A few years back, we used to talk about kilobytes and megabytes, but these days, we are talking about terabytes. Today, the world revolves around data. Every organization is extensively using and creating data, and it keeps multiplying every day.
Any data is meaningless until and unless we extract some sort of useful information that can ultimately lead the management in decision making. And for these purposes, simply say, to analyze, store, process a huge amount of data, there are several Big Data Tools available in the market.
Before we proceed to the Big Data tools, it’s important for you to know that the Big Data tools can be categorized into the following categories: data storage, development platforms, development tools, integration tools, analytics, and reporting tools.
Here are the top 7 Big Data tools:
- Apache Cassandra
- Apache Spark
- Apache Storm
- Apache Kafka
Hadoop is one of the most prominent and widely used tools in the Big Data industry. It has the capability of processing data on a large scale. Hadoop is an open-source framework written in Java and runs on commodity hardware in an existing data center.
Features of Hadoop:
- It is highly used in R&D purpose
- It is flexible in the data processing
- It allows faster data processing
- It can hold all types of data: images, video, JSON, XML, and plain text
- It offers a robust ecosystem that is well suited to meet the needs of the developer
Apache Cassandra is an open-source and free, distributed NoSQL database management system designed to handle a large amount of data across multiple servers providing high availability with no single point of failure.
Cassandra is being used by some of the top companies like – eBay, Comcast, Instagram, Intuit, Netflix, Github, and around 1500 more companies that have large and active data sets.
Features of Apache Cassandra:
- No single point of failure – proven fault tolerance
- Handles massive data
- Scalability and high availability
- Low latency
- Easy distribution of data
MongoDB is an open-source NoSQL database, which means it stores data in JSON-like documents. It is a cross-platform that is compatible with many built-in features. It is a general-purpose document-based, distributed database built for modern era application developers.
MongoDB is used by millions of users, including the top companies like – Verizon, Adobe, SAP, SEGA, SquareSpace.
Features of MongoDB:
- It uses dynamic schemas; hence you can quickly prepare data, and it also allows in reducing cost.
- It is flexible in cloud infrastructure.
- It can store multiple types of data: integer, array, string, boolean, object, etc.
- It provides support for multiple platforms and technologies.
Apache Spark is also an open-source distributed general-purpose cluster computing framework. It is also considered to be the successor of Hadoop as it overcomes several drawbacks of it. Apache Spark provides an interface for entire programming clusters with implicit data parallelism and fault tolerance.
Features of Apache Spark:
- It supports in-memory calculations that make it 100 times faster than compared to Hadoop.
- It consists of 80 high-level operators for efficient execution of queries.
- It offers a substantial set of high-level tools including MLlib, GraphX, Spark SQL
- It also provides high-level APIs in Python, Java, Scala, and R.
Apache Storm is also a distributed stream processing computation Big Data tool. It provides a fault-tolerant and distributed real-time processing system. It is simple and can be used by any programming language.
It is written predominantly in the Clojure programming language. It was initially created by Nathan Marz and the team at BackType. It was open-sourced after being acquired by Twitter.
Features of Apache Storm:
- It offers real-time analytics
- It makes it easy to process unbound streams of data reliably.
- It can process one million 100 byte messages per second per node.
- In case a node dies, it automatically restarts, and the work will restart on the other node.
Apache Kafka was created by LinkedIn in 2011. It is an open-source platform that is used for real-time data pipelines and streaming applications. It is fault-tolerant, highly scalable, and runs in production in thousands of companies.
In Apache Kafka, the communication between the client and server takes place with a simple, high-performance language agnostic TCP control.
Features of Apache Kafka:
- It provides high throughput to the systems.
- It can handle trillions of events in a day.
- It offers high-speed streaming and guarantees zero downtime.
HPCC is an open-source data lake platform that stands for High-Performance Computing Cluster. It is another Big Data tool that was developed by the LexisNexis Risk solution. HPCC Systems is a mature platform that has been used in commercial applications for almost two decades.
Features of HPCC:
- It provides batch, real-time and data streaming ingestion
- It offers high redundancy and availability
- It helps in the parallel data processing
- It supports end-to-end Big Data workflow management
To step into the Big Data industry, it is important to be aware of the Big Data tools. It’s always good to start with Hadoop as it is one of the most widely used and easy to use the tool. To learn more about Big Data and its tools, the Big Data course offered by Simplilearn would help you gain an in-depth knowledge of the Big Data framework using Spark and Hadoop. With the Hadoop course, you will also execute real-life industry projects using the integrated lab.