October 1st, 2014 — Data management
Time for a new look for our NoSQL LinkedIn Skills Index, which tracks mentions of NoSQL database in LinkedIn member profiles, as it enters its third year. We’ve switched from a bar chart to a line chart to reduce clutter – at least on the horizontal plane.
Unfortunately the dominance of MongoDB means that the chart is inevitably cluttered on the low end of the vertical plane, but the line chart at least provides a clear illustration of that dominance.
There are a few other changes of note further down the list, with FoundationDB gaining a place on Sparksee (as predicted) thanks to it having the fastest rate of growth (40.74%) in Q3. ArangoDB also gained a place on InfiniteGraph thanks to recording the second fastest growth rate (37.84%).
We noted last time that Q3 could see OrientDB overtake Aerospike, unless the release of Aerospike as open source had an immediate impact on interest levels. That seems to have occurred, with Aerospike recording 23.80% growth to not only hold off OrientDB but gain ground on Voldemort, which looks likely to be overtaken by both Aerospike and OrientDB in Q4. Inside the top 10 there is also a chance that DynamoDB could overtake MarkLogic in Q4.
Titan (25.97%), RethinkDB (22.88%) and DynamoDB (22.85%) also deserve a mention in terms of growth in Q3, while Neo4j was the fastest growing of the top 10 with 17.99%. MongoDB was of course most popular NoSQL database by a considerable margin, once again accounting for 49% of all LinkedIn member profiles mentioning a NoSQL project.
Of course, we would also note that this is not meant to be a comprehensive analysis, but rather a snapshot of one particular data source.
July 1st, 2014 — Data management
There isn’t a great deal of movement in the June update to our NoSQL LinkedIn Skills Index, which tracks mentions of NoSQL database in NoSQL member profiles. At the tail-end of the list FoundationDB jumped a place above InfiniteGraph and can be expected to gain another place on Sparksee in the next quarter, but otherwise it’s very much ‘as you were’.
Q3 could also see OrientDB overtake Aerospike, unless the recent release of Aerospike as open source his an immediate impact on interest levels. FoundationDB was among those with the fastest growth rates in Q2 at 35.0%, although the faster growth came from ArangoDB (48.0%) followed by RethinkDB (36.6%), Titan (27.1%) and Couchbase (18.9%).
Once again MongoDB was the most popular NoSQL database by a considerable margin, representing 49% of all LinkedIn member profiles mentioning a NoSQL project.
Of course, we would also note that this is not meant to be a comprehensive analysis, but rather a snapshot of one particular data source.
June 30th, 2014 — Data management
Funding for MapR and Couchbase. And more
And that’s the data day, today.
March 14th, 2014 — Data management
Basho under new leadership. And more
And that’s the data day, today.
November 8th, 2013 — Data management
Garantia Data almost becomes RedisDB, raises $9m. And more
And that’s the data day, today.
August 22nd, 2013 — Data management
Hadoop as an engine for data integration. And more.
And that’s the data day, today.
February 28th, 2013 — Data management
Rackspace buys ObjectRocket. Intel delivers Hadoop distro. And more.
And that’s the data day, today.
February 8th, 2013 — Data management
One of the most complicated aspects of putting together our database landscape map was dealing with the growing number of (particularly NoSQL) databases that refuse to be pigeon-holed in any of the primary databases categories.
I have begun to refer to these as “multi-model databases” in recognition of the fact that they are able to take on the characteristics of multiple databases. In truth though there are probably two different groups of products that could be considered “multi-model”:
True multi-model databases that have been designed specifically to serve multiple data models and use-cases
Examples include:
FoundationDB, which is being designed to support ACID and NoSQL, but more to the point in this instance, multiple layers including key-value, document, and object layers
Aerospike, which is planning to combine SQL, key value, and document and graph database technologies in a single database by bringing together its Citrusleaf NoSQL database with the acquired AlchemyDB NewSQL project
OrientDB, which is, at heart, a document database, but can also be used as a graph database; as an object database, making use of the Java persistence API; and as a hybrid database, taking advantage of multiple models to serve different application requirements
ArangoDB, which promises to deliver the benefits of key value and document and graph stores in a single database
Other products that could be considered true multi-model databases are:
Couchbase Server 2.0, which can be used as both a document store and a key value store, as well as a distributed cache
Riak, which is a key-value store, although it can be used as a document store since the value can be a JSON document
NuoDB, which will provide compatibility with other databases by taking on multiple ‘personalities’ – an Oracle personality via PL/SQL compatibility is in the development roadmap, as is a document store personality via JSON support.
General-purpose databases with multi-model options
What’s the difference between multi-model databases and existing general-purpose databases that have optional capabilities for serving multiple models? My book book it’s about being designed for purpose, but I’m sure that will be a debating point for the future. In the mean-time, examples include:
Oracle MySQL 5.6, which can support both SQL-based access and key-value access via the Memcached API.
Oracle MySQL Cluster 7.2, which similarly supports concurrent NoSQL and SQL access to the database.
IBM DB2 10, which extends DB2’s hybrid relational and XML engine to enable the storage and management of graph triples, as well as support for the SPARQL 1.0 query language.
Akiban Server, which has the ability to treat groups of tables as objects and access them as JSON documents via SQL.
PostgreSQL h-store, which can be used for storing key-value pairs within a PostgreSQL data field, thereby enabling schema-less queries against data stored in PostgreSQL
We are also aware of other NewSQL database that plan to adopt support for popular NoSQL data models, while IBM has also talked about plans to integrate key value store NoSQL access capabilities with DB2 and Informix database software.
Other products that could be considered multi-model options include:
Oracle Spatial and Graph, an option for Oracle Database 11g.
One of the drivers of NoSQL database adoption has been polyglot persistence – using multiple databases depending on the specific requirements of individual applications. Multi-model databases contradict this trend, to some extent, so it will be interesting to see whether they begin to gain traction.
While we see the wisdom of selecting the best database for the job, we also recognise that it could sometimes be a matter of choosing the best data model for the job, while relying on a single storage back-end.
August 28th, 2012 — Data management
Citrusleaf. Aerospike. AlchemyDB. Sqrrl. Percolator. Dremel. Pregel. And more.
And that’s the Data Day, today.
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