebase is a database service launched by Databricks for AI application scenarios, representing transactional database capabilities built on top of the Lakehouse architecture.

The Lakehouse architecture was proposed by Databricks in 2020, aiming to combine the flexibility of data lakes with the transactional capabilities of data warehouses.

Lakebase is a new type of database form proposed within the Databricks ecosystem, whose core concept is to build Postgres-compatible transactional database capabilities on top of a data lakehouse architecture.

Lakehouse resolves the contradiction between the lack of ACID transactions in data lakes and vendor lock-in by data warehouse vendors by building a transactional metadata layer (such as Delta Lake, Apache Iceberg, and Apache Hudi) on top of object storage.

Lakehouse is a converged architecture proposed by Databricks that simultaneously supports transactional processing and analytical queries on the same storage layer, with its underlying ACID transaction capabilities implemented via the Delta Lake format.

The differentiation of Databricks Document Intelligence lies in its deep coupling with the Lakehouse architecture and its positioning toward the agent era.

The core idea of Lakehouse is to layer data warehouse-level transaction support, schema management, and query performance optimization capabilities on top of an open data lake storage layer.

Existing Lakehouses excel at analytical (OLAP) queries, while many AI applications also require high-frequency read/write online transaction processing (OLTP) capabilities.

The lakehouse architecture is based on open storage formats (such as Parquet) and open table formats (such as Delta Lake, Iceberg, and Hudi), providing capabilities such as transactions, version management, and schema evolution on top of low-cost object storage.

Moving data out of the lakehouse to an external vector service and back increases engineering complexity and undermines data governance and consistency.

Databricks' Delta Lake and Apache Spark runtime are typical implementations of the lakehouse architecture.

The greatest challenge in migrating dimensional modeling to a Lakehouse architecture lies in the fact that traditional dimensional modeling relies on strict ETL processes and relational database constraints, while the open formats of Lakehouse require a new balance to be struck between schema flexibility and analytical performance.

In traditional architectures, OLTP databases and data lakehouses are separate, requiring data to flow through ETL pipelines, which introduces latency and consistency issues.

LTAP is an extension and upgrade of the traditional HTAP concept, with the difference being that it places this capability within the context of a lakehouse architecture.

IP Functions allow network analysis to be performed directly within the Lakehouse platform using standard SQL, without the need to migrate data across systems.

The lakehouse architecture combines a data lake in open formats (such as Parquet and Iceberg) with the transactional and query capabilities of a database, allowing the same data to be accessed by both the Postgres engine and analytical engines such as Spark and Trino.

Genie Ontology is a semantic layer product launched by Databricks within its Lakehouse architecture, allowing users to ask questions in natural language and receive semantically validated data answers.

The Lakehouse architecture aims to combine the low cost and high flexibility of a data lake with the structured query capabilities of a data warehouse.

The Lakehouse concept was proposed by the Databricks co-founder team, with ACID transaction support implemented at the underlying level via the Delta Lake open format.