Big Data Analytics With Hadoop

June 5, 2019
data analytics

 

Hadoop has been around forever; right from the early days of data analytics and the big data analytics, Hadoop has been an integral part and well-known name in the IT and data analytics industry. Formally known as Apache Hadoop, it is an open source software developed partly in partnership with Apache Software Foundation. 

Today, the software is known across the globe and is used in managing data processing as well as storage for big data applications which run on clustered systems. Hadoop being a well-known name in the data analytics industry is at the center of a dynamic market whose need for big data analytics is constantly increasing. The main factor that contributes to the wide use of Hadoop in data analytics is its ability to handle and manage various applications like predictive analytics, data mining as well as machine learning. 

A feature that distinguishes Hadoop from all other tools available in the market is its ability to handle both structured and unstructured data types, thus giving users increased flexibility for collecting, processing and analyzing big data, which conventional systems like data warehouses and relational databases can’t provide. 

Hadoop and Data Analytics 

As mentioned in the introductory paragraphs, Hadoop is essentially an analytics software for big data and can run on massive clusters of servers, thus providing the user with the ability to support thousands of nodes and humongous amounts of data. Since its inception in the mid-2000s, Hadoop has become an integral part of all data analytics operations mainly because of its significant features like managing nodes in a cluster, fault tolerance capabilities and many more. 

Hadoop due to its wide range of capabilities is a very good fit for any big data analytics application. Due to its capacity to handle any form of data, be it structured or unstructured, Hadoop can handle it all. One of the most notable applications of Hadoop includes its use in customer analytics. With Hadoop, users can predict anything, be it customer churn, analyze click-stream data or analyze and predict the results of an online ad. 

Top Big Data Analytics Tools

Although Hadoop is at the center of big data analytics, there are many notable tools in the market that are definitely worth checking out. Some of the most significant ones are as mentioned below. 

  • R Programming

After Hadoop, R is the leading data analytics tool in the market today. Available in Windows, Mac as well as Linux, R is most commonly used in statistics and data modelling. 

  • Tableau Public

 Tableau Public is an open source, free data analytics tools that have the capability to seamlessly connect data warehouses, Excel or any other source and display all the data on a web-based dashboard with real-time updates. 

  • SAS

SAS is the global leader in data analytics for many years and is widely known for its easy accessibility and manipulation capabilities. 

Conclusion

Hadoop and Big Data Analytics are terms that are synonymous with each other. With Hadoop and the right source, a user can analyze any type of data imaginable. 

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