Hybrid transactional/analytical processing: Difference between revisions

Revision as of 20:33, 6 June 2022

Revision as of 22:08, 6 June 2022

Line 20:

HTAP solves the issue of analytic latency in several ways, including eliminating the need for multiple copies of the same data and the requirement for data to be offloaded from [[operational database]]s to [[data warehouse]]s via [[Extract, transform, load|ETL]] processes.<ref name="Pezzini" /><ref name="Wolpe" />

Most applications of HTAP are enabled by in-memory technologies that can process a high volume of transactions and offer features such as forecasting and simulations. New HTAP technologies use scalable transactional processing, and do not need to rely on keeping the whole database in-memory. For example, smart query selection between a row-based storage engine and a column-based storage engine<ref>{{Cite web |date=2020-06-19 |title=VLDB 2020: TiDB, A Raft-based HTAP Database |url=https://en.pingcap.com/blog/vldb-2020-tidb-a-raft-based-htap-database/ |access-date=2022-06-06 |website=PingCAP |language=en-US}}</ref>. HTAP has the potential to change the way organizations do business by offering immediate business decision-making capabilities based on live and sophisticated analytics of large volumes of data. Government and business leaders can be informed of real-time issues, outcomes, and trends that necessitate action, such as in the areas of public safety, risk management, and fraud detection.<ref name="Pezzini" /><ref name="Baer">Baer, Tony. [http://www.zdnet.com/blog/big-data/fast-data-hits-the-big-data-fast-lane/309 "Fast Data hits the Big Data fast lane."] ZDNet. 16 April 2012</ref>

Some challenges for HTAP include limited industry experience and skills, as well as undefined best practices.<ref name=Pezzini />