April 11th, 2012 — Data management
March 27th, 2012 — Data management
Back in December we ran a series of posts looking at the geographic distribution of NoSQL skills, according to the results of searching LinkedIn member profiles, culminating in a look at the relative overall popularity of the major NoSQL databases.
This week I took another look at LinkedIn to update the results for a forthcoming report, which gives us the opportunity to see how the results have changed over the past quarter:
While this provides us with an interesting opportunity to track LinkedIn profile mentions over time there isn’t a huge amount we can learn from this first update – other than that MongoDB seems to be increasing its dominance.
The only significant change that isn’t immediately obvious from looking at the chart is that Apache HBase has overtaken Apache CouchDB by a tiny margin to claim third place overall.
As we noted last time, however, Apache HBase is more reliant on the US than other NosQL databases for its LinkedIn mentions: it is the second most prevalent NoSQL database mentioned in the USA but fourth in the rest of the world.
Two other points to take into consideration:
- The results for Apache Cassandra are probably disproportionately low since we have to search for the full phrase in order to avoid including people called Cassandra.
- Previously we only searched for Membase. This time we added together the search results for both Membase and Couchbase. This may mean the result for Couch/Membase is disproportionately high since some members probably listed both.
This is not meant to be a comprehensive analysis, however, but rather a snapshot of one particular data source.
February 24th, 2012 — Data management
January 5th, 2012 — Data management
Apache Hadoop 1.0. The future of CouchDB (or Couchbase anyway). And more.
Welcome to the first in an occasional series of data-related news, views and links posts on Too Much Information. You can also follow the series @thedataday.
* The Apache Software Foundation Announces Apache Hadoop v1.0 Self-explanatory.
* The Future of CouchDB Apache CouchDB creator Damien Katz explains why he is focusing his attention on Couchbase Server.
* Understanding Microsoft’s big-picture plans for Hadoop and Project Isotope Mary Jo Foley parses Alexander Stojanovic’s presentation.
* MongoDB Extends Leadership in NoSQL 10gen claims more than 400 commercial customers.
* 1010data’s Unique Big Data Analytics Platform Sees Stunning Growth in 2011 1010data runs the numbers on its adoption in 2011.
* TouchDB 1.0 is out TouchDB is a lightweight CouchDB-compatible database engine suitable for embedding into mobile apps.
* Data Scientist = Rock Star, Really? Virginia Backaitis is sceptical.
* Swimming with Dolphins Splunk’s connector for MySQL.
* What the Sumerians can teach us about data Pete Warden finds data inspiration at the British Museum.
* How To (Not) Get Smart About Big Data Wim Rampen on the importance of filtering noise.
* For 451 Research clients
# Total Data: exploratory analytic platforms Spotlight report
# Apache Hadoop reaches version 1.0, with more to come Analyst note
# Acunu hones focus on ‘big data’ platform for operational analytics Market development report
# Jaspersoft gets big into ‘big data,’ illuminates BI business momentum Market development report
* Google News Search outlier of the day: “Bella” Becomes Most Popular Name for Both Dogs and Cats
And that’s the Data Day, today.
December 12th, 2011 — Data management
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.
December 8th, 2011 — Data management
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, and Apache Cassandra and Riak. Part three examines the geographic spread of 10gen’s MongoDB and Redis.
The statistics showed that 41.0% of the 6,048 LinkedIn members with “MongoDB” in their member profiles are based in the US, putting MongoDB is the top half of the table for geographic spread.
Only 11.2% are in the Bay area, fewer than Hadoop, Membase, HBase, Cassandra, Riak and Redis. The results also indicate that the New York area is a hot-spot for MongoDB skills, with 6.2% – as one might expect given the location of 10gen’s HQ. Other hot-spots include Brazil (4.2%) and Ukraine (2.8%).
Redis is even more widely adopted, with only 37% of the 2,152 LinkedIn members with “Redis” in their member profiles are based in the US, although 12.0% are in the Bay area.
Ukraine is also a hot-spot for Redis skills (3.8%) as is France (3.6%) and Spain (2.9%).
The series will conclude later this week with CouchDB, and Neo4j.
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
December 2nd, 2011 — Data management
NC State University’s Institute for Advanced Analytics recently published some interesting statistics on Apache Hadoop adoption based on a search of LinkedIn data.
The statistics graphically illustrate what a lot of people wer already pretty sure of: that the geographic distribution of Hadoop skills (and presumably therefore adoption) is heavily weighted in favour of the USA, and in particular the San Francisco Bay Area.
The statistics showed that 64% of the 9,079 LinkedIn members with “Hadoop” in their member profiles (by no means perfect but an insightful measure nonetheless) are based in the US, and that the vast majority of those are in the Bay Area.
The results are what we would expect to see given the relative level of immaturity of Apache Hadoop adoption, as well as the nature and location of the early Hadoop adopters and Hadoop-related vendors.
The results got me thinking two things:
- how does the geographic spread compare to a more maturely adopted project?
- how does it compare to the various NoSQL projects?
So I did some searching of LinkedIn to find out.
To answer the first question I performed the same search for MySQL, as an example of a mature, widely-adopted open source project.
The results show that just 32% of the 366,084 LinkedIn members with “MySQL” in their member profiles are based in the US (precisely half that of Hadoop) while only 4.4% are in the Bay area, compared to 28.2% of the 9,079 LinkedIn members with “Hadoop” in their member profiles.
The charts below illustrate the difference in geographic distribution between Hadoop and MySQL. The size of the boxes is in proportion to the search result (click each image for a larger version).
With regards to the second question, I also ran searches for MongoDB, Riak, CouchDB, Apache Cassandra*, Membase*, Neo4j, Hbase, and Redis.
I’ll be posting the results for each of those over the next week or so, but in the meantime, the graphic below shows the split between the USA and Rest of the World (ROW) for all ten projects.
It illustrates, as I suspected, that the distribution of skills for NoSQL databases is more geographically disperse than for Hadoop.
I have some theories as to why that is – but I’d love to hear anyone else’s take on the results.
*I had to use the ‘Apache’ qualifier with Cassandra to filer out anyone called Cassandra, while Membase returned a more statistically relevant result than Couchbase.
World map image: Owen Blacker
November 15th, 2011 — Data management
451 Research has today published a report looking at the funding being invested in Apache Hadoop- and NoSQL database-related vendors. The full report is available to clients, but below is a snapshot of the report, along with a graphic representation of the recent up-tick in funding.
According to our figures, between the beginning of 2008 and the end of 2010 $95.8m had been invested in the various Apache Hadoop- and NoSQL-related vendors. That figure now stands at more than $350.8m, up 266%.
That statistic does not really do justice to the sudden uptick of interest, however. The figures indicate that funding for Apache Hadoop- and NoSQL-related firms has more than doubled since the end of August, at which point the total stood at $157.5m.
A substantial reason for that huge jump is the staggering $84m series A funding round raised by Apache Hadoop-based analytics service provider Opera Solutions.
The original commercial supporter of Apache Hadoop, Cloudera, has also contributed strongly with a recent $40m series D round. In addition, MapR Technologies raised $20m to invest in its Apache Hadoop distribution, while we know that Hortonworks also raised a substantial round (unconfirmed, but reportedly $20m) from Benchmark Capital and former parent Yahoo as it was spun off in June. Index Ventures also recently announced that it has become an investor in Hortonworks.
I am reliably informed that if you factor in Hortonworks’ two undisclosed rounds, the total funding for Hadoop and NoSQL vendors is actually closer to $400m.
The various NoSQL database providers have also played a part in the recent burst of investment, with 10gen raising a $20m series D round and Couchbase raising $15m. DataStax, which has interests in both Apache Cassandra and Apache Hadoop, raised an $11m series B round, while Neo Technology raised a $10.6m series A round. Basho Technologies raised $12.5m in series D funding in three chunks during 2011.
Additionally, there are a variety of associated players, including Hadoop-based analytics providers such as Datameer, Karmasphere and Zettaset, as well as hosted NoSQL firms such as MongoLab, MongoHQ and Cloudant.
One investor company name that crops up more than most in the list above is Accel Partners, which was an original investor in both Cloudera and Couchbase, and backed Opera Solutions via its Accel- KKR joint venture with Kohlberg Kravis Roberts.
It appears that those investments have merely whetted Accel’s appetite for big data, however, as the firm last week announced a $100m Big Data Fund to invest in new businesses targeting storage, data management and analytics, as well as data-centric applications and tools.
While Accel is the fist VC shop that we are aware of to create a fund specifically for big data investments, we are confident both that it won’t be the last and that other VCs have already informally earmarked funds for data-related investments.
451 clients can get more details on funding and M&A involving more traditional database vendors, as well as our perspective on potential M&A suitors for the Hadoop and NoSQL players.
July 26th, 2011 — Data management, Uncategorized
Recently there have been a spate of postings regarding job trends for distributed data management technologies including Hadoop and the various NoSQL databases.
One thing you rarely see on these job trends charts is comparison with an incumbent technology, for context. There’s a reason for that, as this comparison of database-related jobs from Indeed.com illustrates:
Although there has been a recent increase in job postings related to Hadoop and MongoDB, both are dwarfed, in absolute terms, by the number of job postings involving SQL Server and MySQL.
So why all the fuss about Hadoop and NoSQL, from a corporate perspective? This chart, showing the relative growth for the same data management technologies, says it all:
April 20th, 2011 — Data management
As we noted last week, necessity is one of the six key factors that are driving the adoption of alternative data management technologies identified in our latest long format report, NoSQL, NewSQL and Beyond.
Necessity is particularly relevant when looking at the history of the NoSQL databases. While it is easy for the incumbent database vendor to dismiss the various NoSQL projects as development playthings, it is clear that the vast majority of NoSQL projects were developed by companies and individuals in response to the fact that the existing database products and vendors were not suitable to meet their requirements with regards to the other five factors: scalability, performance, relaxed consistency, agility and intricacy.
The genesis of much – although by no means all – of the momentum behind the NoSQL database movement can be attributed to two research papers: Google’s BigTable: A Distributed Storage System for Structured Data, presented at the Seventh Symposium on Operating System Design and Implementation, in November 2006, and Amazon’s Dynamo: Amazon’s Highly Available Key-Value Store, presented at the 21st ACM Symposium on Operating Systems Principles, in October 2007.
The importance of these two projects is highlighted by The NoSQL Family Tree, a graphic representation of the relationships between (most of) the various major NoSQL projects:
Not only were the existing database products and vendors were not suitable to meet their requirements, but Google and Amazon, as well as the likes of Facebook, LinkedIn, PowerSet and Zvents, could not rely on the incumbent vendors to develop anything suitable, given the vendors’ desire to protect their existing technologies and installed bases.
Werner Vogels, Amazon’s CTO, has explained that as far as Amazon was concerned, the database layer required to support the company’s various Web services was too critical to be trusted to anyone else – Amazon had to develop Dynamo itself.
Vogels also pointed out, however, that this situation is suboptimal. The fact that Facebook, LinkedIn, Google and Amazon have had to develop and support their own database infrastructure is not a healthy sign. In a perfect world, they would all have better things to do than focus on developing and managing database platforms.
That explains why the companies have also all chosen to share their projects. Google and Amazon did so through the publication of research papers, which enabled the likes of Powerset, Facebook, Zvents and Linkedin to create their own implementations.
These implementations were then shared through the publication of source code, which has enabled the likes of Yahoo, Digg and Twitter to collaborate with each other and additional companies on their ongoing development.
Additionally, the NoSQL movement also boasts a significant number of developer-led projects initiated by individuals – in the tradition of open source – to scratch their own technology itches.
Examples include Apache CouchDB, originally created by the now-CTO of Couchbase, Damien Katz, to be an unstructured object store to support an RSS feed aggregator; and Redis, which was created by Salvatore Sanfilippo to support his real-time website analytics service.
We would also note that even some of the major vendor-led projects, such as Couchbase and 10gen, have been heavily influenced by non-vendor experience. 10gen was founded by former Doubleclick executives to create the software they felt was needed at the digital advertising firm, while online gaming firm Zynga was heavily involved in the development of the original Membase Server memcached-based key-value store (now Elastic Couchbase).
In this context it is interesting to note, therefore, that while the majority of NoSQL databases are open source, the NewSQL providers have largely chosen to avoid open source licensing, with VoltDB being the notable exception.
These NewSQL technologies are no less a child of necessity than NoSQL, although it is a vendor’s necessity to fill a gap in the market, rather than a user’s necessity to fill a gap in its own infrastructure. It will be intriguing to see whether the various other NewSQL vendors will turn to open source licensing in order to grow adoption and benefit from collaborative development.
NoSQL, NewSQL and Beyond is available now from both the Information Management and Open Source practices (non-clients can apply for trial access). I will also be presenting the findings at the forthcoming Open Source Business Conference.