Top 5 Big Data Tools [Most Used in 2022]

Big Data has become an integral part of any business for improving decision making and gaining a competitive edge over others. Therefore, Big Data technologies, such as Apache Spark and Cassandra are in high demand. Companies are looking for professionals who are skilled in using them to make the most out of the data generated within the organization.

These data tools help in handling huge data sets and identifying patterns and trends within them. So, if you are planning to get into the Big Data industry, you have to equip yourself with these tools. 

We will check out the most popular Big Data technologies in this article.

Big Data Tools & Technologies

1. Apache Storm

Apache Storm is a real-time distributed tool for processing data streams. It is written in Java and Clojure, and can be integrated with any programming language. The software was developed by Nathan Marz and was later acquired by Twitter in 2011. The basic features of Storm are as follows:

• Has massive scalability
• It can process over a million jobs on the node within fractions of seconds
• Real-time data processing
• Storm topology runs until the user shuts it down or an unexpected technical failure occurs
• It guarantees the processing of every tuple
• It can run on JVM (Java Virtual Machine)
• Apache Storm supports (DAG) Direct Acrylic Graph topology
• Being open-source, flexible and robust, it can be used by medium and large-scale organizations
• It has low latency. Performs end-to-end delivery response and data refresh in seconds, depending on the data problem
• Storm guarantees data processing even if the messages are lost or nodes of the cluster die   

The Apache Storm topologies are like a MapReduce job. But, here the data is processed in real-time instead of batch processing in Apache Spark. 

Storm UI daemon offers you a REST API through which you can do the following:

• Interact with the Storm cluster and obtain metrics data
• Start/stop topologies and configure information
• Even if a failure happens, each node is processed at least once

All this make Storm one of the leading Big Data technologies at present.

2. MongoDB

This is an open-source NoSQL database that is an advanced alternative to modern databases. It is a document-oriented database used for storing large volumes of data. Instead of rows and columns used in traditional databases, you will make use of documents and collections.

Documents consist of key-value pairs and the collections have function and document sets. MongoDB is ideal for companies who need to take quick decisions and want to work with real-time data. The Big Data technology is commonly used for storing data obtained from mobile applications, product catalogues and content management systems.

Some of the most popular reasons for getting started with MongoDB are:

• As it stores data in documents, it is very flexible and can be easily adapted by companies
• It supports many ad-hoc queries, such as searching by a field name, regular expressions and range queries. You can execute queries for returning fields in a document
• All fields of a MongoDB document can be indexed for enhancing the quality of searches
• It is great at load balancing as it splits data across MongoDB instances. The technology can run on several servers, and also duplicates data for load balancing in case a technical failure occurs
• You can store data of any type, such as integer, strings, Booleans, arrays and objects
• As this technology uses dynamic schemas, you can store and prepare data quickly, thus saving cost. Learn more about the real time applications of MongoDB.

Read: Big Data Salary in India

3. Cassandra

Cassandra is a distributed database management system that is used for handling large volumes of data across several servers. This is one of the most popular Big Data technologies which is preferred for processing structured data sets. It was first developed by Facebook as a NoSQL solution. It is now used by corporate giants, such as Netflix, Twitter and Cisco.

The most exciting features of Cassandra include:

• It provides an easy to use query language, so it will be hassle-free if you want to transition from a relational database to Cassandra
• Its Masterclass architecture allows data to be read and written on any node
• Data is replicated on different nodes, so there is no single point of failure. Even if a node fails to work, data stored on other nodes will be available for use
• Data can also be replicated across multiple data centres. So, if data is lost or damaged in one data centre, it can be retrieved from other data centres
• It has built-in security features, such as restore mechanisms and data backup
• This tool allows the detection and recovery of failed nodes

Cassandra is now widely used in IoT real world applications where huge streams of data are coming from devices and sensors. It is widely used for social media analytics and while handling customer data.

4. Cloudera

Cloudera is one of the fastest and most secure Big Data technologies out there right now. It was initially developed as an open-source Apache Hadoop distribution that was aimed at enterprise-class deployments. This scalable platform allows you to get data from any environment very easily.

The best features why choosing Cloudera will be great for your project are:

• Offers real-time insights for data monitoring and detection
• You can deploy Cloudera Enterprise across various cloud platforms, such as AWS, Google Cloud and Microsoft Azure
• Cloudera has the capability of developing and training data models
• You can spin or terminate data clusters. This allows you to pay for only what you need and when you require it
• Offers an enterprise-level hybrid cloud solution

Cloudera offers software, support and service in five bundles that are available across multiple cloud providers and on-premise:

• Cloudera Enterprise Data Hub
• Cloudera Analytic DB
• Cloudera Operational DB
• Cloudera Data Science and Engineering 
• Cloudera Essentials

5. OpenRefine

OpenRefine is a powerful Big Data tool that is used for cleaning data and converting it into different formats. You can explore huge data sets using this tool comfortably. The prominent features of this tool are:

• You can extend your data set to various web services
• Import data in different formats 
• Handle cells with multiple data values and perform cell transformations
• You can use Refine Expression Language to perform advanced data operations
• The tool allows you to explore huge data sets easily within a matter of seconds

Also Read: Hadoop Tools to Make Your Big Data Journey Easy

Conclusion

The Big Data technologies discussed here will help any company to increase its profits, understand its customers better and develop quality solutions. And the best part is, you can start learning these technologies from the tutorials and resources available on the Internet.

If you are interested to know more about Big Data, check out our PG Diploma in Software Development Specialization in Big Data program which is designed for working professionals and provides 7+ case studies & projects, covers 14 programming languages & tools, practical hands-on workshops, more than 400 hours of rigorous learning & job placement assistance with top firms.

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