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Lakehouse
A lakehouse is a new data architecture that combines the flexibility and low cost of a data lake with the structure and management features of a data warehouse.
TECHNICAL DEFINITION
A lakehouse is a modern data architecture that integrates the best features of data lakes (raw data storage, flexibility) and data warehouses (structured data, ACID transactions, schema enforcement), enabling unified data management for analytics and machine learning workloads.
BACKGROUND
Hitachi, Ltd. is a Japanese multinational conglomerate founded in 1910 and headquartered in Chiyoda, Tokyo. The company is active in various industries, including digital systems, power and renewable energy, railway systems, healthcare products, and financial systems. The company was founded as an electrical machinery manufacturing subsidiary of the Kuhara Mining Plant in Hitachi, Ibaraki, by engineer Namihei Odaira in 1910. It began operating as an independent company under its current name in 1920.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Data lake + warehouse
- Hybrid data architecture
- Unified data platform
- Delta Lake architecture
USAGE NOTE
Lakehouses are gaining popularity for their ability to handle diverse data types and support both traditional BI and advanced ML analytics.
DEVELOPERS
Organizations developing technology related to Lakehouse.
Pioneered the Lakehouse architecture, offering a unified platform for data engineering, machine learning, and data analytics. Their platform is foundational for AI engineering, providing the infrastructure to prepare data for model training, feature engineering, and MLOps.
Through Azure Synapse Analytics, Microsoft offers a comprehensive analytics service that integrates data warehousing, big data analytics, and data integration capabilities in a lakehouse pattern. This supports end-to-end AI engineering workflows, from data ingestion to model deployment.
Provides a suite of services like S3 (object storage), AWS Lake Formation (data lake governance), AWS Glue (ETL and cataloging), and Amazon Athena (interactive query service) that enable customers to build and manage robust lakehouse architectures crucial for AI/ML data pipelines.
Offers services such as BigQuery (serverless data warehouse), Cloud Storage (object storage), and Dataproc (managed Apache Hadoop and Spark) that collectively support lakehouse patterns. These are essential for scalable data processing and analytics underpinning AI development and prompt engineering data management.
While known for its data warehouse, Snowflake has significantly expanded its capabilities to support unstructured data, data science workloads, and external tables, evolving into a 'data cloud' that functions with strong lakehouse characteristics. This enables data scientists and ML engineers to access and process diverse data types for AI models.
Offers an open data lakehouse platform that provides a SQL query engine directly on data lakes, accelerating data access and empowering data teams to work with data in a lakehouse pattern. This is critical for feature engineering and timely data access for AI/ML applications.
Based on Trino (formerly PrestoSQL), Starburst provides an open data lake analytics platform that allows querying data across various sources, including data lakes, without moving it. This facilitates data preparation and integration for complex AI engineering projects and data used in prompt generation.
Delivers an enterprise data cloud that supports lakehouse architectures across hybrid and multi-cloud environments. Its platform is designed for advanced analytics and AI/ML applications, providing data management and processing capabilities vital for large-scale AI engineering.