AiVibeData Lakehouse
A data lakehouse combines the low-cost, flexible storage of a data lake with warehouse features such as ACID transactions, schema enforcement and fast SQL, using open table formats like Delta Lake, Apache Iceberg or Apache Hudi.
A data lakehouse is a data architecture that adds data warehouse capabilities, such as ACID transactions, schema enforcement and fast SQL queries, directly on top of data lake storage. It relies on open table formats, chiefly Delta Lake, Apache Iceberg and Apache Hudi, which add a metadata and transaction layer over Parquet files in object storage. Multiple processing engines can then read and write the same tables consistently.
The lakehouse aims to replace the common two-tier pattern of a data lake feeding a separate warehouse, reducing data copies, pipeline complexity and cost. One platform can serve business intelligence dashboards, data science and machine-learning training from the same governed data. Time travel, which queries a table as it was at an earlier point, helps with audits and with reproducing model training data. Databricks popularised the term, and several cloud data platforms now support open table formats.
Performance depends on file sizing, compaction, partitioning and statistics maintenance, which must be managed explicitly or by the platform. Governance needs a catalogue that controls access consistently across engines. The choice of table format affects which tools can be used, although interoperability between formats is improving. For small datasets, a conventional database or warehouse is often simpler.
Key points
- Adds transactions and schema enforcement to files stored in object storage
- Built on open table formats such as Delta Lake, Apache Iceberg and Apache Hudi
- Serves BI, data science and machine learning from one copy of the data
- Time travel lets users query tables as they were at an earlier point
Where AiVibe comes in
AiVibe Software Services delivers cloud solutions on AWS, Microsoft Azure, Google Cloud or on-premise, together with cloud security, legacy modernisation, data analytics and AI and machine learning services.