MongoDB for Big Data: The Perfect Alliance

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Vinod Raju


September 20th, 2016

Learning a database is a MUST have skill for any IT employee. The idea that, database skills is only for the meticulous DBA’s (data base administrators) is a passé. The reason for this quite is simple, if observed carefully. Roughly 90% of the software systems built for any purpose revolve around 2 major activities:

  1. Data storage
  2. Data retrieval & processing

Data retrieval and processing is backed by the field of algorithms and it’s not everyone’s cup of tea, but the former isn’t. About 7 out of 10 people in IT industry would have directly/indirectly interacted with some kind of database at some point of time in their career and most of the time we would not have bothered to delve deeper to learn more about it.

Rather, one would just use databases as mere black boxes which store the data and you can retrieve it on demand. This ideology was fine until the last decade but not anymore since the advent of BIG DATA.

The world of databases is divided into two groups, the SQL based and the NoSQL based. Though the two seem to be rivals from their nomenclature, the bridge connecting the two is the data. In the last 10 years, the foot print of NoSQL databases has surpassed that of SQL based RDBMS which has been in place for close to 3 decades. The sole reason for this is DATA which dictates the policy of any organization to use NoSQL or SQL based systems.

Traditional RDBMS (relational database management systems) were originally designed to accommodate data originating from transactions in day to day life. Banking & financial services industries contributed to the bulk of the data and this was structured data (the ones which would fit perfectly into tables consisting of rows and columns). RDBMS is capable of managing large volumes of data effectively and was not designed to store unstructured and semi structured data (plain text, photos, voice messages, video files, xml files etc.), which constitutes the bulk of BIG DATA.

Ease of horizontal scalability and ability to accommodate a wide variety of data (schema free) by and large are the hallmark features of NoSQL databases which has made it a clear winner to be integrated with BIG DATA processing frameworks like Hadoop.

Any aspiring candidate who would be eager to embrace exciting career opportunities in the areas related to BIG DATA, data analytics or data science should seriously consider mastering at least a couple of databases under the NoSQL umbrella and MongoDB is THE guy to shake hands with! As a matter of fact, MongoDB is THE most popular and widely used NoSQL database in the industry consistently in the last 5 years.

database

 

The role of a data engineer, data analyst or data scientist is incomplete without a decent understanding of where and how the data is stored. The knowledge of the backend databases would significantly impact the next stages in the pipeline, namely the ETL operations or the analysis and reporting related tasks. The end to end knowledge in data analytics related tasks is never complete without the working knowledge of the database involved.

The obvious question which would arise in one’s mind is that “how many databases do I need to learn?” The course about MongoDB offered by Jigsaw is not isolated to just one topic; it would rather cover the fundamental design concepts in all the 4 categories of NoSQL databases to begin with. MongoDB would be stressed upon in detail as the course progresses. Indeed, it’s a safe bet to learn the most popular NoSQL database to begin with in order equip your engineer’s tool kit with another interesting and a must have tool. Welcome to the world of databases.


Suggested Read

MongoDb for Real Time Analytics

Why we Need NoSQL Database Systems

 

 

 

 

Vinod Raju on sablinkedin
Vinod Raju
Vinod holds an MS in Computer Science from Syracuse University, New York, USA and a B.E degree in Computer Science.

His 10 years of experience in IT, research and training spans across several areas such as web application development, system software
engineering, software testing and network administration in companies such as Cognizant, Cisco and Erricson.

His interest revolves around teaching and research in areas of parallel processing, machine learning algorithms,
applied mathematics and analytics.

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