HTAP technology uses scalable transaction processing and does not need to rely on keeping the entire database in memory. HTAP has the potential to change the way organizations do business by providing instant business decision-making capabilities based on real-time and complex analysis of large volumes of data. Government and business leaders are informed of real-time issues, findings and trends that require action, such as in the areas of public safety, risk management and fraud detection. [7][11]

Some of the challenges faced by HTAPs include limited industry experience and skills, and undefined best practices. [7]

In 2020, the PingCAP team published the industry's first paper describing the actual implementation of a distributed Hybrid Transactional/Analytical Processing (HTAP) database: TiDB: A Raft-based HTAP Database.

HTAP database supports both workloads in one database, delivering speed and simplicity. And today, "cloud-native HTAP" is a reality; users want an HTAP database that they can mix and match smoothly with Kafka, Spark, and other technologies in the cloud. Use cases include fraud prevention, e-commerce recommendation engines, smart grids, and artificial intelligence.

HTAP databases integrate with, and are designed in part for, streaming data sources such as Kafka and messaging systems for advanced analytics, AI, and machine learning such as Spark. They serve multiple analytics clients, from business analysts entering SQL queries to BI tools, applications, and machine learning models that generate dozens or thousands of queries per second.