![]() While Snowflake's marketing has not run with the lakehouse terminology, preferring the term 'data cloud' to describe its ability to support multiple data processing and analytics workloads, AWS has very much picked up on it as a term to describe its combined portfolio of data and analytics services – placing its 'lake house architecture' front and center of its data and analytics announcements at re:Invent 2020. ![]() In fact, the first use of the term by a vendor we have found can be attributed to Snowflake, which in late 2017 promoted that its customer, Jellyvision, was using Snowflake to combine schemaless and structured data processing in what Jellyvision described as a data lakehouse. Amazon Web Services (AWS) previously used the term (or in its case, 'lake house') in late 2019 in relation to Amazon Redshift Spectrum, its service that enables users of its Amazon Redshift data warehouse service to apply queries to data stored in its Amazon S3 cloud service. Often uttered flippantly to describe the result of the theoretical combination of a data warehouse with data lake functionality, usage of the term became more serious and more widespread in early 2020 as Databricks adopted it to describe its approach of marrying the data structure and data management features of the data warehouse with the low-cost storage used for data lakes.ĭatabricks was not the first to start using the data lakehouse terminology, however. ![]() ![]() The term 'data lakehouse' entered the data and analytics lexicon over the last few years. ![]()
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