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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MongoDB 2.4 addresses search concerns - SD Times: Software Development News

MongoDB 2.4 addresses search concerns - SD Times: Software Development News | Big Data Technology, Semantics and Analytics |
New release brings tools to help scale the NoSQL poster child, as well as default settings that will prevent newbie mistakes
Tony Agresta's insight:

We agree with key points made in this article including: 

"Stirman said that the search capabilities added to MongoDB 2.4 are not a panacea. It does not have 'everything you could ever need in search, but we think that for a lot of people, the feature will be good enough. There will still be users who have more sophisticated needs for search, and they will integrate with a separate search technology,' he said.

Integrating with other search technology is never easy.  Maintaining two technologies always presents challenges. 

I would highly suggest you read this blog post written by the Bloor Group on MarkLogic:

You will find a great list of search capabilities in MarkLogic here:

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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

To see related videos, visit


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