May 17th, 2013 — Data management
Tableau IPOs. Funding for EdgeSpring, Cloudant LucidWorks, and GraphLab
And that’s the data day, today.
March 26th, 2013 — Data management
As Q1 comes to a close its time to take another look at our NoSQL LinkedIn Skills Index, based on the number of LinkedIn member profiles mentioning each of the NoSQL projects. This is the second update since we rebooted the analysis in September 2012 to account for more products and refine our search terms.
A few interesting statistics to pick out: Neo4j has, as predicted, jumped ahead of MarkLogic for sixth place. No other changes of position, but outside the top ten, shown here, Apache Accumulo continues to grow well.
In fact, Apache Accumulo had the fastest rate of growth for the second quarter in succession, just ahead of DynamoDB and OrientDB -once again – followed by Apache Cassandra and MongoDB.
MongoDB’s growth means that it once again extended its lead as the most popular NoSQL database, according to LinkedIn profile mentions. As the chart below illustrates, it now accounts for 46% of all mentions of NoSQL technologies in LinkedIn profiles, according to our sample, compared with 45% in December.
February 12th, 2013 — Data management
ClearStory sheds light on data analysis service. Illuminating ‘dark data’. 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.
January 15th, 2013 — Data management
As 2012 came to a close I tweeted
NuoDB has today kicked off that debate with the launch of its Cloud Data Management System and 12 rules for a 21st century cloud database.
NuoDB’s 12 rules appear pretty sound to me – in fact you could argue they are somewhat obvious. This is actually to NuoDB’s credit in my opinion, in that they haven’t simply listed 12 differentiating aspects of their product, but 12 broader requirements.
Either way, I believe that this is the right time to be debating what constitutes a “cloud database”. Database on the cloud are nothing new, but these are existing relational database products configured to run on the cloud.
In other words, they are databases on the cloud, not databases of the cloud. There is a significant difference between spinning up a relational database in a VMI on the cloud versus deploying a database designed to take advantage of, enable, and be part of, the cloud.
To me, a true cloud database would be one designed to take advantage of and enable elastic, distributed architecture. NuoDB is one of those, but it won’t be the only one. Many NoSQL databases could also make a claim, albeit not for SQL and ACID workloads.
This isn’t a matter of SQL versus NoSQL, however. We’ve seen companies building their own next-generation database platforms deploying NoSQL and SQL technologies alongside each other for different workload and consistency requirements. Where the SQL layer falls down is the inability of existing relational databases to support elastic, geographically distributed cloud environments.
NuoDB believes it has a solution to that. So too do others including GenieDB, Translattice and VMware. Meanwhile Google’s F1 and Spanner projects have legitimized the concept of the globally-distributed SQL database.
Either way, the era of the relational cloud database – rather than the relational database on the cloud – has begun.
January 10th, 2013 — Data management
SAP on HANA. Funding for Guavus and ScaleArc. And more
And that’s the Data Day, today.
January 10th, 2013 — Data management
451 Research’s 2013 Database survey is now live at http://bit.ly/451db13 investigating the current use of database technologies, including MySQL, NoSQL and NewSQL, as well as traditional relation and non-relational databases.
The aim of this survey is to identify trends in database usage, as well as changing attitudes to MySQL following its acquisition by Oracle, and the competitive dynamic between MySQL and other databases, including NoSQL and NewSQL technologies.
There are just 15 questions to answer, spread over five pages, and the entire survey should take less than ten minutes to complete.
All individual responses are of course confidential. The results will be published as part of a major research report due during Q2.
The full report will be available to 451 Research clients, while the results of the survey will also be made freely available via a
presentation at the Percona Live MySQL Conference and Expo in April.
Last year’s results have been viewed nearly 55,000 times on SlideShare so we are hoping for a good response to this year’s survey.
One of the most interesting aspects of a 2012 survey results was the extent to which MySQL users were testing and adopting PostgreSQL. Will that trend continue or accelerate in 2013? And what of the adoption of cloud-based database services such as Amazon RDS and Google Cloud SQL?
Are the new breed of NewSQL vendors having any impact on the relational database incumbents such as Oracle, Microsoft and IBM? And how is SAP HANA adoption driving interest in other in-memory databases such as VoltDB and MemSQL?
We will also be interested to see how well NoSQL databases fair in this year’s survey results. Last year MongoDB was the most popular, followed by Apache Cassandra/DataStax and Redis. Are these now making a bigger impact on the wider market, and what of Basho’s Riak, CouchDB, Neo4j, Couchbase et al?
Additionally, we have been tracking attitudes to Oracle’s ownership of MySQL since the deal to acquire Sun was announced. Have MySQL users’ attitudes towards Oracle improved or declined in the last 12 months, and what impact will the formation of the MariaDB Foundation have on MariaDB adoption?
We’re looking forward to analyzing the results and providing answers to these and other questions. Please help us to get the most representative result set by taking part in the survey at http://bit.ly/451db13
December 19th, 2012 — Data management
GenieDB, Qubole, EdgeSpring, CouchDB, and more
And that’s the Data Day, today.
December 18th, 2012 — Data management
Time again to take a look at our NoSQL LinkedIn Skills Index, based on the number of LinkedIn member profiles mentioning each of the NoSQL projects. This is the first update since we rebooted the analysis in September to account for more products and refine our search terms.
On the face of it not a lot has changed in the last quarter, although there are a few interesting statistics to pick out. For instance, Neo4j is now practically tied for sixth place with MarkLogic and can be expected to overtake it in Q1 2013. Outside the top ten shown above, Apache Accumulo has gained two places – overtaking Aerospike and Hypertable.
In fact, Apache Accumulo showed the fastest rate of growth in mentions between September and December, just ahead of DynamoDB and OrientDB, followed by Couchbase and MongoDB.
MongoDB’s growth means that it has cemented its place as the most popular NoSQL database, according to LinkedIn profile mentions. As the chart below illustrates, it now accounts for 45% of all mentions of NoSQL technologies in LinkedIn profiles, according to our sample, compared with 43% in September.
December 13th, 2012 — Data management
451 Research’s Information Management practice has published its latest long-format report: Total Data Analytics. Written by Krishna Roy, Analyst, BI and Analytics, along with myself, it examines the impact of ‘big data’ on business intelligence and analytics.
The growing emphasis on ‘big data’ has focused unprecedented attention on the potential of enterprises to gain competitive advantage from their data, helping to drive adoption of BI/analytics beyond the retail, financial services, insurance and telecom sectors.
In 2011 we introduced the concept of ‘Total Data‘ to reflect the path from the volume, velocity and variety of big data to the all-important endgame of deriving maximum value from that data. Analytics plays a key role in deriving meaningful insight – and therefore, real-world business benefits – from Total Data.
In short, big data and Total Data are changing the face of the analytics market. Advanced analytics technologies are no longer the preserve of MBAs and ‘stats geeks,’ as line-of-business managers and others increasingly require this type of analysis to do their jobs.
Total Data Analytics outlines the key drivers in the analytics sector today and in the coming years, highlighting the technologies and vendors poised to shape a future of increased reliance on offerings that deliver on the promise of analyzing structured, semi-structured and unstructured data.
The report also takes a look at M&A activity in the analytics sector in 2012, as well as the history of investment funding involving Hadoop, NoSQL and Hadoop-based analytics specialists. It also contains a list of 40 vendors we believe have the greatest potential to shape the market in the coming years.
The report is available now to 451 Research clients, here. Non-clients can get more information and download an executive summary from the same link.
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