Top 10 Big Data Tools that you should know about

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Big data is simply too large and complex data that cannot be dealt with using traditional data processing methods.

Big Data requires a set of tools and techniques for analysis to gain insights from it.

There are a number of big data tools available in the market such as Hadoop which helps in storing and processing large data, Spark helps in-memory calculation, Storm helps in faster processing of unbounded data, Apache Cassandra provides high availability and scalability of a database, MongoDB provides cross-platform capabilities, so there are different functions of every Big Data tool.

Imagine you being at the top of the game in the field of Big Data and your business on cloud nine, just like Sachin Tendulkar in the game of cricket.

So what can help you shine bright like a diamond in the world of Big Data?

The answer is an excellent set of Big Data tools.

“A Good Tool improves the way you work. A Great Tool improves the way you think”

– Jeff Duntemann, Co-Founder of Coriolis

Analyzing and processing Big Data is not an easy task. Big Data is one big problem and to deal with it you need a set of great big data tools that will not only solve this problem but also help you in producing substantial results.

This blog gives an insight into the Top Big Data Tools available in the market.

What are the best Big Data Tools?

Here is the list of top 10 big data tools –

  • Apache Hadoop
  • Apache Spark
  • Flink
  • Apache Storm
  • Apache Cassandra
  • MongoDB
  • Kafka
  • Tableau
  • RapidMiner
  • R Programming

Big Data is an essential part of almost every organization these days and to get significant results through Big Data Analytics a set of tools is needed at each phase of data processing and analysis.

There are a few factors to be considered while opting for the set of tools i.e., the size of the datasets, pricing of the tool, kind of analysis to be done, and many more.

With the exponential growth of Big Data, the market is also flooded with its various tools. These tools used in big data help in bringing out better cost efficiency and thus increases the speed of analysis.

Let’s discuss these big data tools in detail – 

1. Apache Hadoop

Apache Hadoop is one of the most popularly used tools in the Big Data industry.

Hadoop is an open-source framework from Apache and runs on commodity hardware. It is used to store process and analyze Big Data.

Hadoop is written in Java. Apache Hadoop enables parallel processing of data as it works on multiple machines simultaneously. It uses clustered architecture. A Cluster is a group of systems that are connected via LAN.

It consists of 3 parts-

  • Hadoop Distributed File System (HDFS) – It is the storage layer of Hadoop.
  • Map-Reduce – It is the data processing layer of Hadoop.
  • YARN – It is the resource management layer of Hadoop.

The below GIF will help you to understand the Hadoop architecture easily –

Big Data tool Hadoop Working

Everything that has been developed comes with some disadvantages also. Here are a few about Hadoop-

  • Hadoop does not support real-time processing. It only supports batch processing.
  • Hadoop cannot do in-memory calculations.

2. Apache Spark

Apache Spark can be considered as the successor of Hadoop as it overcomes the drawbacks of it. Spark, unlike Hadoop, supports both real-time as well as batch processing. It is a general-purpose clustering system.

It also supports in-memory calculations, which makes it 100 times faster than Hadoop. This is made possible by reducing the number of read/write operations into the disk.

It provides more flexibility and versatility as compared to Hadoop since it works with different data stores such as HDFS, OpenStack and Apache Cassandra.

It offers high-level APIs in Java, Python, Scala and R. Spark also offers a substantial set of high-level tools including Spark SQL for structured data processing, MLlib for machine learning, GraphX for graph data set processing, and Spark Streaming. It also consists of 80 high-level operators for efficient query execution.

3. Apache Storm

Apache Storm is an open-source big data tool, distributed real-time and fault-tolerant processing system. It efficiently processes unbounded streams of data.

By unbounded streams, we refer to the data that is ever-growing and has a beginning but no defined end.

The biggest advantage of Apache Storm is that it can be used with any of the programming languages and it further supports JSON based protocols.

The processing speed of Storm is very high. It is easily scalable and also fault-tolerant. It is much easier to use.

On the other hand, it guarantees the processing of each data set. It’s processing speed is rapid and a standard observed was as high as a million tuples processed per second on each node.

4.Apache Cassandra

Apache Cassandra is a distributed database that provides high availability and scalability without compromising performance efficiency. It is one of the best big data tools that can accommodate all types of data sets namely structured, semi-structured, and unstructured.

It is the perfect platform for mission-critical data with no single point of failure and provides fault tolerance on both commodity hardware and cloud infrastructure.

Cassandra works quite efficiently under heavy loads. It does not follow master-slave architecture so all nodes have the same role. Apache Cassandra supports the ACID (Atomicity, Consistency, Isolation, and Durability) properties.

5. MongoDB

MongoDB is an open-source data analytics tool, NoSQL database that provides cross-platform capabilities. It is exemplary for a business that needs fast-moving and real-time data for taking decisions.

MongoDB is perfect for those who want data-driven solutions. It is user-friendly as it offers easier installation and maintenance. MongoDB is reliable as well as cost-effective.

It is written in C, C++, and JavaScript. It is one of the most popular databases for Big Data as it facilitates the management of unstructured data or the data that changes frequently.

MongoDB uses dynamic schemas. Hence, you can prepare data quickly. This allows in reducing the overall cost. It executes on MEAN software stack, NET applications and, Java platform. It is also flexible in cloud infrastructure.

But a certain downfall in the processing speed has been noticed for some use-cases.

6. Apache Flink

Apache Flink is an Open-source data analytics tool distributed processing framework for bounded and unbounded data streams. It is written in Java and Scala. It provides high accuracy results even for late-arriving data.

Flink is a stateful and fault-tolerant i.e. it has the ability to recover from faults easily. It provides high-performance efficiency at a large scale, performing on thousands of nodes.

It gives a low-latency, high throughput streaming engine and supports event time and state management.

7. Kafka

Apache Kafka is an open-source platform that was created by LinkedIn in the year 2011.

Apache Kafka is a distributed event processing or streaming platform which provides high throughput to the systems. It is efficient enough to handle trillions of events a day. It is a streaming platform that is highly scalable and also provides great fault tolerance.

The streaming process includes publishing and subscribing to streams of records alike to the messaging systems, storing these records durably, and then processing these records. These records are stored in groups called topics.

Apache Kafka offers high-speed streaming and guarantees zero downtime.

8. Tableau

Tableau is one of the best data visualization and software solution tools in the Business Intelligence industry. It’s a tool that unleashes the power of your data.

It turns your raw data into valuable insights and enhancing the decision-making process of the businesses.

Tableau offers a rapid data analysis process and resulted in visualizations are in the form of interactive dashboards and worksheets.

It works in synchronization with other Big Data tools such as Hadoop.

Tableau offered the capabilities of data blending are best in the market. It provides an efficient real-time analysis.

Tableau is not only bound to the technology industry but is a crucial part of some other industries as well. This software doesn’t require any technical or programming skills to operate.

9. RapidMiner

RapidMiner is a cross-platform tool that provides a robust environment for Data Science, Machine Learning and Data Analytics procedures. It is an integrated platform for the complete Data Science lifecycle starting from data prep to machine learning to predictive model deployment.

It offers various licenses for small, medium, and large proprietary editions. Apparently, it also offers a free edition that permits only 1 logical processor and up to 10,000 data rows.

RapidMiner is an open-source tool that is written in java. RapidMiner offers high efficiency even when integrated with APIs and cloud services. It provides some robust Data Science tools and algorithms.

10. R Programming

R is an open-source programming language and is one of the most comprehensive statistical analysis languages.

It is a multi-paradigm programming language that offers a dynamic development environment. As it is an open-source project and thousands of people have contributed to the development of the R.

R is written in C and Fortran. It is one of the most widely used statistical analysis tools as it provides a vast package ecosystem.

It facilitates the efficient performance of different statistical operations and helps in generating the results of data analysis in graphical as well as text format. The graphics and charting benefits it provides are unmatchable.


These big data tool not only helps you in storing large data but also helps in processing the stored data in a faster way and provides you better results and new ideas for the growth of your business.

There are a vast number of Big Data tools available in the market. You just need to choose the right tool according to the requirements of your project.

Remember, “If you choose the right tool and use it properly, you will create something extraordinary; If used wrong, it makes a mess.”

So make the right choice and thrive in the world of Big Data. DataFlair is always here for your help.

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2 Responses

  1. Anith joseph says:

    Yarn, hive, hbase, python, are equally important tools..

  2. Ayush Shrivastava says:

    Which tools are mostly used by big companies who needs parallel computing as well unbound data, fast computation, faster retrieval, have large data sets. Which tool would you recommend for the tech giants to use?

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