Tools that use for Big data

Tools that use for Big data

Big Data is a term that directly indicates a huge amount of data which can be exceeding Terabytes in size. This large and complex data set is difficult to process using traditional applications or tools. Big data always brings a number of challenges with its volume and complexity. Most of the time, the real world data generate without any proper structure. Now the challenge is how we are going to store these unstructured data and analyse it to improve our daily life.

Today, there have thousands of tools which can be used in big data but, not all are efficient and it also takes a lot of time to find a perfect tool. To save your valuable time, we set up a list of top big data tools.

Let’s take a look at our list -

NoSQL and MongoDB

NoSQL (Not Only SQL) databases can handle unstructured data and store with no particular schema which is very common in big data management. NoSQL database like MongoDB gives better performance in storing a massive amount of data. It is a good resource to manage data that is frequently changing or data that is semi-structured or unstructured. Most commonly, it is used for store data in mobile apps, product catalogs, real-time personalization, content management, and applications that deliver a single view across multiple systems.


HPCC Systems platform is set of easy-to-use software features enabling developers and data scientists to process and analyze data at any scale. It belongs to the open source community, the HPCC Systems platform is available free of licensing and service costs. It supports SOAP, XML, HTTP, REST and JSON. This system can store file part replicas on multiple nodes to protect against disk or node failures. It has administrative tools for environment configuration, job monitoring, system performance management, distributed file system management, and more. It’s highly efficient and flexible.


Apache Storm is free and open source distributed real-time computation system. Storm makes easy to reliably process unbounded streams of data, doing for real-time processing. Storm can be used with any programming language. Storm can be used in many cases such as - online machine learning, continuous computation, distributed RPC, ETL, and more. The storm is fast, scalable, fault-tolerant, guarantees your data will be processed and is easy to set up and operate.


Hadoop is an open-source software framework for distributed storage of large datasets on computer clusters. It is designed to scale up from single servers to thousands of machines. Hadoop provides large amounts of storage for all sorts of data along with the ability to handle virtually limitless concurrent jobs or tasks. It offers robust ecosystem that is well suited to meet the analytical needs of the developer. It brings Flexibility In Data Processing and allows for faster data Processing. But it’s not for the data beginner.


OpenRefine is a powerful big data tool for working with messy data by cleaning, transforming formats, and extending with web services and external data. OpenRefine is an open source tool. It is pretty user-friendly that can help you to explore large data sets with easily and quickly even the data is unstructured. And you can ask questions to the community if you get stuck. They are very helpful and patient. You can also check out their Github repository.


Cloudera is the fastest, easiest and highly secure modern big data platform. It allows anyone to get any data from any environment within single, scalable platform. It is free and an open-source project to store large amount data and can access better the stored data. Cloudera is mostly an enterprise solution to help manage a business. It will also deliver a certain amount of data security, which is highly important if you’re storing any sensitive or personal data.


Talend is the leading open source integration software provider to data-driven enterprises. Talend connects at big data scale, 5x faster and at 1/5th the cost. Talend offers a number of data products. It's Master Data Management offering real-time data, applications, and process integration with embedded data quality and stewardship.


It facilitates managing and querying large datasets residing in the distributed storage. Apache Hive can help with querying by using HiveQL – and SQL-like language and managing large datasets real fast. It offers Java Database Connectivity interface.


NodeXL is free and open-source network analysis and visualization software. It provides exact calculations. It is one of the best statistical tools for data analysis which includes advanced network metrics, access to social media network data importers, and automation.


KNIME helps you to manipulate, analyze, and model data through visual programming. It is used to integrate various components for data mining and machine learning.


The Tableau platform is a recognized leader in the analytics market and is a good option for non-data scientists working in enterprises, across any sector. A big benefit that users find from Tableau is the ability to reuse existing skills, in the Big Data context. Tableau makes use of a standardized SQL (Structured Query Language) to query and interface with Big Data systems, making it possible for organizations to make use of existing database and analyst skills sets to find the insights they are looking for, from a large data set. Tableau also integrates its own in-memory data engine called "Hyper" enabling fast data lookup and analysis.


It’s a distributed database that is high-performing and deployed to handle mass chunks of data on commodity servers. Cassandra offers no space for failure and is one of the most reliable Big Data tools. It was first developed by the social media giant Facebook as a NoSQL solution.


Spark is the next hype in the industry among the big data tools. It can handle both batch data and real-time data. As Spark does in-memory data processing, it processes data much faster than traditional disk processing. This is indeed a plus point for data analysts handling certain types of data to achieve a faster outcome. Spark is flexible to work with HDFS as well as with other data stores. It’s also quite easy to run Spark on a single local system to make development and testing easier.


SAMOA stands for Scalable Advanced Massive Online Analysis. It is an open source platform for big data stream mining and machine learning. It allows you to create distributed streaming machine learning algorithms and run them on multiple DSPEs (distributed stream processing engines). SAMOA’s closest alternative is BigML tool.


Pentaho provides big data tools to extract, prepare and blend data. It offers visualizations and analytics that change the way to run any business. This Big data tool allows turning big data into big insights. It empowers users to architect big data at the source and streams them for accurate analytics. Seamlessly switch or combine data processing with in-cluster execution to get maximum processing. It allows checking data with easy access to analytics, including charts, visualizations, and reporting. It supports a wide spectrum of big data sources by offering unique capabilities.

We use cookies to improve your experience on our site and to show you personalised advertising. Please read our cookie policy and privacy policy.