Showing posts with label Big Data. Show all posts
Showing posts with label Big Data. Show all posts

Tuesday, December 2, 2014

Bigdata Ecosystem



I have had a lot of discussions on big data with my clients and prospects.  During these discussions some questions comes up on Hadoop for ex: – What are the different components of a Hadoop ecosystem?

In my point of view the question should be - What are different components of a Bigdata ecosystem?

Everyone seems to have a different answer for it.  I have tried to consolidate the answers. The results are this picture.


Tuesday, June 10, 2014

Apache Hive Architecture Diagram



While studying about internals of Apache Hive, I realized that everyone is using same set of illustrations (from here & here) to explain Hive architecture.  To understand the internals of Hive, I drew my own diagram. I am sharing the same here, if you like it share it.


Wednesday, October 2, 2013

Does unstructured data exist?



While talking to NO SQL enthusiastics, I often hear that No SQL database can handle unstructured data. Similar arguments are also echoed by Hadoop and BigData devotes.  Are these people are technically correct or just using marketing hype to influence IT decision makers who are business savvy but technical dependent?

In my point of view, there is nothing called unstructured data. NO SQL, Hadoop and BigData zesty people are calling any dataset which does not fit in relational data base as unstructured data. What do you think?

In the context of data, there are two attributes which defines complexity. First is relationship among objects (equivalent to tables in relational database) of data and second is varying number of elements (equivalent to columns in a table in relational database) in objects. With respect to these two parameters there are four possible combinations:

      * Both number of elements in objects and relationship among objects is fixed; it is not changing over time period.
a.       Numbers of elements in objects are fixed and relationships among objects are simple and can be described using relational math. This type of data is prime candidate for relational database.
b.      Numbers of elements in objects are fixed and relationships among objects are not simple and difficult/nearly impossible to describe using relational math. For example if relationships among objects are mimicking graph structure than graph database (e.g. Neo4j) is better choice than relational or any other type of database.

      * Numbers of elements in objects are varying on ad hoc basis irrespective of complexity of relationships among objects than relational database is not the solution. You need database which can accommodate varying number of elements in  objects such as MongoDB

       * Numbers of elements in objects are fixed but relationship among object is varying on ad hoc basis. Again relational database is not the solution. You should explore HBase or MongoDBfor this scenario. 

          *Both numbers of elements in objects are varying and relationships among objects are changing on ad hoc basis. Yep, you guessed correctly, relational database is not the part of the solution. For this scenario you can explore HBase or MongoDB.

In above discussion, I have not considered volume of data.

In truly unstructured data, structure of data is not definable. If one can’t define a structure, then structure does not exist from programming perspective. 

There is no unstructured data. Data has structure, we may have not been able to discover or comprehend it yet.

Thursday, October 25, 2012

Book Review: Ethics of Big Data



Book Review:  Ethics of Big Data by Kord Davis and Doug Patterson: Publisher- O'Reilly: ISBN- 13: 978-1449311797


Ethics of Big Data is one of the rare books which bring up question of ethics in business to engineering level.

Though book title is confusing, it should be something like “Ethics of Personal Data” to characterize its content more accurately and ethically. It seems author wanted to ride on Big Data wave.

Book covers ethical question from four dimensions:

  1. Privacy,
  2. Identity,
  3. Ownership, and
  4. Reputation

in a very comprehensive manner.  Author misses one vital dimension – Public/Social Good. Though I am trained in philosophy but I find any ethical discussion which is devoured of social/public good vey hollow.

Kord tracks the ethical question in very smart way and avoids being prescriptive but advice on periphery so business specific solutions can be devised and implemented.

Disclaimer: I did not get paid to review this book, and I do not stand to gain anything if you buy the book. I got electronic format of book from publisher for review.


One can get more information about book and related topics from:

  1. Amazon: http://www.amazon.com/Ethics-Big-Data-Balancing-Innovation/dp/1449311792
  2. Publisher -- Oreilly http://shop.oreilly.com/product/0636920021872.do
Another Review: http://beajumarang.wordpress.com/2012/10/18/review-ethics-of-big-data