Hybrid transactional/analytical processing: Difference between revisions

Revision as of 22:16, 17 February 2021

Revision as of 00:12, 1 March 2021

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However, Gartner's most recent reports suggest broader advantages than a single unified database can offer. Traditional application architectures separated transactional and analytical systems. Digital business, and the need to respond to business moments, means that using "after the fact" analysis is no longer adequate. Business moments are transient opportunities that must be exploited in real time. If an organization is unable to recognize and/or respond quickly to a business moment by taking fast and well-informed decisions, then some other organization will, resulting in a missed opportunity (or a new business threat). HTAP allows advanced analytics to be run in real time on "in flight" transaction data, providing an architecture that empowers users to respond more effectively to business moments.<ref>{{Cite web|url=https://www.gartner.com/doc/3352419/enable-digital-business-innovation-hybrid|title=How to Enable Digital Business Innovation via Hybrid Transaction/Analytical Processing|website=www.gartner.com|access-date=2017-04-15}}</ref>

The main technical challenges for an HTAP database are how to be efficient both for operational (many small transactions with a high fraction of updates) and analytical workloads (large and complex queries traversing large number of rows) on the same database system and how to prevent the interference of the analytical queries over the operational workload. This kind of operational workload is also commonly referred to as [[Operational analytical processing|Operational Analytical Processing]].

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" />

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

HTAP functionality is offered by database companies, such as [[Microsoft Azure]] Synapse Link<ref>{{Cite web|url=https://www.zdnet.com/article/a-closer-look-at-azure-synapse-link/|title=A closer look at Azure Synapse Link|website=www.zdnet.com|access-date=2017-04-15}}</ref> for [[Cosmos DB]], DbAlibaba DRDS, LeanXcale,<ref>{{Cite web|url=https://www.bloorresearch.com/research/hybrid-real-time-data-processing/|title=Hybrid real-time data processing|last=Research.|first=Bloor|website=bloorresearch.com|language=en-us|access-date=2019-10-30}}</ref> [[TiDB]],<ref>{{Cite news|url=https://www.datanami.com/2018/02/22/hybrid-database-capturing-perishable-insights-yiguo/|title=The Hybrid Database Capturing Perishable Insights at Yiguo|date=2018-02-22|work=Datanami|access-date=2018-03-02|language=en-US}}</ref><ref>{{Cite news|url=https://www.infoworld.com/article/3313327/database/how-tidb-combines-oltp-and-olap-in-a-distributed-database.html|title=How TiDB combines OLTP and OLAP in a distributed database|last=Xu|first=Kevin|work=InfoWorld|access-date=2018-11-14|language=en}}</ref> Hubble, [[ArangoDB]], [[Aerospike (database)|Aerospike]], [[GridGain Systems|Apache Ignite/GridGain In-Memory Data Fabric]], [[IBM]] [[IBM_Db2]] IDAA,<ref> [https://www-01.ibm.com/events/wwe/grp/grp004.nsf/vLookupPDFs/02%20-%20Dan%20Wardman%20%20-%20Real%20Time%20Analytics%20with%20IDAA%20and%20Big%20Data%20on%20System%20z/$file/02%20-%20Dan%20Wardman%20%20-%20Real%20Time%20Analytics%20with%20IDAA%20and%20Big%20Data%20on%20System%20z.pdf "Real Time Analytics with IDAA and Big Data on System z"]</ref> [[InterSystems]],<ref>{{Cite web|url=https://www.gartner.com/reviews/market/operational-dbms|title=Operational Database Management Systems (ODBMS) Software Reviews|last=Inc.|first=Gartner|website=Gartner|language=en-us|access-date=2018-02-14}}</ref><ref>{{Cite journal|last=Gartner|date=12 Feb 2018|title=Critical Capabilities for Operational Database Management Systems|url=https://www.gartner.com/doc/3855663?ref=SiteSearch&sthkw=Critical%20Capabilities%20for%20Operational%20Database%20Management%20Systems&fnl=search&srcId=1-3478922254|journal=Gartner}}</ref> [[Kdb+]], [[Microsoft SQL Server]], [[Neo4j]], [[TigerGraph]], [[Oracle 12c In-Memory]],<ref name="oracle.com 2018">{{cite web |title=Leading-edge Database technology now available in all environments |website=oracle.com |url=https://www.oracle.com/corporate/pressrelease/oracle-database-avail-everywhere-030617.html |archive-url=https://web.archive.org/web/20180828115603/https://www.oracle.com/corporate/pressrelease/oracle-database-avail-everywhere-030617.html |archive-date=2018-08-28 |url-status=dead}}</ref> [[SAP HANA]],<ref>{{Cite web|url = http://www.cioreview.com/cxoinsight/internet-of-everything-and-hybrid-transactional-analytical-processing--nid-12929-cid-49.html|title = Internet of Everything and Hybrid Transactional Analytical Processing|last = Review|first = CIO|website = CIOReview|access-date = 2016-03-26}}</ref><ref>{{Cite web|url = https://www.gartner.com/doc/reprints?id=1-2OW5H99&ct=151005&st=sb|title = Gartner Reprint|website = www.gartner.com|access-date = 2016-03-26}}</ref> [[MemSQL]], [[MongoDB]], [[VoltDB]], [[NuoDB]], [[OrientDB]], [[DataStax]], [[eXtremeDB]], [[Splice Machine]],<ref>{{cite web |title=The Splice Machine Data Platform |url=https://splicemachine.com/product/data-platform/ |website=Splice Machine}}</ref> [[EsgynDB]], [[Spanner (database)|Cloud Spanner]], HarperDB, [[Amazon Aurora]] (Parallel Query), BlobCity, [[Couchbase]],<ref>https://www.datanami.com/2018/09/20/couchbase-to-deliver-parallel-json-analytics-without-the-etl/</ref> [[YugabyteDB]]<ref>https://docs.yugabyte.com/latest/faq/general/</ref> and Postgres.

==References==