BigData, Programming

What is BigData?

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Big Data is not just about lots of data or the big thing in a particular size, it is actually a concept providing an opportunity to find new insight into your existing data as well guidelines to capture and analysis your future data. It makes any business more agile and robust so it can adapt and overcome business challenges.

In earlier days there were lots of data considered in a flat file and there was not any structure to make all data as in proper manner so get requested data with efficient and proper analysis to searching through all data. So after IBM came into the picture to provide a relational database management system with following ACID properties. At that time, many organizations started working to develop an RDBMS system with full fill all the rules of ACID property and also had been working on those database systems to improve more and more.

Now we have the almost well-structured database and data is growing instantly and getting bigger and bigger.

Why BigData concept into the picture?

Now every organization had the expertise to manage structured data but the world had already changed to unstructured data. There was intelligence in the videos, photos, SMS, text, social media messages and various other data sources. All of these needed to now bring to a single platform and build a uniform system which does what businesses need. The way we do business has also been changed. There was a time when the user only got the features what technology supported, however, now users ask for the feature and technology is built to support the same. The need for the real-time intelligence from the fast paced data flow is now becoming a necessity.

The largest amount (Volume) of difference (Variety) of high-speed data (Velocity) is the properties of the data. The traditional database system has limits to resolve the challenges this new kind of the data presents. Hence the need of the Big Data Science. We need innovation in how we handle and manage data. We need creative ways to capture data and present to users.

The challenge is to manage high volume data consist of petabytes (1,024 terabytes) or exabytes (1,024 petabytes) data with million and trillion data of people around the globe, all from different sources (e.g. Web, sales, customer contact center, social media, mobile data and so on) and also the major part of data are doing analysis and it is the biggest part of data manipulation such a massive data. Many organizations like Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC, HP and Dell already invested millions in building data management and analytics and also other to involve getting better software to handle such unstructured data to represent it into structured data.

What BigData to work?

In Big Data various different data sources are part of the architecture, hence extract, transform and integration are one of the most essential layers of the architecture. Most of the data is stored in relational as well as nonrelational data marts and data warehousing. As per the business need various data are processed as well converted to proper reports and visualizations for end users. Just like software the hardware is almost the most important part of the Big Data Architecture. In the big data architecture, hardware infrastructure is extremely important and failure over instances as well as redundant physical infrastructure is usually implemented.

NoSQL is a very famous word and it really means Not Relational SQL or Not Only SQL. This is because in Big Data Architecture the data is in any format. It can be unstructured, relational or in any other format or from any other data source. To bring all the data together relational technology is not enough, hence new tools, architecture and other algorithms are invented which take care of all the kind of data. So this phenomenon called NoSQL and it is the engine of BigData architecture.

The NoSQL supports column store, document store, key-value stores, and graph databases. Here are some of the examples of the each of the No SQL Category.

Column: Hbase, Cassandra, Accumulo
Document: MongoDB, Couchbase, Raven
Key-value: Dynamo, Riak, Azure, Redis, Cache, GT.m
Graph: Neo4J, Allegro, Virtuoso, Bigdata

You can find lots of methodology for NoSQL now in the market and choose one that will fulfill all your business needs. Hadoop is famous nowadays as open source, free and Java-based software framework offers a powerful distributed platform to store and manage Big Data.

All the big social media sites nowadays have moved away from Relational Database. Actually, this is not entirely true. Many of the popular social media sites use Big Data solutions along with Relational Database. Many are using relational databases to deliver the results to end user on the run time and many still use a relational database as their major backbone.

Here are a few examples:
Facebook with MySQL to display the timeline
Twitter with MySQL
Tumblr with Sharded MySQL
Wikipedia uses MySQL for data storage

There are many for prominent organizations which are running large scale applications uses relational database along with various Big Data frameworks to satisfy their various business needs.

Big data analytics often associate with cloud computing because the analysis of large data sets in real-time requires a platform like Hadoop to store large datasets across a distributed cluster and MapReduce to coordinate, combine and process data from multiple sources.The Cloud Computing and Big Data are Google and Amazon.com. Both have fantastic Big Data offering with the help of the cloud.

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