The Data Day, A few days: May 16-29, 2015

Total Data market to hit $115bn by 2019. And more

And that’s the data day, today.

Database Landscape Map – December 2012

As previously mentioned, one of my most popular pieces of research while at 451 has been the database landscape graphic we produced for our NoSQL, NewSQL and Beyond report.

I recently published an updated version but noted that there were a group of database vendors that had emerged in 2012 that didn’t easily fit into the segments we’d created.

In order to address that I went back to the drawing board and, taking inspiration from London Underground and The Real Story Group, set about mapping the connections between the various players in the database space.

Note: the latest update to the map is available here.

I’ll be honest – I’m not convinced that this is as practically useful as the original, although I believe it is more accurate and it was an exhausting interesting exercise to put it together.

If anyone spots any glaring omissions or errors please keep them to yourself let us know. Additionally, the image is also available on posters, mugs, t-shirts and mouse pads, for a small fee 🙂

Of course, if you’re looking for some perspective on what this all means, I can recommend one of our highly competitive subscription packages

NoSQL LinkedIn Skills Index – rebooted

I decided to reboot our analysis of NoSQL skills, according to LinkedIn search results.

There are two main reasons for doing so: the first iteration did not take in enough of the various NoSQL projects; and I have – with help – worked my way around the eccentricities of LinkedIn search to produce a more accurate result for Apache Cassandra.

The analysis therefore now incorporates a wider spectrum of NoSQL projects, the top ten most popular of which are displayed below. The chart illustrates the number of LinkedIn member profiles mentioning each of the NoSQL projects:


The main change from the previous results is the promotion of Apache Cassandra, thanks to our better search string, while MarkLogic is the first of our new additions to make the top ten.

What hasn’t changed is the dominance of MongoDB, which is way-ahead of all the others. While I am not breaking out growth percentages versus previous counts due to the reboot, it is fair to say that MongoDB is outpacing many of its rivals. Neo4j and DynamoDB are also growing particularly well.

In fact, as can be seen from the chart below, MongoDB accounts for 43% of all mentions of NoSQL technologies in LinkedIn profiles, according to our sample.


Previewing Information Management in 2012

Every New Year affords us the opportunity to dust down our collective crystal balls and predict what we think will be the key trends and technologies dominating our respective coverage areas over the coming 12 months.We at 451 Research just published our 2012 Preview report; at almost 100 pages it’s a monster, but offers some great insights across twelve technology subsectors, spanning from managed hosting and the future of cloud to the emergence of software-defined networking and solid state storage; and everything in between. The report is available to both 451Research clients and non-clients (in return for a few details); access the landing page here.  There’s a press release of highlights here. Also, mark your diaries for a webinar discussing report highlights on Thursday Feb 9 at noon ET, which will be open for clients and non-clients to attend. Registration details to follow soon…

Here are a selection of key takeaways from the first part of the Information Management preview, which focuses on information governance, ediscovery, search, collaboration and file sharing. (Matt Aslett will be posting highlights of part 2, which focuses more on data management and analytics, shortly.)

  • One of the most obvious common themes that will continue to influence technology spending decisions in the coming year is the impact of continued explosive data and information growth.  This  continues to shape new legal frameworks and technology stacks around information governance and e-discovery, as well as to drive a new breed of applications growing up around what we term the ‘Total Data’ landscape.
  • Data volumes and distributed data drive the need for more automation and auto-classification capabilities will continue to emerge more successfully in e-discovery, information governance and data protection veins — indeed, we expect to see more intersection between these, as we noted in a recent post.
  • The maturing of the cloud model – especially as it relates to file sharing and collaboration, but also from a more structured database perspective – will drive new opportunities and challenges for IT professionals in the coming year.  Looks like 2012 may be the year of ‘Dropbox for the enterprise.’
  • One of the big emerging issues that rose to the fore in 2011, and is bound to get more attention as the New Year proceeds, is around the dearth of IT and business skills in some of these areas, without which the industry at large will struggle to harness and truly exploit the attendant opportunities.
  • The changes in information management in recent years have encouraged (or forced) collaboration between IT departments, as well as between IT and other functions. Although this highlights that many of the issues here are as much about people and processes as they are about technology, the organizations able to leap ahead in 2012 will be those that can most effectively manage the interaction of all three.
  • We also see more movement of underlying information management infrastructures into the applications arena.  This is true with search-based applications, as well as in the Web-experience management vein, which moves beyond pure Web content management.  And while Microsoft SharePoint continues to gain adoption as a base layer of content-management infrastructure, there is also growth in the ISV community that can extend SharePoint into different areas at the application-level.

There is a lot more in the report about proposed changes in the e-discovery arena, advances of the cloud, enterprise search and impact of mobile devices and bring-your-device-to-work on information management.

Our Total Data report is now totally available

…and it’s totally awesome.

Data volumes are exploding. Enterprises need better techniques to analyze, for example, IT management data or customer behavior statistics. The term ‘big data’ has emerged to describe new data management challenges posed by the growing volume, variety and velocity of data being produced by interactive applications and websites, as well as sensors, meters and other data-generating machines.

Our term ‘Total Data’ denotes a broad approach to data management that makes use of all available data, regardless of where it resides, to improve the efficiency and accuracy of business intelligence.

Total Data describes how users are deploying specialist data management technologies to maximize the benefit from individual operational or analytic workloads, while avoiding the creation of data silos by applying a unified approach to management that enables efficient data movement and integration.

This report examines the trends behind big data, as well as the new and existing technologies used to store and process this data, and outlines a Total Data management approach that is focused on selecting the most appropriate data storage and processing technology to deliver value from big data.

For more details of our Total Data report, and how to get it, see this page.

Valeriy Lobanovskyi: soccer manager… big data visionary

The increased focus on the value of data, combined with the recent release of Moneyball, has focused much attention on Oakland Athletics general manager Billy Beane and his successful use of data to improve performance.

Beane was my no means the first to realize the potential use of data in sports, however. That title could arguably go to Valeriy Lobanovskyi, manager of the Dynamo Kyiv soccer team between 1974 and 1990.

Lobanovskyi’s name is unlikely to be well known to even the most ardent football fans but our research into Total Football as an inspiration for our total data concept has highlighted the fact that Lobanovskyi was as much a big data visionary as he was a footballing visionary.

Total football is most readily associated with Rinus Michels and his teams: Ajax of Amsterdam, Barcelona, and the Dutch national side of the 1970s; but while Michels was busy winning Dutch league titles and European Cups, Lobanovskyi similarly was busy at Dynamo Kiev winning the Soviet League eight times, the Ukrainian league five times, and the European Cup Winner’s Cup twice with an approach known as Universality.

Describing the concept of Universality, Lobanovskyi once stated that “the most important thing in football is what a player is doing on a pitch when he is not in possession of the ball.”

Total football devotees will recognize the description, and as Hortonworks co-founder Arun C Murthy recently noted, Lobanovskyi arguably deserves as much credit as Michels for coming up with what would eventually become known as total football.

So far, so football visionary. What separates Lobanovskyi from Michels is the fact that he based much of his vision on data, and the analysis of data. Originally trained as an engineer, Lobanovskyi saw the potential value of a scientific, data-led approach to sport.

Together with statistician Anatoliy Zelentsov, Lobanovskyi devised a method of recording and analyzing the events and actions in a game of football and using it to provide players with a statistical analysis of their performance and set targets designed to meet the style he wanted the team to play (squeezing, pressing, or combination).

“All life,” Lobanovskyi once said, “is a number”.

An example of Lobanovskyi and Zelentsov’s targets, as explained in Inverting the Pyramid: A History of Football Tactics, by Jonathan Wilson, is displayed below:

To put this in some context, Lobanovskyi was using statistics and data as a means of gaining competitive advantage in sport 20 years before the formation of Opta Sports and Prozone, and almost 30 years before Beane and the 2002 Oakland Athletics.

Clients can read more about Total Football, and our description of approaches to data management in an era of ‘big data’, in our Total Data report, to be released in the coming days.

The geographic distribution of NoSQL skills – just one more thing

Hidden away amongst the details of our little tour around LinkedIn statistics on NoSQL and Hadoop skills was some interesting information on how many LinkedIn members list the various data management technologies in our sample in their profiles.

Our original post contained the fact that there were 9,079 LinkedIn members with “Hadoop” in their member profiles, for example, compared to 366,084 with “MySQL” in their member profiles.

Later posts showed there were 170 with “Membase” and 1,687 with “HBase”, 787 with “Apache Cassandra” and 376 with “Riak”, 6,048 with “MongoDB” and 2,152 with “Redis”, and finally, 1,844 with “CouchDB” and 268 with “Neo4j”.

This gives us an interesting perspective on the relative adoption of the various NoSQL databases:

If it wasn’t already obvious from the list above, the chart illustrates just how much more prevalent MongoDB skills are compared to the other NoSQL databases, followed by Redis, Apache CouchDB, Apache HBase and Apache Cassandra. The chart also illustrates that while HBase is the second most prevalent NoSQL skill set in the USA, it is only fourth overall given its lower prevalence in the rest of the world.

In response, a representative from a certain vendor notes “Some skills are more valued not because they are more prevalent, but because they are harder to achieve.” Make of that what you will.

The geographic distribution of NoSQL skills: CouchDB and Neo4j

Following last week’s post putting the geographic distribution of Hadoop skills, based on a search of LinkedIn members, in context, this week we will be publishing a series of posts looking in detail at the various NoSQL projects.

The posts examine the geographic spread of LinkedIn members citing a specific NoSQL database in their member profiles, as of December 1, and provides an interesting illustration of the state of adoption for each.

We’ve already taken a look at Membase and HBase; Apache Cassandra and Riak; and 10gen’s MongoDB and Redis.

Part four brings the series to a close with a look at Apache CouchDB and Neo4j, which boast the most geographically diverse adoption of the NoSQL databases in our sample.

The statistics showed that 36.4% of the 1,844 LinkedIn members with “CouchDB” in their member profiles are based in the US, while only 8.9% are in the Bay area, the least of any of the NoSQL database we looked at.

The results also indicate that the UK is a particularly strong area for CouchDB skills, with 7.1%. Other hot-spots include Canada (4.1%), Germany (4.0%) and The Netherlands (3.1%).

Neo4j is even more widely adopted, with only 36.2% of the 268 LinkedIn members with “Neo4j” in their member profiles based in the US, although 10.4% are in the Bay area.

With 4.1%, Sweden is a hot-spot for Neo4j skills, as one might expect given that’s where it and Neo Technology originated. The UK is also strong with 9.7%, followed by India with 5.6% and the New York area with 4.9%.

Since Neo4j originated in Europe it is of course an open question whether its higher adoption in the Rest of the World than the US is a sign of a greater spread of adoption, or a relative failure to infiltrate the US market. Given that the company already has an active presence in the US we are inclined towards the former.

N.B. The size of the boxes is in proportion to the search result (click each image for a larger version). World map image: Owen Blacker

Forthcoming webinar: What is a cloud database?

Cloud computing and big data are two of the hottest topics in the industry today, which makes cloud databases a particularly hot prospect for 2012. What is a cloud database, however? On Thursday, December 15 at 12:00pm EST I’ll be taking part in a webinar with Karen Tegan Padir, Vice President of Products and Marketing, EnterpriseDB on the subject of cloud computing and true cloud databases.

In this webcast, you’ll get an overview of the current state of cloud database computing, and more specifically the differences between cloud databases and databases in the cloud. I’ll be providing an overview of the functional requirements that separate databases running in the public cloud, and databases that will be used to power private and hybrid clouds.

Then Karen will provide an overview and demonstration of Postgres Plus Cloud Server, which provides DaaS for PostgreSQL databases and went into public beta earlier this week.

You can register for the event here

The geographic distribution of NoSQL skills: HBase and Membase

Following last week’s post putting the geographic distribution of Hadoop skills, based on a search of LinkedIn members, in context, this week we will be publishing a series of posts looking in detail at the various NoSQL projects.

The posts examine the geographic spread of LinkedIn members citing a specific NoSQL database in their member profiles, as of December 1, and provides an interesting illustration of the state of adoption for each.

We begin this week’s series with Membase and HBase, the two projects that proved, like Apache Hadoop, to have significantly greater adoption in the USA compared to the rest of the world.

The statistics showed that 58.2% of the 170 LinkedIn members with “Membase” in their member profiles are based in the US (as previously explained, we tried the same search with Couchbase, but with only 85 results we decided to use the Membase result set as it was more statistically relevant).

As with Hadoop, a significant proportion (27.1%) of those are in the Bay area, the highest proportion of all the NoSQL databases we looked at. The results also indicate that Ukraine is a hot-spot for Membase skills, with 3.5%, while Membase adoption is lower the UK (2.4%) than other NoSQL databases.

It should not be a great surprise that Apache HBase returned similar results to Apache Hadoop. The top eight individual regions for HBase were exactly the same as for Hadoop, although the UK (3.4%) is stronger for HBase, as is India (10.7%).

The statistics showed that 57.0% of the 1,687 LinkedIn members with “HBase” in their member profiles are based in the US, with 25.0% in the Bay area (the third highest in our sample behind Hadoop and Membase).

The series will continue later this week with MongoDB, Riak, CouchDB, Apache Cassandra, Neo4j, and Redis.

N.B. The size of the boxes is in proportion to the search result (click each image for a larger version). World map image: Owen Blacker