Big Data and Data Warehousing

Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set. The target moves due to constant improvement in traditional DBMS technology as well as new databases like NoSQL and their ability to handle larger amounts of data.

Big Data is different from Data Warehousing as Big Data is a technology and a data warehouse is an architecture. The Big Data technology is capable of handling a lot of data. It can handle huge stream of data in the form of unstructured strings and is used to store and manage large amounts of data.

A data warehouse is the place where you build the corporate single version of the truth. A data warehouse contains data that is integrated and is organized that can be analyzed. A data warehouse contains historical data. Using advanced analytics techniques such as predictive analytics, data mining, statistics, and natural language processing, businesses can study big data to understand the current state of the business and track evolving aspects such as customer behavior.

Big data is actually a super-set of the information and processes that have characterized data warehousing since its inception, with big data focusing on large-scale and often short-term analysis. With the advent of big data, data warehousing itself can return to its roots — the creation of consistency and trust in enterprise information. In truth, there exists a substantial overlap between the two areas; the precepts and methods of both are highly complementary and the two will be mandatory for all forward-looking enterprises.

The Big data Implementations are normally used either in a Relational Database or Flat Database or a Not Only SQL Database.

Relational Database Based Design

Either an open source or proprietary RDBMS can be used as a data store, designed appropriately for big data application or a data warehousing application keeping in mind of performance optimization. Additional software tools include an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users. The proper analysis of the enterprise data is called business intelligence. The Big Data requires a dedicated server and cannot share any other application (BI, ETL, OLAP etc) and requires larger processors, main memory which in turn increases the cost.

No-SQL (Not Only SQL):

A NoSQL database provides a mechanism for storage and retrieval of data that uses looser consistency models than traditional relational databases. This offers simplicity of design, horizontal scaling and finer control over availability. NoSQL databases are often highly optimized key–value stores intended for simple retrieval and appending operations, with the goal being significant performance benefits in terms of latency and throughput.

FlatDatabases:

Plain text files such as comma separated values (CSV) usually contain one record per line. There are different conventions for depicting data. These databases are suitable for extremely large databases having millions of terabytes of data. Clients do not need to spend huge infrastructure and operational money to maintain these databases. Example: CSV Text File, Hbase, MongoDB, Casendra etc.

BlueApple has worked on the Microsoft BI platform and Pentaho that offers open source Business Intelligence (BI) products providing data reporting, online analytical processing, analytics, data mining, business performance management, benchmarking, predictive analytics and prescriptive analytics.

BlueApple employs experienced engineers who have expertise in SQL databases very well and we are looking forward to work on these databases. BlueApple has experience in all of the above data storage technology, architecture, design and its respective strengths and limitations. Depending upon a customer requirement, BlueApple can propose the most appropriate solution.

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Between now and 2020, the sheer volume of digital information is predicted to increase to 35 trillion gigabytes – much of it coming from new sources including blogs, social media, internet search, and sensor networks.

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