Top 20 Big Data Tools Used By Professionals in 2025

Introduction

In our fast-paced tech world, data is surging at an incredible rate—around 2.5 quintillion bytes daily. Yet, this data needs organization to be useful. This is where big data comes into the picture. Businesses must gather valuable insights from this vast information ocean, and that’s where the right data analytics tools and skilled data analysts come in. By transforming raw data into meaningful patterns, companies can refine their strategies and stay ahead in the game. Big data tools are of great help when it comes to organizing quintles of data. In this article, we will explore top 20 big data softwares.

List of Top 20 Big Data Tools

1. Hadoop
2. Spark
3. NoSQL databases (MongoDB, Cassandra)
4. SQL databases (MySQL, PostgreSQL)
5. Hive
6. Pig
7. Flink
8. Kafka
9. HBase
10. Presto
11. Elasticsearch
12. Splunk
13. Tableau
14. Power BI
15. Talend
16. Apache NiFi
17. TensorFlow
18. RapidMiner
19. KNIME
20. DataRobot

Top 10 Open Source Big Data Softwares

Open source big data tools are software solutions that are freely available to the public, allowing anyone to use, modify, and distribute them. These tools enable organizations to handle and analyze massive amounts of data efficiently. Some popular open source big data tools include:

Hadoop

An open-source framework for storing and processing big data. It provides a distributed file system called Hadoop Distributed File System (HDFS) and a computational framework called MapReduce. HDFS is designed to store and manage large amounts of data across a cluster of commodity hardware. MapReduce is a programming model used to process and analyze large datasets in parallel. Hadoop is highly scalable and fault-tolerant, making it suitable for processing massive datasets in a distributed environment.

Pros

• Scalable and flexible data storage
• Cost-effective solution for processing big data
• Supports a wide range of data processing tools

Cons

• Complex setup and administration
• Performance limitations for real-time data processing
• Limited security features

Spark

An open-source data processing engine for big data analytics. It provides an in-memory computational engine processing large datasets 100 times faster than Hadoop’s MapReduce. Spark’s programming model is based on Resilient Distributed Datasets (RDDs), distributed data collections that can be processed in parallel. Spark supports various programming languages, including Python, Java, and Scala, making it easier for developers to write big data applications. Spark’s core APIs include Spark SQL, Spark Streaming, MLlib, and GraphX, which provide functionality for SQL queries, stream processing, machine learning, and graph processing.

Pros

• Fast and efficient data processing
• Supports real-time data streaming and batch processing
• Interoperable with other big data tools such as Hadoop and Hive

Cons

• High memory requirements for large datasets
• Complex setup and configuration
• Limited machine learning capabilities compared to other tools

Flink

An open-source data processing framework for real-time and batch processing. Flink provides a streaming dataflow engine to process continuous data streams in real time. Unlike other stream processing engines that process streams as a sequence of small batches, Flink processes streams as a continuous flow of events. Flink’s stream processing model is based on data streams and stateful stream processing, which enables developers to write complex event processing pipelines. Flink also supports batch processing and can process large datasets using the same API.

Pros

• Real-time data processing capabilities
• Efficient event-driven processing
• Scalable and fault-tolerant

Cons

• The steep learning curve for new users
• Limited support for some big data use cases
• Performance limitations for extensive datasets

Hive

An open-source data warehousing tool for managing big data. It manages large datasets stored in Hadoop’s HDFS or other compatible file systems using SQL-like queries called HiveQL. HiveQL is similar to SQL, making it easier for SQL users to work with big data stored in Hadoop. Hive translates HiveQL queries into MapReduce jobs, which are executed on a Hadoop cluster.

Pros

• Supports SQL-like queries for data analysis
• Interoperable with other big data tools
• Scalable and efficient data warehousing solution

Cons

• Performance limitations for real-time data processing
• Limited support for advanced analytics and machine learning
• Complex setup and administration

Storm

An open-source real-time data processing system for handling big data streams. It was developed at BackType and later open-sourced. Storm processes data streams in real-time, making it ideal for use cases where data must be processed and analyzed as it is generated. A storm is highly scalable and can be easily deployed on a cluster of commodity servers, making it well-suited for big data processing. Storm also provides reliability through its use of a “master node” that oversees the processing of data streams, automatically re-routing data to other nodes in the event of a failure.

Pros

• Real-time data processing capabilities
• Scalable and fault-tolerant
• Supports a wide range of data sources

Cons

• Complex setup and configuration
• Limited support for batch processing
• Performance limitations for huge datasets

Cassandra

An open-source NoSQL database for handling big data. It was initially developed at Facebook and was later open-sourced. Cassandra is designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. It uses a peer-to-peer architecture, which allows it to scale horizontally and easily handle increasing amounts of data and traffic. Cassandra also provides tunable consistency, meaning clients can choose the consistency they need for a particular operation.

Pros

• High availability and scalability
• Supports real-time data processing
• Efficient handling of large amounts of unstructured data

Cons

• Complex setup and administration
• Limited support for advanced analytics
• Performance limitations for enormous datasets

Zookeeper

An open-source tool for managing the coordination of distributed systems. It was initially developed at Yahoo! and later open-sourced. ZooKeeper provides a centralized repository for distributed systems configuration information, naming, and synchronization services. It also provides a simple, distributed way to coordinate tasks across a cluster of servers, making it well-suited for large-scale distributed systems. ZooKeeper is known for its reliability and fault tolerance, as it uses a “quorum” system to ensure that the system’s state remains consistent, even in the event of a node failure.

Pros

• Provides coordination and management for distributed systems
• Scalable and fault-tolerant
• Supports a wide range of use cases

Cons

• Complex setup and administration
• Performance limitations for vast datasets
• Limited security features

Mahout

An open-source machine learning library for big data analysis. It was created to make it easier for developers to use advanced machine learning algorithms on large amounts of data. Mahout provides a library of algorithms for tasks such as recommendation systems, classification, clustering, and collaborative filtering. It is built on top of Apache Hadoop, allowing it to scale to handle enormous amounts of data, making it well-suited for big data processing. Mahout also provides a simple, user-friendly API for integrating algorithms into applications, making it accessible to many developers and organizations. Mahout helps organizations derive insights from their data and make better data-driven decisions by providing scalable machine learning algorithms.

Pros

• Supports a wide range of machine learning algorithms
• Interoperable with other big data tools
• Scalable and efficient data analysis

Cons

• Limited support for deep learning and neural networks
• The steep learning curve for new users
• Performance limitations for huge datasets

Pig

An open-source platform for data analysis and manipulation of big data. It was created to make it easier for developers to process and analyze large amounts of data. Pig provides a simple scripting language called Pig Latin, allowing developers to write complex data processing tasks concisely and easily. Pig translates Pig Latin scripts into a series of MapReduce jobs that can be executed on a Hadoop cluster, allowing it to scale to handle substantial amounts of data. This makes Pig well-suited for use in big data processing and analysis.

Pros

• Supports data analysis and manipulation using a high-level programming language
• Interoperable with other big data tools
• Scalable and efficient data processing

Cons

• Performance limitations for real-time data processing
• Limited support for advanced analytics and machine learning
• The steep learning curve for new users

HBase

An open-source NoSQL database for handling big data, especially unstructured data. It is a column-oriented database that provides real-time, random access to big data. HBase is designed to handle huge amounts of data, scaling to billions of rows and millions of columns. It uses a distributed architecture, allowing it to scale horizontally across many commodity servers and provide high availability with no single point of failure. HBase also provides strong consistency, ensuring that data is always up-to-date and accurate, even in the face of node failures. This makes HBase well-suited for use cases requiring real-time data access and strong consistency, such as online gaming, financial services, and geospatial data analysis.

Pros

• Supports real-time data processing and retrieval
• Scalable and efficient handling of large amounts of unstructured data
• Interoperable with other big data tools

Cons

• Complex setup and administration
• Limited support for advanced analytics
• Performance limitations for enormous datasets

Top 10 Closed Source Big Data Tools

Closed source big data tools are proprietary software solutions developed and maintained by specific companies. Unlike open source tools, these tools are not freely available for the public to use, modify, or distribute. Instead, users typically need to purchase licenses or subscriptions to access and use these tools. Some examples of closed source big data tools include:

Cloudera

Cloudera is a prominent name in the field of big data management and analytics. With its comprehensive suite of software and services, Cloudera empowers organizations to efficiently store, process, and analyze vast amounts of data. It provides scalable solutions for data engineering, data warehousing, machine learning, and more, enabling businesses to derive valuable insights.

Pros

• Offers a wide range of tools for complete big data management.
• Can handle large data volumes, suitable for growing needs.
• Efficiently manages structured data storage.
• Supports AI and machine learning for insights.
• Robust encryption and access controls.
• Professional assistance and training available.

Cons

• Learning curve due to extensive ecosystem.
• Higher licensing and subscription expenses.
• Requires substantial hardware and expertise.
• Time-consuming integration with existing systems.
• May not be as cloud-integrated as some solutions.
• Frequent updates can cause compatibility issues.

MapR

MapR is a distributed data platform designed to manage, process, and analyze large-scale data. It offers integrated data analytics, real-time event streaming, and AI capabilities, making it suitable for a variety of big data applications.

Pros

• Combines data storage, processing, and analytics in one platform.
• Supports real-time analytics for quicker insights.
• High availability and data protection with no single point of failure.
• Easily scales to handle increasing data volumes.
• Built-in support for machine learning and AI applications.
• Enables real-time event-driven architectures.

Cons

• Setup and management can be challenging.
• Licensing and resource expenses can be high.
• Requires expertise to fully utilize its features.
• Smaller community compared to other big data tools.
• Frequent updates may lead to compatibility challenges.
• Integration with certain cloud platforms can be limited.

Databricks

Databricks is a unified analytics platform designed for big data processing and machine learning. Built on Apache Spark, it offers collaborative features for data engineering, data science, and analytics.

Pros

• Integrates data processing, analytics, and machine learning in one platform.
• Easily scales to handle large datasets and workloads.
• Enables seamless teamwork with interactive notebooks and dashboards.
• Provides managed infrastructure, reducing operational overhead.
• Supports integration with various data sources and third-party tools.
• Offers built-in libraries for machine learning tasks.

Cons

• Can be expensive, especially for larger deployments.
• Requires familiarity with Spark and related technologies.
• Proprietary features might limit flexibility.
• Some customization may be restricted in the managed environment.
• Cloud-based, reliant on stable internet connectivity.
• Support quality and frequency of updates can vary.

IBM BigInsights

IBM BigInsights is an enterprise-grade big data platform that incorporates Apache Hadoop and other open-source technologies. It provides tools for data storage, processing, and analytics.

Pros

• Scales to handle massive volumes of data efficiently.
• Offers integration with various data sources and analytics tools.
• Provides robust security features for data protection.
• Supports advanced analytics and machine learning.
• Enables data exploration through SQL and other query languages.
• Can be customized to fit specific business needs.

Cons

• Can be complex to set up and manage.
• Requires expertise in Hadoop ecosystem technologies.
• Licensing and support costs can be significant.
• Requires regular updates and maintenance.
• Performance may vary based on cluster configuration.
• Proprietary features could lead to vendor dependency.

Microsoft HDInsight

Microsoft HDInsight, a cloud-based big data platform within Microsoft Azure, empowers organizations to process and analyze vast datasets. Leveraging open-source frameworks, HDInsight offers scalable clusters and seamless integration with Azure services. It simplifies complex data tasks and facilitates data-driven decision-making through cloud agility and analytics capabilities.

Pros

• Integrates well with other Azure services and Microsoft products.
• Simplifies deployment and management tasks.
• Easily scales resources up or down based on requirements.
• Offers robust security features and compliance options.
• Provides user-friendly interfaces and development tools.
• Supports hybrid cloud scenarios for data processing.

Cons

• Some customization options may be restricted.
• Tied to the Azure ecosystem and Microsoft technologies.
• Costs can accumulate based on resource usage.
• Performance might be influenced by cloud infrastructure.
• Learning Azure-specific concepts may be required.
• Relies on stable internet connectivity for operations.

Talend

Talend is an open-source big data integration platform that facilitates data extraction, transformation, and loading (ETL) tasks. It supports various data sources and offers an intuitive graphical interface.

Pros

• User-friendly interface.
• Supports various data sources.
• Extensive data transformation capabilities.

Cons

• Learning curve for complex tasks.
• Limited advanced analytics features.
• Some features require paid versions.

SAP HANA

SAP HANA is an in-memory database platform that accelerates data processing and analytics. It provides real-time insights by storing data in memory and offers advanced analytics capabilities.

Pros

• In-memory processing for fast analytics.
• Real-time data insights.
• Integration with other SAP solutions.

Cons

• High implementation and maintenance costs.
• Requires specialized skills for administration.
• Limited support for non-SAP applications.

Informatica Big Data Edition

Informatica Big Data Edition is a comprehensive solution for data integration and management, designed to handle large-scale data processing and analytics.

Pros

• Seamlessly integrates with various data sources and systems.
• Scales to manage large volumes of data efficiently.
• Offers data cleansing and enrichment features.
• Supports advanced analytics and machine learning.
• Provides data governance and security features.

Cons

• Setup and configuration can be complex.
• Requires familiarity with Informatica tools and concepts.
• Licensing and deployment costs can be high for smaller organizations.
• Handling extremely large datasets might impact performance.
• Integrating with certain specialized platforms might require additional efforts.

Oracle Big Data Appliance

Oracle Big Data Appliance is a comprehensive and integrated solution designed for processing and analyzing large volumes of diverse data. It combines hardware and software components to provide a unified platform that facilitates efficient data management, analytics, and integration with other Oracle software products.

Pros

• Provides a pre-configured hardware and software stack for easy deployment.
• Scales to accommodate growing data needs.
• Supports a wide range of data types, including structured and unstructured data.
• Offers built-in security features for data protection.
• Seamlessly integrates with Oracle Database and other Oracle software.

Cons:

• Initial investment and licensing costs can be high.
• Setup and configuration might require specialized expertise.
• Integration with non-Oracle tools and technologies could be challenging.
• Performance bottlenecks can occur with improper configuration.
• Managing and optimizing the appliance may require specialized skills.

Teradata Vantage

Teradata Vantage is an advanced analytics platform that brings together powerful data processing and analytics capabilities. It enables businesses to efficiently manage and analyze large datasets, leveraging its integrated ecosystem for data warehousing, machine learning, and data lake capabilities, thus providing comprehensive insights for informed decision-making.

Pros

• Integrates data warehousing, advanced analytics, and data lakes, offering a comprehensive analytics solution.
• Scales effortlessly to handle massive datasets and complex analytical workloads.
• Provides built-in machine learning and AI capabilities for predictive and prescriptive analytics.

Cons

• Licensing and infrastructure costs can be high, making it more suitable for larger enterprises.
• Requires expertise to fully leverage its capabilities, potentially posing a learning curve for new users.
• Demands skilled administrators and resources for optimal performance and maintenance.

How Much Do Big Data Engineers Earn?

The salary of a Big Data Engineer can vary widely based on factors such as location, company, and experience. On average, Big Data Engineers in the United States can earn between $100,000 and $150,000 annually, with top earners making over $180,000 annually.

In India, the average salary for a Big Data Engineer is around INR 8,00,000 to INR 15,00,000 per year. However, salaries can vary greatly based on factors such as the company, location, and experience.

It’s important to note that salaries in the technology industry can be high, but the demand for skilled Big Data Engineers is also high. So, it can be a lucrative career option for those with the right skills and experience.

Roadmap to Learn Big Data Technologies

To learn big data, here is a possible roadmap:

Learn Programming 

A programming language like Python, Java, or Scala is essential for working with big data. Python is popular in the data science community because of its simplicity, while Java and Scala are commonly used in big data platforms like Hadoop and Spark. Start with the basics of programming, such as variables, data types, control structures, and functions. Then learn how to use libraries for data manipulation, analysis, and visualization.

Learn SQL

SQL is the language used for querying and managing big data in relational databases. It’s important to learn SQL to work with large datasets stored in databases like MySQL, PostgreSQL, or Oracle. Learn how to write basic queries, manipulate data, join tables, and aggregate data.

Checkout: Top 10 SL Projects from Beginner to Advance Level

Understand Hadoop

Hadoop is a big open-source data processing framework that provides a distributed file system (HDFS) and a MapReduce engine to process data in parallel. Learn about its architecture, components, and how it works. You’ll also need to learn how to install and configure Hadoop on your system.

Learn Spark 

Apache Spark is a popular big data processing engine faster than Hadoop’s MapReduce engine. Learn how to use Spark to process data, build big data applications, and perform machine learning tasks. You must learn the Spark programming model, data structures, and APIs.

Learn NoSQL Databases 

NoSQL databases like MongoDB, Cassandra, and HBase store unstructured and semi-structured data in big data applications. Learn about their data models, query languages, and how to use them to store and retrieve data.

Learn Data Visualization

Data visualization presents data in a visual format, such as charts, graphs, or maps. Learn to use data visualization tools like Tableau, Power BI, or D3.js to present data effectively. You’ll need to learn how to create easy-to-understand, interactive, and engaging visualizations.

Learn Machine Learning

Machine learning analyzes big data and extracts insights. Learn about machine learning algorithms, including regression, clustering, and classification. You’ll also need to learn to use machine learning libraries like Scikit-learn, TensorFlow, and Keras.

Checkout our Free Course on Introduction to ML and AI

Practice with Big Data Projects

To become proficient in big data, practice is essential. Work on big data projects that involve processing and analyzing large datasets. You can start by downloading public datasets or by creating your own datasets. Try to build end-to-end big data applications, from data acquisition to data processing, storage, analysis, and visualization.

Other than this, you may have a look at the following things also:

1. Ways to deal with semi-structured data with High volumes.
2. Utilizing ETL Pipelines to deploy our system on Cloud Like Azure, GCP, AWS, etc.
3. How can data mining concepts be used to prepare interactive dashboards and make a complete ecosystem?
4. The efficiency of Batch processing vs. Stream Processing in Big Data Analytics or Business Intelligence.

Remember that big data is a vast field; this is just a basic roadmap. Keep learning and exploring to become proficient in big data.

To learn more about Big Data Technologies from senior people, you may refer to archives of Analytics Vidhya for Data Engineers.

Conclusion

In conclusion, using Big Data tools has become increasingly important for organizations of all sizes and across various industries. The tools listed in this article represent some of the most widely used and well-regarded Big Data softwares among professionals in 2025. Whether you’re looking for open-source or closed-source solutions, there is a Big Data tool out there that can meet your needs. The key is carefully evaluating your requirements and choosing a tool that best fits your use case and budget. With the right Big Data tool, organizations can derive valuable insights from their data, make informed decisions, and stay ahead of the competition.

To learn all the mentioned technologies related to big data in a more structured and concise manner, you can refer to the following courses or programs by Analytics Vidhya by experienced people. After learning, you may be hired by organizations like Deloitte, PayPal, KPMG, Meesho, paisaBazzar, etc. Checkout Analytics Vidhya Courses to Master Big Data Tools and Technologies

Frequently Asked Questions

Chirag Goyal

I, Chirag Goyal, am a Content Editor Intern in Analytics Vidhya. I am currently pursuing my Bachelor of Technology (B.Tech) in Computer Science and Engineering from the Indian Institute of Technology Jodhpur(IITJ). I am very enthusiastic about Machine learning, Deep Learning, and Artificial Intelligence.