Lakehouse

Databricks is a leading vendor in the data and AI platform space.

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

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.

Open table formats are a key abstraction layer in the Lakehouse ecosystem, providing table-level metadata management, transactional guarantees and schema evolution capabilities on top of object storage.

HP chose Databricks as the core engine of CustomerLake because its lakehouse architecture can handle diverse customer data such as structured transaction records, semi-structured clickstream logs, and unstructured customer service conversation text.

Before multi-statement transactions reached GA, the version already provided atomicity for a single write to a single table, and GA extends atomicity from a single write to a group of operations.

Cross-statement transactions place higher demands on metadata storage, and the availability of the metadata service will directly become a critical path.

Lakehouse solutions mostly adopt optimistic concurrency control together with atomic commits at the metadata layer.

While multi-statement transactions improve consistency, they may introduce lock contention or conflict retries under high-concurrency write scenarios, thereby affecting throughput.

The Lakehouse concept is to store data in open file formats (such as Parquet, ORC) while layering on data warehouse capabilities such as transactions, schema evolution, and time travel, avoiding the data redundancy and synchronization latency caused by running a data lake and a data warehouse as two parallel systems.

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.

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