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, use smart query selection between row-based storage or column-based storage engines<ref name="PingCAP" /><ref name="TiDB">PingCAP. [https://en.pingcap.com/blog/vldb-2020-tidb-a-raft-based-htap-database/ "VLDB 2020: TiDB, A Raft-based HTAP Database"]. 19 June 2020</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 />
