Evaluating HTAP Databases for Machine Learning Applications

Evaluating HTAP Databases for Machine Learning Applications

Evaluating HTAP Databases for Machine Learning Applications
Businesses are producing a greater number of intelligent applications; which traditional databases are unable to support. A new class of databases, Hybrid Transactional and Analytical Processing (HTAP) databases, offers a variety of capabilities with specific strengths and weaknesses to consider. This article aims to give application developers and data scientists a better understanding of the HTAP database ecosystem so they can make the right choice for their intelligent application.

HTAP is a term coined by The Gartner Group to describe a class of database systems that are Hybrid Transactional and Analytical Processing Systems. These systems exhibit the transactional capabilities of operational databases such as Oracle, SQL Server, DB2, MySQL, and Postgres as well as the capabilities of analytical systems such as Teradata, Netezza, and Vertica.

The power of HTAP is to serve a new class of applications that are intelligent –  applications that analyze real-time data to provide the most timely insights possible. Previous generations of databases could not service such applications because the data had to move from operational systems to analytical systems via an ETL (Extraction, Transformation, and Load) process that typically takes many hours. It could even take s days before an analytical system would be able to process the data. This inherent lag caused by ETL required companies, governments, and academic researchers to look through the rear-view mirror of their operation instead of looking forward through the windshield.

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HTAP enables new technical capabilities such as machine learning, real-time operational reporting and dashboarding, and reactive systems. These capabilities serve as the foundation for a new class of intelligent applications.

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