MapR gives single view of big data

Big Data is everywhere. If not for Big Data, these utterly complex and seemingly impossible applications that run today would never have taken birth. Imagine Google without Big data management, it would be a clutter of unimaginable mess.

What is Big Data?

Big data literally means huge volumes of data. When you have petabytes of data to deal with you need a trusted data management architecture and other tools to make the data access and querying easy.

One of the ways to handle this humongous data is to distribute it to several parallel DBMS systems. This solution doesn’t quite cut it. We also need to segregate the data into relevant sets and arrange them in proper sizes. This way when we need to query something from the database the servers can serve them without much delay.

What is MapR and What does it offer?

MapR has been striving to unite the data and then serve their customers from that single pool of data. We all know how important data security is and when you keep all your data in single pool there are high chances of losing everything at a time. By introducing the newest addition to its family, the MapR converged data platform 6.0, MapR has once again gave hope for better Big data storing and processing capabilities.

With access to many open source project and Apache ecosystems it is possible to upgrade your systems at your own pace and still not worry about what would happen if you don’t update overnight.

It allows data processing tools like Hadoop, Apache Drill, Spark and others to easily integrate with the user’s ecosystem without any hazel. It also supports commercial engines and tools like SAP, MySql, Vertica and even cloud services.

With its extended support for all kinds of data processing frameworks and tools MapR is truly making a huge difference in Big Data.

What are the problems faced during the data processing stage?

For all convenience purposes the problems or the hurdles faced in big data processing is divided into 3 V’s

  1. Volume

Larger the volume harder it is to distribute to several DBMS systems. Apache Hadoop framework one of the most popular way to deal with this problem. MapR package supports almost all open source Apache tools including Hadoop.

  1. Velocity

The processing difficulty is also increased by the speed at which the data accumulates in the servers. If you take astronomical data sets the velocity at which the bulk of data arrives is insanely high and to make sure we don’t run out of server space, we generally prefer cloud architecture where the data size is unknown.

  1. Variety

We have a large pool of distinguished data, however it the data is not segregated properly and stored we might face problems bigger than anticipated. For any data query or even predictive analysis unique data sets are needed. Big Data management ecosystems like MapR include related tools in their packages to take care of these problems.

MapR gives the solutions based on real-time data. Also, it is the top-rated data management ecosystem according to many sources. IBM Big Data and Microsoft Azure are very popular and well known Big Data management systems.

 

BI Consultant

About the Author

BI Consultant

DataFactZ is a professional services company that provides consulting and implementation expertise to solve the complex data issues facing many organizations in the modern business environment. As a highly specialized system and data integration company, we are uniquely focused on solving complex data issues in the data warehousing and business intelligence markets.

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