With a massive amount of data collected by organizations across various sectors, it has become quite significant for these organizations to utilize the data collected efficiently. Right utilization of the data can help in making better business decisions and gain insights into the results of strategies adopted by organizations. This massive amount of data that aids in making strategic business decisions is known as big data. Big data comprises of structured as well as unstructured data. Thus, organizations that want to do big data analytics need to use tools that can efficiently huge amounts of varied datasets. Here are some of the popular data analytics tools being used by many organizations:

Presto
Initially developed by Facebook, the SQL-based database query engine Presto became an open-sourced big data analytics tool in 2013. It is being used by Netflix, Airbnb, and Teradata. Presto has the ability to handle petabytes of data. With one of the highest speeds of data retrieval, Presto can extract data from multiple data sources. Using Presto, you can run analytics across all the data systems of the organization.

Apache Hadoop
One of the most popular big data analytics tools, Apache Hadoop can process huge amounts of data through a distributed system. This open source software has been developed using Java.

PolyBase
PolyBase is a big data analytics tool used to analyze relational as well as non-relational data. This is especially useful while analyzing and querying data obtained from systems that use Hadoop, Azure Data Lake Store, and Azure Blob Storage. Some of the biggest advantages of using PolyBase include flexible storage options, scalable performance management, and enterprise security.

Hive
This big data analysis tool is run on top of Hadoop. This tool is used for the management of distributed data for Hadoop. It uses HSQL to access big data across systems. HSQL or HiveSQL is a query language similar to SQL. If an organization is required to do data mining, they can use Hive along with Hadoop.

NoSQL
Big data is mostly made of unstructured data. To query and analyze this unstructured data, NoSQL or Not Only SQL can be used. With a better performance than most databases, NoSQL can store a huge amount of data. With a number of open source NoSQL databases available, an organization can build their own versions on top of the existing NoSQL databases.

Big data in EXCEL
Using EXCEL 2013, it is possible to connect with the data stored in Hadoop. You can access and analyze big data through Hortonworks’ Enterprise Apache Hadoop platform. In EXCEL 2013, the Power View feature can be used to summarize and analyze big data.