April 1st, 2014 — Data management
The latest version of our NoSQL LinkedIn Skills Index shows the continued strength of MongoDB, as the document database increased its share back to 49% of all mentions of NoSQL databases in Q1.
We were surprised to find MongoDB’s proportion of the Index (based on the number of LinkedIn member profiles mentioning each of the NoSQL projects) actually declined in the previous quarter: from 49% to 48%. The Q1 results suggest that was just a blip.
We had wondered whether Couchbase’s leap of two places in our previous update might also be a blip, but in fact Couchbase retained seventh spot in Q1 and there were no changes of position within the top ten this quarter.
Outside the top ten, Titan gained a place on Hypertable, as expected, while RethinkDB leapfrogged AllegroGraph, thanks to recording the third fastest growth (34.94%) in the quarter. The fastest climber, in terms of mentions, was FoundationDB (42.86%), followed by ArangoDB (38.89%). DynamoDB (28.03%) and Titan (26.88%) complete the list of the top five fastest climbers.
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.
December 18th, 2013 — Data management
There’s an early end to the quarter for our NoSQL LinkedIn Skills Index, based on the number of LinkedIn member profiles mentioning each of the NoSQL projects, just as there was in 2012.
We predicted in Q3 that Couchbase would overtake MarkLogic this quarter, which came to pass, but were somewhat surprised to see Couchbase also leapfrog Riak to claim 7th place. It’s almost too close to call between the three, though we wouldn’t be surprised to see those places change hands in the coming quarters.
There were no other changes of position outside the top ten, although Titan is bearing down on Hypertable having recorded the fastest growth in Q4 (49.5%) and can be expected to gain a place in Q1 2014. The second fastest climber, in terms of mentions, was FoundationDB, followed by ArangoDB, RethinkDB and Apache Cassandra (the latter being particularly notable since it was the only one of the five fastest growers to also be one of the top ten most mentioned in LinkedIn member profiles).
That growth was of course not enough to close the gap on MongoDB as the most mentioned NoSQL database in LinkedIn member profiles, although for the first time MongoDB’s proportion of the overall total actually declined – from 49% in Q3 to 48%, upsetting our prediction that MongoDB would pass the 50% threshold in Q4.
It will be interesting to see whether MongoDB’s dominance declines again in Q1, although either way it retains a monumental lead over all the other NoSQL databases in terms of mentions in LinkedIn profiles.
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.
November 14th, 2013 — Data management
Total Data Integration. PostgreSQL on RDS. And more
And that’s the data day, today.
October 1st, 2013 — Data management
With our rebooted NoSQL LinkedIn Skills Index, based on the number of LinkedIn member profiles mentioning each of the NoSQL projects, now into its second year, I thought it was a good time to add some newer projects to the list; specifically: ArangoDB, FoundationDB, RethinkDB, and Titan.
It shouldn’t surprise anyone to find that those four new additions failed to make a dent in the top ten list of the NoSQL databases most often cited in LinkedIn profiles. However, there is still some interesting activity this quarter, with Riak leapfrogging MarkLogic (as predicted).
Outside the top ten, Apache Accumulo overtook Voldemort, and saw the second fastest growth in mentions in Q3, behind only DynamoDB and ahead of Neo4j, MongoDB, and Cassandra.
That growth saw MongoDB extend its lead as the most popular NoSQL database, according to LinkedIn profile mentions. As the chart below illustrates, it now accounts for 49% of all mentions of NoSQL technologies in LinkedIn profiles, according to our sample, compared with 47% in June.
Incidentally, adding the four new NoSQL databases to the analysis did not have a significant impact on MongoDB’s share. Without them it still registered 49%. Expect MongoDB to pass the 50% threshold in Q4, however, as well as Couchbase to overtake MarkLogic.
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.
August 22nd, 2013 — Data management
Hadoop as an engine for data integration. And more.
And that’s the data day, today.
July 23rd, 2013 — Data management
DataStax raises $45m. Actian’s post-acquisition binge strategy. And more
And that’s the data day, today.
July 17th, 2013 — Data management
FoundationDB acquires Akiban. Cloudera acquihires Myrrix. 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.
October 12th, 2012 — Data management
Oracle goes in-memory. Attivio raises $34m. And more.
And that’s the Data Day, today.
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