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The following tables compare general and technical information for a number of available database administration tools. Please see individual product articles for further information.
BigQuery is a managed, serverless data warehouse product by Google, offering scalable analysis over large quantities of data. It is a Platform as a Service that supports querying using a dialect of SQL. It also has built-in machine learning capabilities. BigQuery was announced in May 2010 and made generally available in November 2011. [1]
Amazon Redshift; Microsoft Azure Synapse Analytics (formerly Azure SQL Data Warehouse) Google BigQuery; Oracle Autonomous Data Warehouse Cloud (ADWC) Snowflake Computing;
Amazon Redshift is a data warehouse product which forms part of the larger cloud-computing platform Amazon Web Services. [1] It is built on top of technology from the massive parallel processing (MPP) data warehouse company ParAccel (later acquired by Actian ), [ 2 ] to handle large scale data sets and database migrations .
The product was the first commercially available business intelligence platform built for and aimed at scalable or massively parallel relational database management systems like Amazon Redshift, Google BigQuery, HP Vertica, Netezza, and Teradata.
Dremel is the query engine used in Google's BigQuery service. [1] Dremel is the inspiration for Apache Drill, [2] Apache Impala, [3] and Dremio, [4] an Apache licensed platform that includes a distributed SQL execution engine. In 2020, Dremel won the Test of Time award [5] at the VLDB 2020 conference, recognizing the innovations it pioneered. [6]
Cloud-based data warehouses like Amazon Redshift, Google BigQuery, Microsoft Azure Synapse Analytics and Snowflake Inc. have been able to provide highly scalable computing power. This lets businesses forgo preload transformations and replicate raw data into their data warehouses, where it can transform them as needed using SQL .
Extract, load, transform (ELT) is an alternative to extract, transform, load (ETL) used with data lake implementations. In contrast to ETL, in ELT models the data is not transformed on entry to the data lake, but stored in its original raw format.