Big Intellects

In earlier days data was stored in the flat file and there was no structure in the flat file.  If any data has to be retrieved from the flat file it was a project by itself. There was no possibility of retrieving the data efficiently and data integrity has been just a term discussed without any modeling or structure around. Database residing in the flat file had more issues than we would like to discuss in today’s world.

Edgar Frank Codd was a British computer scientist who, while working for IBM, invented the relational model for database management, the theoretical basis for relational databases. He presented 12 rules for the Relational Database and suddenly the chaotic world of the database seems to see discipline in the rules. Relational Database was a promising land for all the unstructured database users. Relational Database brought into the relationship between data as well improved the performance of the data retrieval. Database world had immediately seen a major transformation and every single vendors and database users suddenly started to adopt the relational database models.

Since Edgar F Codd proposed 12 rules for the RBDMS there were many different vendors who started them to build applications and tools to support the relationship between database. This was indeed a learning curve for many of the developer who had never worked before with the modeling of the database. However, as time passed by pretty much everybody accepted the relationship of the database and started to evolve product which performs its best with the boundaries of the RDBMS concepts. This was the best era for the databases and it gave the world extreme experts as well as some of the best products. The Entity Relationship model was also evolved at the same time. In software engineering, an Entity–relationship model (ER model) is a data model for describing a database in an abstract way.

Well, everything was going fine with the RDBMS in the database world. As there were no major challenges the adoption of the RDBMS applications and tools was pretty much universal. There was a race at times to make the developer’s life much easier with the RDBMS management tools. Due to the extreme popularity and easy to use system pretty much every data was stored in the RDBMS system. New age applications were built and social media took the world by the storm. Every organizations was feeling pressure to provide the best experience for their users based the data they had with them. While this was all going on at the same time data was growing pretty much every organization and application.

The enormous data growth now presented a big challenge for the organizations who wanted to build intelligent systems based on the data and provide near real time superior user experience to their customers. Various organizations immediately start building data warehousing solutions where the data was stored and processed. The trend of the business intelligence becomes the need of everyday. Data was received from the transaction system and overnight was processed to build intelligent reports from it. Though this is a great solution it has its own set of challenges. The relational database model and data warehousing concepts are all built with keeping traditional relational database modeling in the mind and it still has many challenges when unstructured data was present.

Every organization had 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 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 of the real time intelligence from the fast paced data flow is now becoming a necessity.

Large 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.