15 Best Big Data Tools That You Need to Check Out Today!

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15 Best Big Data Tools That You Need to Check Out Today!

Big Data is a large amount of data collected in real-time in various formats and structures. Latest technologies have simplified data gathering from multiple sources. Data warehouses and data lakes can store this data on-premises or on the cloud.

However, the collected data is of no use to the business until it is analyzed. Basic data analytics tools like MS Excel cannot process Big Data due to the excess volume and complex nature of data. Big Data needs tools designed explicitly for the purpose. 

Big Data Analytics is a type of advanced analytics where statistical algorithms, what-if models, and predictive analysis are used to identify the patterns, trends, and correlations between different elements. 

What Are Big Data Tools? 

Big Data tool is a software used to clean, format, and process vast data in real-time. It is an analytical system capable of understanding complicated information and deriving actionable insights from it. Big Data tools help enterprises make data-driven decisions and increase returns.  

Why Do We Need Big Data Tools?

The US economy faces around $3.1 trillion yearly losses due to poor data quality. The losses can be minimized by adopting a data-driven model and investing in the right Big Data tools. 

Organizations have begun understanding the importance of Big Data Analytics tools and technology. An Executive Survey report by New Vantage says that 97.2% of enterprises are investing in Big Data and artificial intelligence

Big Data tools can help businesses with the following: 

  • Reducing the cost of investment 
  • Making faster and better decisions
  • Investing in R&D for new products and services 
  • Increasing customer satisfaction
  • Streamlining supply chain and logistics 
  • Improving sales and marketing pipelines

Picking the right Big Data tools for the business is crucial. The accuracy of Big Data analytics and derived insights depends on the tools used for the process. In this blog, our expert talks about the best Big Data analytics tools preferred by numerous enterprises from around the globe. There are numerous tools available in the market. However, our list has been compiled based on the data and usage details collected from enterprises.

The Best Tools for Big Data Analytics

1. Apache Hadoop

Apache Hadoop is one of the best open-source Big Data analytics tools in the market. It’s written in Java and is used to handle clustered file systems through the MapReduce programming model. Hadoop is cross-platform software used by more than half of the Fortune 50 companies. 

  • The HDFS (Hadoop Distributed File System) can store data in all formats and structures in the same file system. 
  • Hadoop is highly scalable software that delivers efficient results in a single server and multiple servers. 
  • The software allows for faster and flexible data processing. 
  • It can be used for free under the Apache License. 
  • Hadoop is robust and a perfect Big Data tool to process Big Data from a cluster of devices. 
  • Be careful to prevent excessive use of disk space due to data redundancy. 

2. Apache Storm

Apache Storm is another open-source Big Data tool that offers the best real-time processing capabilities. The Storm has cross-platform abilities and provides distributed stream processing. It’s written in Java and Clojure and is fault-tolerant. 

  • Storm can process one million 100-byte messages per second per node. 
  • It is fast, reliable, and scalable. 
  • Data processing is guaranteed with Apache Storm. Every unit of data is processed at least once. 
  • The processing will restart automatically on another node if the current node dies. 
  • Storm can run parallel calculations across thousands of computers.
  • It can be used for ETL, real-time processing, continuous computation, and log processing. 
  • Strom can be a little different to understand, though it is one of the easiest software tools to use once deployed.

3. Atlas.ti

Atlas.ti is known as a comprehensive all-in-one software for research. It is used to research markets, understand user experience, and help with academic research and qualitative analytics. The software is available in two versions- desktop for on-premises use and web version for cloud applications. 

  • Atlas.ti can be integrated with data for processing and analytics. 
  • Export data across the devices and machines.
  • It is creates network diagrams and data visualizations in the desktop versions. 
  • Atlas.ti codes and analyzes huge amounts of transcripts/ notes/ research data. 
  • It is easy to understand and use in an enterprise and provides full support to the R&D department. 

4. Tableau

Tableau falls in the category of leading tools for Big Data visualization and is available in three versions- Tableau Desktop, Tableau Server, and Tableau Online for cloud solutions. The open-source version of the software is known as Tableau Public. The data visualization tool works with data of all sizes and formats and provides real-time reports through the interactive dashboard. 

  • Tableau is flexible, scalable, and works on multiple platforms, including mobile devices.
  • Many Big Data consulting companies like DataToBiz are partners of Tableau and offer offshore data analytics and visualization services. 
  • There’s no need to code or use a programming language to work on Tableau. 
  • The templates are easy to use and can be customized to create reports in countless formats. 
  • The tool offers an array of features to bridge the gap between data and employees/ management. 

5. Apache Cassandra

Apache Cassandra is a free, open-source software that deals with vast volumes of data on several servers connected to one another. The NoSQL DBMS uses CQL (Cassandra Structure Language) to share information with the databases in the enterprise. Low latency is one of the significant advantages of using Cassandra. 

  • The tool allows for linear scalability with an increase in data volume and requirements. 
  • Data replication is automated across numerous data centers in the enterprise for fault tolerance.
  • It has a simple ring structure and can effortlessly handle huge loads of data. 
  • There’s no single point of failure when using Cassandra. Even when the systems and data centers are down, you won’t lose the data.

6. Rapidminer

Rapidminer is an open-source Big Data analytics tool that SMEs and large enterprises alike can use. It’s a perfect choice to use with data science models, predictive analytics, and new data mining models in the business. Rapidminer helps with data preparation, implementing machine learning, and deploying models. 

  • It comes with Java core and allows cross-platform integrations.
  • Rapidminer works with APIs and cloud systems just as effectively.
  • The tool allows you to choose multiple data processing methods to analyze the data.
  • Choose between GUI or batch processing, depending on your requirements. 
  • Interactive dashboards and an easy interface make Rapidminer a worthy Big Data tool even for remote analytics. 

7. Knime

Knime is Konstanz Information Miner, open source Big Data software used for analytics, reporting, and data integration. The tool helps integrate machine learning and data mining models. Knime is the best choice for research, BI, CRM, etc. It has a rich algorithm set and is still easy to use in the enterprise. It is a free tool that comes with GNU General Public License. 

  • It is one of the simple yet highly effective Big Data ETL tools (Extract Transform Load tool).
  • It works with other systems and several languages through seamless integration. 
  • Manual and repetitive work is automated by using Knime. It saves time and resources. 
  • The tool is known for its stability and ability to organize workflows within the enterprise.

8. MongoDB

MongoDB is written in C, C++, and JavaScript. It is a NoSQL and document-oriented database that works with multiple operating systems. It is a free open-source Big Data tool that processes massive amounts of data and develops file systems for storage. 

  • MongoDB has been designed to work with modern data applications. 
  • It is a cost-effective tool with reliable features and services. 
  • MongoDB is perfect for those who want Big Data analytical tools that are easy to install, use, and maintain over time. 
  • It is suitable to store structured and unstructured data and can be quickly scaled to meet the increasing demands of the enterprise. 

9. Cloudera

If you’re looking for quick and secure data platforms, Cloudera is the answer. Cloudera is free and open-source software that works with any data environment and encompasses Apache Hadoop, Spark, Impala, etc. Data collection, processing, managing, modeling, and distribution are easily performed using Cloudera. 

  • It can help develop and train data models in the enterprise for Big Data analytics.
  • It delivers real-time insights and reports that are used to monitor and detect changes in the business.
  • Cloudera is a multi-cloud software app and delivers high performance. 
  • Data security is the biggest advantage of using this software tool. 
  • It allows for a node-based subscription where you pay only for what you use but can be slightly expensive as well. 

10. Oracle Data Miner

Oracle Data Miner is used by data scientists for business and data analytics. It provides the easy drag and drop feature to make changes to the editor interface and customize the reports. The Big Data tool is an extension of the Oracle SQL Developer and deals with graphical workflows. 

  • The software documents machine learning methodologies used in the enterprise. 
  • It supports the development and deployment of various ML models and increases the speed of workflow. 
  • The software is secure and scalable to suit the growing demands of the enterprise. 
  • Oracle Data Miner works with Big Data SQL to gather data from several data sources, including Apache Hadoop. 

11. Apache Samoa

Apache Samoa stands for Scalable Advanced Massive Online Analysis and is an open-source software tool used for data mining and machine learning. It is a well-known platform that allows data stream mining of Big Data. Data classification, clustering, regression, and development of new ML algorithms can be performed using Apache Samoa.

  • Samoa allows you to run ML algorithms on multiple engines.
  • It is a simple, efficient, and easy-to-use platform for various businesses. 
  • The software is known for real-time streaming and fast results. 
  • Samoa is scalable and user-friendly. It is a pluggable Write Once Run Anywhere (WORA) architecture.

12. Apache Spark

Apache Spark is an open-source Big Data analytics tool that deals with machine learning and cluster computing. Spark has gained fame for being a lightning-fast analytics engine that can process massive amounts of Big Data with the utmost ease. 

  • The software is written in Java, Python, R, and Scala so that it can be run on various platforms. 
  • It supports data streaming, SQL queries, machine learning, etc.
  • Spark allows you to combine the various elements of its huge library on a single workflow. 
  • Another advantage of using Apache Spark is that it can read data from all other Apache tools and platforms. 

13. Apache Kafka

Apache Kafka is a publish-subscribe messaging system that sends messages from one endpoint to another. It works online and offline and prevents data loss by replicating the messages on disk storage and within the cluster. Apache works seamlessly with Spark and Storm to process and distribute Big Data analytics within the enterprise. 

  • It is scalable software that can easily manage high volumes of data streams. 
  • It is reliable and fault-tolerant. 
  • Kafka is stable and delivers great performance by sending volumes of messages without a single glitch or error. 
  • It is known for its zero-downtime feature and super fast services. 

14. Apache CouchDB

Apache CouchDB is an open-source, document-oriented NoSQL database with cross-platform abilities. It stores data in JSON documents and responds to JavaScript queries. Fault tolerance and the ability to run a single logical database on numerous servers are the two advantages of using Apache CouchDB. 

  • Inserting, updating, deleting, and regulating documents are simple and easy on CouchDB.
  • The documents can be translated into multiple languages.
  • Indexing and retrieving data is fast and efficient using CouchDB. 
  • The main purpose of this Big Data tool is to run queries and create reports from files stored in the database. 
  • The software tool supports mobile devices and works with Android and iOS platforms. 

15. Apache Hive

Apache Hive is an open-source cross-platform data warehousing tool used to facilitate data summarization and analytics in large volumes. It is fast and assists in managing large datasets with ease. Apache Hive manages data stored in other Apache systems such as Hadoop, Hbase, etc. It accepts input C questions and runs the analytics on a cluster to deliver the answer to the query. 

  • The query language for data modeling is similar to SQL. 
  • Java or Python can be used to define the tasks that need to be performed by Hive. 
  • It is a stable framework that handles batch processing and is built on HDFS. 
  • Hive doubles up as a data warehouse and supports Tez, Apache Spark, MapReduce computing engines. 


Big Data management and analytics tools have been developed to handle Big Data operations in an enterprise. To summarize, big data management is useful for a retrospective analysis of enterprise data and its operations. It’s vital to consider factors like flexibility, scalability, licensing rights, cost of investment, and maintenance before choosing a suitable software tool. 

Check out the trial versions of the software to get a better idea. You can work with Big Data solution providers to improve the quality and accuracy of data analytics and adopt the data-driven model in the enterprise. It helps optimize resources, increase productivity, and speed up returns. 

Originally appeared on GlobalTechOutlook.com

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