Big Data Technology, Semantics and Analytics
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Big Data Technology, Semantics and Analytics
Trends, success and applications for big data including the use of semantic technology
Curated by Tony Agresta
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Updated Database Landscape map – June 2013 — Too much information

Updated Database Landscape map – June 2013 — Too much information | Big Data Technology, Semantics and Analytics |
Tony Agresta's insight:

MarkLogic is uniquely positioned on this database landscape map.    Here's what makes the position very different from other vendors:

1.  Search - MarkLogic is directly connected to all the major enterprise search vendors.   Recent recognition of this was confirmed by Gartner in its Enterprise Search Magic Quadrant.   Notice that other NoSQL technologies are nowhere close to this connection point.

2.  General Purpose - MarkLogic provides an enterprise NoSQL database and Application Services and Search.   With support for many development languages, REST and JAVA APIs, MarkLogic has clear links to SAP, Enterprise DB and a host of other database providers.

3.   Graph and Document - MarkLogic has long been recognized as a document store and used widely all over the world for this purpose.  Notice the subtle connection to Graph as well connecting MarkLogic to other vendors in this space like Neo4J.  MarkLogic 7 promises to deliver a world class triple store to index subjects, predicates and objects in XML documents or load other triples through the MarkLogic Content Pump.  For the first time, the only Enterprise NoSQL technology with search will include semantics.  Updated APIs and support for SPARQL are part of this release.

4.  Big Tables - MarkLogic's ability to handle big data has long been known.  The olive green line is designated for vendors like MarkLogic, Cassandra, HBASE, etc.   MarkLogic's partnership with Intel for Distribution of Apache Hadoop and the fact that MarkLogic ships with Hadoop connectors provide additional confirmation for this position.

5.   Key Value Stores - Data can be stored as keys without a database schema required lby relational databases.  In MarkLogic's case, huge quantities of data can be indexed in real time with data stored in memory and disk making search results instant and complete.   After a recent analysis of over 50+ MarkLogic customers, the abilty to quickly get up and running and deliver new information products to market was a business driver they mentioned over and over again.

The fact is, no one else on the list has all of these qualities.   Because of this unique position, visually you see MarkLogic distanced from other clusters or long lists of technology vendors.  

To learn more, you can go to MarkLogic Resources.

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Press release: MarkLogic opens up RSC data

Tony Agresta's insight:

Big data applications continue to radically transform industry, science and our lives.  I never would have guessed that 170 years of information could be unlocked and available on line.  But in so doing, the RSC has exceeded the expectations of their customers creating loyalty driven by content and a positive user experience.   Now data has been integrated, assembled and organized using different formats.   If this isn't enough, the automation used to tag the content allows users to not only gain access quickly but to do so within context.  It's this point around "context" that is transforming applications like these today.   With context comes understanding allowing the human mind to quickly interpret meaning and take action.   Dynamic publishing applications like these and the others the RSC has developed provide a ground breaking competitive advantage.  This article is worth reading, especially the ROI metrics in paragraph 9.  Enterprise ready, hardened, scalable, secure big data technology allows organizations like the RSC to dramatically improve their productivity today. They are publishing 3 times as many journals and 4 times as many articles over 2006.  New applications are bound to increase exposure and help chemists, students and researchers.   In so doing, it helps all of us. 



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Database Revolution; Future of Information Publishing

Tony Agresta's insight:

Here’s a brilliant presentation from Mike Bowers, Principal Engineer at the Church of Jesus Christ of Latter Day Saints.  It accomplishes two major objectives:

  • Mike reviews the strengths and weaknesses of the five major classes of databases today (relational, dimensional, object, graph and document). 
  • He then dissects the major NoSQL databases on the market including MarkLogic, Mongo, Riak, Cloudant/Couch DB and Cassandra.  How do they stack up? Are they enterprise ready? 

If developer productivity, application performance and enterprise readiness are concerns that your company has, this video is a “must see”.  Here are some sound bites I took away from the presentation.  Please note these comments only begin to scratch the surface of Mike’s message.


Over 80 % of the data being created today is unstructured and organizations need to store, search and analyze hundreds of different data formats at light speed.   The ability to handle data variability, data variety and data relevance has jumped to the top of the agenda for both business and IT. But how can organizations discern meaning from this data?   How do they create context around unstructured data with so many formats in play?  How do they make it discoverable?  


Relational Models are not well suited to handle the problem since they were designed to organize your data in rows, columns and tables.  The variety and complexity of unstructured data coupled with the overriding need to scale out on commodity hardware prevent them from leveraging over 80% of the data today.


Mike shows a great example of how the document database (NoSQL database) takes unstructured data in the form of a story, identifies the data elements in the story (topic, location, author), semantically links these elements to show relationships between the elements and then identifies the hierarchy within the story (title, subtitle, body, etc…).   Armed with all of this, the unstructured data lives with context.  The original document persists but now all of the elements are discoverable in a variety of ways.  


Given the reality that unstructured data is growing so rapidly and needs to be integrated and analyzed alongside structured data to complete the picture, what does an application need from a NoSQL database?  Basically what every database needs - five core capabilities:  1) inserts, updates and deletes 2) the ability to query the data 3) the ability to search the data 4) the ability to bulk process the data and 5) the ability to do all of this consistently.  With extraordinary data volumes, this has to be done at scale in an affordable way.  


The only enterprise NoSQL database that handles all of this today is MarkLogic.   Mike evaluates search relevance, advanced search using facets, geospatial search, entity enrichment, data consistency, developer productivity using JAVA, the ability to retrieve multiple documents, integration with the BI stack using SQL, real time data ingestion, indexing and much more.   Imagine if you had to ask your programmers to develop an application to handle data locks, threading bugs, serialization, dead locks and rare conditions?     Imagine if you had to write the code to ensure all parts of your data transactions succeeded?  How would you ensure all of the data has been committed consistently? Do the commits meet all of your data rules?    How do you ensure your data survives system failures and can be recovered after an inadvertent deletion?   


The vast majority of NoSQL databases lack these capabilities but MarkLogic has all of them.  If you are evaluating database technology today, I would highly recommend watching this video – at least twice.  


Learn more at

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Ian Sykes's comment, January 2, 2013 11:39 AM
Hi Dominic Happy New Year. Yes I was impressed by this and included it on my Blog. Certainly clarifies a lot for 2013.
Adrian Carr's curator insight, April 30, 2013 1:38 PM

this is a great presentation - full of great insight into the market

Edwin's curator insight, March 19, 2014 10:30 PM

Future of Database development