Lakehouse: Difference between revisions
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=Related Concepts= | =Related Concepts= | ||
[[Data Warehouse]]. Schema-on-write. Business Intelligence (BI). Unstructured data. [[Data Lake]]. Schema-on-read. ETL, ELT, [[Machine Learning]], data management, zero-copy cloning. DataFrame, data pipeline, batch job, streaming pipeline, SQL engines: Spark SQL, [[ | [[Data Warehouse]]. Schema-on-write. Business Intelligence (BI). Unstructured data. [[Data Lake]]. Schema-on-read. ETL, ELT, [[Machine Learning]], data management, zero-copy cloning. DataFrame, data pipeline, batch job, streaming pipeline, SQL engines: Spark SQL, [[PrestoDB]], [[Hive]], AWS Athena. | ||
=Implementations= | =Implementations= | ||
* [[Apache Iceberg]] | * [[Apache Iceberg]] | ||
Revision as of 21:14, 1 May 2023
External
- https://www.cidrdb.org/cidr2021/papers/cidr2021_paper17.pdf Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics by Michael Armbrust, Ali Ghodsi, Reynold Xin, Matei Zaharia
Internal
Overview
An architectural pattern used to implement access to data that is based on open direct-access data formats (such as Apache Parquet and ORC), has support for machine learning and data science and offers state-of-the-art performance. It is based on the concept of Data Lake.
Related Concepts
Data Warehouse. Schema-on-write. Business Intelligence (BI). Unstructured data. Data Lake. Schema-on-read. ETL, ELT, Machine Learning, data management, zero-copy cloning. DataFrame, data pipeline, batch job, streaming pipeline, SQL engines: Spark SQL, PrestoDB, Hive, AWS Athena.