{"id":4272,"date":"2016-11-30T20:30:20","date_gmt":"2016-11-30T20:30:20","guid":{"rendered":"http:\/\/www.odbms.org\/blog\/?p=4272"},"modified":"2016-11-30T20:30:20","modified_gmt":"2016-11-30T20:30:20","slug":"new-gartner-magic-quadrant-for-operational-database-management-systems-interview-with-nick-heudecker","status":"publish","type":"post","link":"https:\/\/www.odbms.org\/blog\/2016\/11\/new-gartner-magic-quadrant-for-operational-database-management-systems-interview-with-nick-heudecker\/","title":{"rendered":"New Gartner Magic Quadrant for Operational Database Management Systems. Interview with Nick Heudecker"},"content":{"rendered":"

“It is too soon to call the operational DBMS market a commodity market, but it\u2019s easy to see a future where that is the case.”–Nick Heudecker. <\/strong><\/p><\/blockquote>\n

I have interviewed Nick Heudecker<\/strong>, Research Director on Gartner\u2019s Data & Analytics team.
\nThe main topic of the interview is the new Magic Quadrant for Operational Database Management Systems.<\/p>\n

RVZ<\/p>\n

Q1. You have published the new Magic Quadrant for Operational Database Management Systems<\/a> (*). How do you define the operational database management system market?<\/strong><\/p>\n

Nick Heudecker<\/strong>: We define a DBMS as a complete software system used to define, create, manage, update and query a database. DBMSs provide interfaces to independent programs and tools that both support and govern the performance of a variety of concurrent workload types. There is no presupposition that DBMSs must support the relational model or that they must support the full set of possible data types in use today. OPDBMSs must include functionality to support backup<\/a> and recovery<\/a>, and have some form of transaction durability<\/a> \u2014 although the atomicity, consistency, isolation and durability model is not a requirement. OPDBMSs may support multiple delivery models, such as stand-alone<\/a> DBMS software, certified configurations, cloud (public and private) images or versions, and database appliances.<\/p>\n

Q2. Can you explain the methodology you used for this new Magic Quadrant? <\/strong><\/p>\n

Nick Heudecker<\/strong>: The methodologies for several Gartner methodologies are public. The Magic Quadrant methodology can be found here. <\/a><\/p>\n

We use a number of data sources when we\u2019re creating the Magic Quadrant for Operational Database Management Systems.
\nWe survey vendor reference customers and include data from our interactions with Gartner clients. We also consider earlier information and any news about vendors’ products, customers and finances that came to light during the time frame for our analysis.<\/p>\n

Once we have the data, we score vendors across the various dimensions of Completeness of Vision<\/em> and Ability to Execute<\/em>.
\nOne thing that\u2019s important to note is Magic Quadrants are relative assessments of vendors in a market. We couldn\u2019t have one vendor on an MQ because it would be right in the middle – there\u2019s nothing to compare it to.<\/p>\n

Q3. Why were there no Visionaries this year?<\/strong><\/p>\n

Nick Heudecker<\/strong>: We determined there was an overall lack of vision in the market. After a few years of rapid feature expansion, the focus has shifted to operational excellence and execution. Even Leaders shifted to the left on vision, but are still placed in the Leaders quadrant based on their vision for the development of hybrid database management, hardware optimization and integration, emerging deployment models such as containerization, as well as vertical features.<\/p>\n

Q4. Were you surprised by the analysis and some of the results you obtained? <\/strong><\/p>\n

Nick Heudecker<\/strong>: The lack of overall vision in the market struck us the most. Other than in a few notable cases, we received largely the same story from most vendors. The explosion of features, and the vendors emerging to implement them, has slowed. The features that initiated the expansion, such as storing new data types, geographically distributed storage, cloud and flexible data consistency models, have become common. Today, nearly every established or emerging DBMS vendor supports these features to some degree. The OPDBMS market has shifted from a phase of rapid innovation to a phase of maturing products and capabilities.<\/p>\n

Q5. Do you believe the “NoSQL” label will continue to distinguish DBMSs?<\/strong><\/p>\n

Nick Heudecker<\/strong>: If you look at the entire operational DBMS space, there\u2019s already a great deal of convergence between NoSQL<\/a> vendors, as well as between NoSQL and traditionally relational vendors. Nearly every vendor, nonrelational and relational, supports multiple data types, like JSON<\/a> documents, graph or wide-column. NoSQL vendors are adding SQL: MongoDB\u2019s BI Connector<\/a> and Couchbase\u2019s N1QL<\/a> are good, if diverse, examples. They\u2019re also adding things like schema management and data validation capabilities.
\nOn the relational side, they\u2019re adding horizontal scaling options and alternative consistency models, as well as modern APIs. And everyone either has or is adding in-memory<\/a> and cloud<\/a> capabilities.<\/p>\n

It is too soon to call the operational DBMS market a commodity market, but it\u2019s easy to see a future where that is the case.<\/p>\n

Q6. What are the other “Vendors to Consider\u201d?<\/strong><\/p>\n

Nick Heudecker<\/strong>: The other vendors to consider are vendors that did not meet the inclusion requirements for the Magic Quadrant. Usually this is because they missed our minimum revenue requirements, but that doesn\u2019t mean they don\u2019t have compelling products.<\/p>\n

——————————-
\nNick Heudecker<\/strong> is a Research Director on Gartner\u2019s Data & Analytics team. His coverage includes data management technologies and practices. <\/em><\/p>\n

——————————-<\/p>\n

Resources<\/strong>
\n(*) Magic Quadrant for Operational Database Management Systems. Published: 05 October 2016 ID: G00293203Analyst(s): Nick Heudecker, Donald Feinberg, Merv Adrian, Terilyn Palanca, Rick Greenwald<\/a><\/p>\n

–\u00a0Complimentary Gartner Research:\u00a0100 Data and Analytics\u00a0Predictions Through 2020.<\/strong>\u00a0Get exclusive access to Gartner\u2019s top 100 data and analytics predictions through 2020. Plus access other relevant Gartner research including Magic Quadrant reports for database and data warehouse solutions, and the market guide for in-memory computing (LINK to MemSQL web site – registration required<\/a>).<\/em><\/p>\n

Related Posts<\/strong><\/p>\n

– MarkLogic Named a Next-Generation Database Challenger in 2016 Gartner Magic Quadrant.\u00a0By GARY BLOOM, Chief Executive Officer and President MARKLOGIC<\/a><\/p>\n

– MarkLogic Recognized in New Gartner\u00ae Magic Quadrant. Gartner Magic Quadrant for Operational Database Management Systems positions MarkLogic\u00ae the highest for ability to execute in the Challengers Quadrant<\/a><\/p>\n

–\u00a0Accelerating Business Value with a Multi-Model, Multi-Workload Data Platform<\/a><\/p>\n

–\u00a0NuoDB Recognized by Gartner in Critical Capabilities for Operational Database Management Systems. Elastic SQL database achieves top five score in all four use cases.<\/a><\/p>\n

–\u00a0Clustrix Recognized in Gartner Magic Quadrant for Operational Database Management Systems<\/a><\/p>\n

–\u00a0Learn why EDB is named a \u201cChallenger\u201d in the 2016 Gartner ODBMS Magic Quadrant<\/a><\/p>\n

–\u00a0DataStax Receives Highest Scores in 2 Use Cases in Gartner\u2019s Critical Capabilities for Operational Database Management Systems<\/a><\/p>\n

–\u00a0Gartner Scores Oracle Highest In 3 of 4 Use Cases: Gartner Critical Capabilities for Operational Database Management Systems Report<\/a><\/p>\n

– Gartner Critical Capabilities For Operational Database Management Systems 2016 – Redis Labs Ranked Second Highest In 2\/4 Categories (Link- Registation required)<\/a><\/p>\n

 <\/p>\n

Follow us on Twitter: @odbmsorg<\/a><\/strong><\/p>\n

##<\/p>\n","protected":false},"excerpt":{"rendered":"

“It is too soon to call the operational DBMS market a commodity market, but it\u2019s easy to see a future where that is the case.”–Nick Heudecker. I have interviewed Nick Heudecker, Research Director on Gartner\u2019s Data & Analytics team. The main topic of the interview is the new Magic Quadrant for Operational Database Management Systems. […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[7,29,59,106,115,142,178,1037,210,224,263,288,1034,354,358,788,383,391,791,409,415,449,1036,1035],"_links":{"self":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/4272"}],"collection":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/comments?post=4272"}],"version-history":[{"count":16,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/4272\/revisions"}],"predecessor-version":[{"id":4296,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/posts\/4272\/revisions\/4296"}],"wp:attachment":[{"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/media?parent=4272"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/categories?post=4272"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.odbms.org\/blog\/wp-json\/wp\/v2\/tags?post=4272"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}