The Data Day: February 10, 2017

SEE YOU IN COURT, THE FUTURE OF DATA AND ANALYTICS AT STAKE!

And that’s the data day, today.

NoSQL LinkedIn Skills Index – An Interesting Occasional Update

I was recently prompted by OrientDB CEO Luca Garulli to take another look at the NoSQL LinkedIn Skills Index, which we previously updated on a regular basis between September 2012 and 2015.

I wouldn’t read too much into the results since there’s been such a long period between updates, and this is – as ever – just a snapshot of one particular data source. However, they are definitely interesting, especially when you consider that we retired the NoSQL LinkedIn Skills Index primarily because the results had become so boringly predictable.

As such I’d make the following observations without any additional comment:

  • It is interesting to note that MongoDB’s share of mentions of NoSQL databases in LinkedIn member profiles has declined since September 2015, from 51% to 48%. Of course, MongoDB remains the number one by a considerable margin.
  • It is also interesting to note that Redis has climbed above Cassandra to claim second spot.
  • Similarly it is interesting that Neo4j has climbed above CouchDB for fifth place.
  • And it is also interesting that DynamoDB has overtaken Couchbase for eighth place.
  • It is also interesting that the two fastest growing NoSQL databases, in terms of mentions in LinkedIn profiles, are Google Cloud Bigtable (up 557%) and Azure DocumentDB (up 254%).
  • And it is also interesting that the third fastest growth came from RethinkDB, despite the recent demise of the company of the same name.
  • Those growth rates saw Google Clooud Bigtable climb above Voldemort, ArangoDB, Hypertable and Allegrograph, while Azure DocumentDB climbed above Titan and Voldemort, and RethinkDB climbed above Titan and Accumulo.

Since Luca prompted another look at the results, I should also probably point out that mentions of OrientDB grew at a healthy 83% as OrientDB held on to 11th place in the Index.

Interesting…

The Data Day: September 9, 2016

What happened in data platforms and analytics this week will leave you speechless

And that’s the data day, today.

The Data Day: June 24, 2016

You’ll never believe what happened in data and analytics this week

And that’s the data day, today.

The Data Day, A few days: January 30-February 8, 2016

Investment funding for Hadoop and NoSQL in 2015. And more.

And that’s the data day, today.

The Data Day, A few days: January 16-22, 2016

Funding for Qubole, ScaleArc, GigaSpaces, MariaDB. And more.

And that’s the data day, today.

NoSQL LinkedIn Skills Index – September 2015

Three years after we (re)started tracking mentions of NoSQL database in LinkedIn member profiles it is time to retire the NoSQL LinkedIn Skills Index – at least in terms of regular updates.

We started tracking mentions of NoSQL database in LinkedIn member profiles in order to keep an eye on trends that could shape the industry, but after three years it has become clear that in terms of LinkedIn member profiles there is only one trend: the total dominance of MongoDB.

Once again MongoDB was responsible for more than 50% of all mentions of NoSQL database in LinkedIn member profiles in Q3, placing it way, way ahead of the nearest competitor.

sept donut

As always there were changes of position further down the rankings, with OrientDB overtaking Accumulo and RethinkDB overtaking Voldemort. We are talking about very small numbers, however. To be honest tracking these numbers has become something of a chore given the lack of change, and even the addition of Microsoft Azure DocumentDB and Google Cloud Bigtable couldn’t lift our interest

For the record, the fastest growth in the quarter was recorded by RethinkDB, with mentions up 36.2%, followed by multi-model players OrientDB (28.0%) and ArangoDB (23.0%), as well as Aerospike (22.1%). Inside the top ten, DynamoDB had the fastest growth (16.5%).

However, since none of the top 10 look like changing places any time soon, and none of the players outside stand any chance of breaking into the top 10, the time has come to retire the NoSQL LinkedIn Skills Index.

Perhaps we’ll pull it out and freshen it up on special occasions, however.

sept skills index

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.

The Data Day, A few days: July 3-17, 2015

Confluent raises $24m, OLAP meets Hadoop. And more

And that’s the data day, today.

The Data Day, A few days: February 2-6, 2015

NoSQL enters the multi-model age. And more

And that’s the data day, today.

It’s the end of NoSQL as we know it (and I feel fine)

Last week I tweeted that this week was shaping up to be a watershed week in the history of NoSQL. I was referring, of course, to MongoDB launching 3.0 and DataStax acquiring Aurelius – although more specifically what the context of these two announcements tells us about the future of NoSQL.

While each of these announcements could be considered significant in its own right in combination they suggest a new stage in the evolution of NoSQL and a clear signal that the future of NoSQL will be driven by database products that support multiple data models.

When we formally started covering NoSQL in 2010 it made sense to divide the various projects into four groups: key value stores, distributed (wide) column stores (or BigTable clones), graph databases, and document-oriented databases.

By early 2013 it had become obvious that there was another emerging category: multi-model databases.

Multi-model NoSQL databases have therefore been around for several years but while we have seen growing interest in these multi-model databases, in terms of widespread adoption they still lagged behind the early specialist NoSQL databases. That’s what makes the recent announcements by MongoDB and DataStax so significant.

    1. Along with releasing version 3.0 of its document database, MongoDB also began to share (at least with us) its long-term multi-model vision for MongoDB, explaining how the pluggable storage engine architecture could enable the database to support multiple data models – such as key value, graph and relational.
    1. Meanwhile DataStax described how its acquisition of Aurelius will see it developing a graph database to complement Apache Cassandra’s wide column key value model, and explained its multi-model strategy.
  • Multi-model momentum may have been growing for years but the fact that the commercial providers behind the two most popular NoSQL databases have detailed their plans to go multi-model confirms that the multi-model approach is the future of NoSQL.

    Indeed, since we expect to see similar moves from other NoSQL players it will become increasingly difficult to divide the NoSQL space in terms of key value stores, wide column stores, graph databases, and document-oriented databases. Instead it makes sense to divide the NoSQL projects in terms of whether they are single-model or multi-model.

    451 Research clients can read more about our perspectives on MongoDB’s strategic direction, as well as DataStax’s acquisition of Aurelius, and the wider implications for the NoSQL sector.