As effective as it is in almost every field today, the term big data has turned into a scourge of sorts that seems to be floating around every corner you turn. People have been so talked out with big data that there is not much left for us to discuss unless of course, we were to discuss the various tools that are employed in processing and analyzing it. There are several platforms, tools, and extensions available today that are meant to serve each and every need.
When it comes to big data, there is no platform or tool that asserts itself as Hadoop does. Of course, the cute little yellow elephant mascot is both symbolic of Hadoop as well as big data in general. An open-source platform for big data, Apache’s Hadoop serves as the go-to place for everything big data, from the most fundamental aspects to more complex processes. With a myriad of different modules to offer, Hadoop sets the bar quite high for versatility. The Hadoop framework itself contains four modules.
Hadoop Common: A module comprised of Java libraries and utilities, Hadoop common supports other Hadoop modules. The libraries of this module contain java files and scripts required to run Hadoop.
HDFS: Hadoop distributed file system or HDFS as it is commonly known is the primary storage system employed in all Hadoop applications.
YARN: Short for Yet Another Resource Negotiator, YARN is Hadoop’s cluster management system. It serves as a buffer between the HDFS’ and the processing engines.
Map Reduce: This is Hadoop’s data conversion module which converts data sets and breaks them down into individual elements called tuples. This is one of the most vital aspects of Hadoop and defining it in two sentences does not do it justice by the least. Perhaps we could cover this topic in detail in the future.
One of the most fundamental tasks when it comes to big data is data sorting or data refining. As a software applied in an underrated aspect of big data, OpenRefine is an open source big data sorting tool that allows you to sort out the most contrived and unstructured of business data. One of the most desirable aspects of this open source tool is its simplicity and the user-friendly nature of its interface which will allow even data newbies to get right into the big data experience. However, as with anything and everything in technology, a little of experience and technology would be most recommended to get the most out of data using Open Refine.
An open source application that runs on Hadoop and NoSQL databases, Talend is a comprehensive tool to harness the best out of big data. It equips the user with various graphics tools and wizards to use Hadoop effectively with data quality, data integration, and management. Furthermore, the simplicity of this application is an added bonus for beginners. Talent offers a visual representation of the data and the processes rather than the source code in a simple drag and drop system, which while desirable to some, could become quite a hassle during certain processes. Talend also offers TalendForge, a collection of open source extensions to support and enhance the experience with various products offered by them.
When it comes to non-relational databases, there is none like Mongo for big data. A document-oriented, cross-platform database, Mongo comes with full index support and a great deal of flexibility. Horizontal Scalability and third-party log tool support are some of the additional features that Mongo offers. This database also comes with a replication facility called the Replica Set which provides automatic data failover and data redundancy.
Yet another data visualization tool for data analysis and other big data related processes, Tableau is quite an impressive piece of technology with a twist. It offers a new perspective of the data by allowing the user to slice it up into bits and even merging it with other data to gain yet another perspective. Tableau harnesses the power of both Apache’s Hadoop as well as Hive to offer an immersive and interactive process.