Top 15 Big Data Analytics Tools And Software You Should Know In 2022

Top 15 Big Data Analytics Tools And Software You Should Know In 2022
Top 15 Big Data Analytics Tools And Software You Should Know In 2022

What is Big Data?

Big Data is a massive amount of data sets that cannot be stored, processed, or analyzed using traditional tools. Today, there are millions of data sources that generate data at a very rapid rate. These data sources are present across the world. Some of the largest sources of data are social media platforms and networks. Let’s use Facebook as an example—it generates more than 500 terabytes of data every day. This data includes pictures, videos, messages, and more.

Data also exists in different formats, like structured data, semi-structured data, and unstructured data. For example, in a regular Excel sheet, data is classified as structured data—with a definite format.

What is Big Data Analytics?

Big Data analytics is a process used to extract meaningful insights, such as hidden patterns, unknown correlations, market trends, and customer preferences. Big Data analytics provides various advantages—it can be used for better decision making, preventing fraudulent activities, among other things.

Best Big Data Analytics Tools

Big data analytics tools are solutions that pull data from multiple sources and prepare it for visualization and analysis to discover deeper business insights into trends, patterns and associations within data.

  1. Hadoop: Hadoop helps in storing and analyzing data and is considered to be one of the best tools to handle huge data. It is written in Java and is an open-source framework. Right from plain text, images to videos, Hadoop stands the potential to hold it all.
  1. Talend: Talend is used for data integration and management. Talend is the leading open-source integration software provider to data-driven enterprises. Our customers connect anywhere, at any speed. From ground to cloud and batch to streaming, data or application integration, Talend connects at big data scale, 5x faster and at 1/5th the cost.
  1. Apache Spark: Apache Spark is one of the most powerful open-source big data analytics tools. It is a data processing framework that can quickly possess very large data sets. It can also distribute data processing tasks across multiple computers, either on its own or in conjunction with other distributed computing tools.
  1. MongoDB: MongoDB is a free and open-source data analytics tool that is known to provide support for multiple technologies and platforms. It also supports multiple operating systems including Windows Vista and Linux.
  1. Azure Databricks: Azure Databricks is a unified big data analytics platform that provides data management, machine learning and data science to businesses through integration with Apache Spark.
  1. Pentaho: Pentaho addresses the barriers that block your organization’s ability to get value from all your data. The platform simplifies preparing and blending any data and includes a spectrum of tools to easily analyze, visualize, explore, report and predict.
  2. Apache Cassandra: Apache Cassandra big tech giants like Facebook, Accenture, Yahoo, etc. rely on Cassandra. This is an open-source framework that is known for managing huge data volume in the least possible time.
  1. Microsoft Azure: Microsoft Azure, formerly known as Windows Azure, is a public cloud computing platform handled by Microsoft. It provides a range of services that include computing, analytics, storage, and networking.
  1. Zoho Analytics: Zoho Analytics is a BI and Data analytics software platform that helps its users to visually analyze data, create visualizations, and get a better and in-depth understanding of raw data.
  1. Python: Python right from data cleaning, data modelling, data reporting to building analysis algorithms, Python has got you covered. Python is a relatively easy tool to work on. I addition to being user-friendly, Python is known for its portability.
  1. RapidMiner: RapidMiner much like KNIME, RapidMiner operates through visual programming and is capable of manipulating, analyzing and modeling data. RapidMiner makes data science teams more productive through an open-source platform for data prep, machine learning, and model deployment.
  1. Splunk: Splunk is a great option for a lot of different people. It can handle small, midsized, and large business enterprise data as well as public administrations and nonprofits.
  1. Power BI: Power BI is yet another powerful business analytics solution by Microsoft. Power BI comes in three versions – Desktop, Pro, and Premium.
  2. Alteryx: Alteryx is that one tool that companies can use to discover and analyze the data. Not just that – this data analytics tool helps in finding deeper insights by deploying and sharing the analytics at scale
  1. SiSense: SiSense is a great option that is embraced by a lot of very seasoned business intelligence (BI) tool users because it has so many comprehensive features. It’s a great option for just about all of your needs.

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