In search of database nirvana: The challenges of delivering HTAP

Companies are looking for a single database engine that can address all their varied needsfrom transactional to analytical workloads, against structured, semistructured, and unstructured data, leveraging graph databases, document stores, text search engines, column stores, key value stores, and wide column stores. Rohit Jain discusses the challenges one faces on the path to this nirvana.

Companies are looking for a single database engine that can address all their varied needs—from transactional to analytical workloads, against structured, semistructured, and unstructured data, leveraging graph databases, document stores, text search engines, column stores, key value stores, and wide column stores. They are looking for the ultimate database nirvana. The term hybrid transactional/analytical processing (HTAP), coined by Gartner, perhaps comes closest to describing this concept. (451 Research uses the terms convergence or converged data platform. The terms multimodel or unified are also used.) But can such a nirvana be achieved? Rohit Jain discusses the challenges one faces on the path to this nirvana, including: Attendees looking to assess query and storage engines would benefit from understanding what the key considerations are when picking an engine to run their targeted workloads. Also, developers working on such engines can better understand capabilities they need to provide in order to run workloads that span the HTAP spectrum.