With its advanced analytical strength, big data has been powering some of the most significant developments across industries today. From equipping organizations with more informed, quicker, and agile decision-making to driving business growth and efficiency, Big data analytics is a fundamental tool for almost every industry today. The need for advanced analytics to track data is surging tremendously with businesses and organizations across industries understanding the transformative power of data and adopting data-driven goals and objectives. 

In this blog, we will discuss the top 10 most popular big data analytics tools with their differentiated features and use cases. If you are someone who works with data analytics and is interested in learning about the best tools for big data analytics, this blog will help you find the best out of the many choices available. 

Top 10 Big Data Analytics Tools

1. APACHE Hadoop

APACHE Hadoop is an open-source Java-based platform that allows the processing and storing of large datasets across clusters of computers by employing a simple programming model. This tool has two major components- HDFS (Hadoop Distributed File System) which enables secure and distributed storage and MapReduce which enables the delivery of complex computation of massive chunks of datasets. APACHE Hadoop is capable of processing structured as well as unstructured data from a single server and scaling up to multiple computers. Users can also avail of cross-platform support in Hadoop. Tech moguls like Microsoft, IBM, Amazon, etc use Hadoop. 

Features 

• It is open-source and allows the running of applications on several nodes
• Quick accessibility through HDFS and offers scalability both vertically and horizontally
• Highly level of Fault tolerance using a replication mechanism
• Very Cost-effective and does not need a license
• Offers faster data processing and data reliability

2. Cassandra

APACHE Cassandra is an open-source NoSQL database that can fetch massive chunks of data. It is popularly known for its high availability and scalability.  Cassandra can handle and process data in all forms- structured, unstructured and Semi-structured. This tool is ideal for mission-critical data. It can withstand data centre outages without loss of data whether on-premise or across public or private clouds, because of its low latency and no master-client architecture. Cassandra can handle petabytes of resources with no to zero downtime and deliver thousands of operations every second. 

Features 

• Offers high flexibility of data storage supporting data in all forms
• Fault Tolerance that allows replacement for any failure of nodes 
• Quick data processing and storage 
• Easy distribution of data by replicating data on several data centres

3. Spark

APACHE Spark is another top most popular big data analytics tool that can handle and process colossal amounts of datasets in semi-structured and structured formats. It is open-source and can be used for processing data and several tasks on a large scale. Its user-friendly APIs offer easy methods of data-pulling and can handle multi-petabytes of data. Spark uses in-memory computing for data processing and features several libraries for AI/ML applications, stream processing and SQL queries. It is a multi-language, unified engine analytics of large-scale datasets. 

Features 

• Open-source framework and performs advanced analytics
• Performs quick distributed SQL queries for dashboarding and ad-hoc reporting
• Real-time streaming of data processing
• Executes EDA (Exploratory Data Analysis) on petabyte scale
• Trains ML algorithms and scale to fault-tolerant clusters to several machines by leveraging the same cod

4. Qubole

This is another popular big data analytics tool that can fetch data in a value chain by employing machine and ad-hoc analysis. It is also an open-source and cost-efficient data lake ideal for Machine learning, streaming analytics, ad-hoc analysis and data exploration. Qubole performs end-to-end service in less time and effort, essential for moving data pipelines. It can set up multi-cloud services including Google Cloud, Azure and AWS. Qubole is known for reducing cloud computing costs by 50%. 

Features 

• Supports Extract-Transport-Load (ETL) process and allows transporting data from several sources in one place
• Offers real-time insights by monitoring user’s systems
• Offers predictive analysis 
• Protect user data with an advanced security system

5. Xplenty

Highly ideal for building a data pipeline with minimal codes. Businesses can rely on this tool for their sales, marketing and support. This tool also supports ETL- Extract, Transport and Load through its iterative graphical interface. It is cost-effective with a nominal cost for hardware and software. Users can process data over the cloud for analytics.

Features 

• Rest API for executing any task
• Offers flexibility and security of data 
• Offers integration apps for on-premise or on-cloud 

6. MongoDB

It is a document-oriented NoSQL database and open-source platform that can be employed for storing and processing high volumes of data in documents rather than tables and rows. It is popular among developers and Data Analytics experts for its scalability, high availability and flexible data model.  It is a document database that allows storing of structured and unstructured data for developers. 

Features 

• It is written in C++ and capable of holding multiple documents
• Easy storing of files without disturbances in the stack
• Can replicate the master data and the user can call back for a backup

7. SAS

This again is one of the most preferred and popular Big data analytics tools which is great for creating statistical modeling. SAS offers data analysts a great platform for mining, managing, extracting and updating data from multiple sources in a variety of formats. Statistical Analytical System offers innovative analytics for actionable insights. 

Features 

• Flexible programming language  with vast libraries and easy-to-learn syntax 
• Offers end-to-end security called SAS Secure
• Offers support for numerous programming languages including SQL

8. PowerBI 

Offering numerous features like dashboards, data modelling and reporting PowerBI is also one of the top data analytics tools. It integrates Microsoft’s ecosystem which makes it possible for users to analyse and visualize large datasets. 

Features

• Data reporting and visualisation
• Data transformation and Modelling
• Sharing and Collaboration

9. Tableau

Another tool that allows data analysis and visualization where users can make interactive dashboards. Tableau offers high-level insights into critical KPIs by integrating multiple data sources. It’s a free online platform that is ideal for data exploration and communication.

Features

• Data visualization and connectivity
• Supports viewing on different devices
• Sharing reports and collaboration

10. Qlik Sense

Best for running ad-hoc queries and applying advanced techniques for analyzing large datasets. The tool allows users to build visualizations, monitor data in real-time