10 Most Popular Analytic Tools In Big Data

Many online analytics tools can be used to perform data analytics. Let us know some of the most frequently used analytics.

Analytic tools in big data: With the growing influence of technology in our lives, the use and demand to track and manage a large set of data is increasing at a rapid pace. In the present scenario, most of the big firms are using data to generate business and analyse market trends.

Data tracking is gaining popularity and hence collecting raw data and extracting useful information from them is the need of the present.

What are Analytic Tools in Big Data?

A company having the right tools and technologies to track and analyse a big set of data properly can keep up with the rapid pace of technologies and elevate its growth rate.

Analytics tools in big data help to organise these big data in a preferred format to make the right decision. To accelerate your business growth or be consistent you need to use the right analytic tools in big data. Let us have a sneak peek into some of the popular analytic tools in big data.

Why use Analytic tools in big data?

Data analytics is a crucial activity to help businesses make the right and improved decisions based on their business-related tasks. Analytics tools in big data help to organise these big data in a preferred format to make the right decision.

The knowledge of data analytics and the right tools can help in extracting important insights and making informed decisions based on big and complex data. Analytic tools are specifically designed to handle large datasets and derive important insights and information. The best use of data analytics is to predict trends and patterns within data. This real-time analytics can help in enhancing strategic planning. Businesses can thrive by using the right analytic tools in big data.

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Top 10 Analytic Tools in Big Data 2024

Data analytics is performed using various tools available online which helps in extracting important insights and predictions based on the big data. Some popular analytic tools in big data are mentioned below.

1. Apache Hadoop

This tool is one of the most crucial tools used for data analytics. Apache Hadoop is a Java-based open-source platform that is used for data analytics.

Analytic Tools in Big Data 2024
Type of tools	Java based data analytic tool
Availability	Open Source platform
Support	Multiple Platform support such as Amazon, Microsoft, IBM, etc.
Applications	Can be used for storing and processing large data for businesses. The HDFS is fast and quick acess. Easy to use MySQL, JSON, etc. Can be used for parallel processing. It can implement data warehousing and ETL (Extract, Transform, Load) It can also integrate machine learning and data science tools and technologies.

2. Cassandra

Cassandra is an open-source-distributed database that is used to fetch large amounts of data. It is one of the important data analytic tools. It can handle a large set of data with effective speed and performance.

Analytic Tools in Big Data 2024
Type of tool	Java based data analytic tool
Availability	Open Source platform
Support	Multiple Platform support such as Amazon, Microsoft, IBM, etc.
Applications	Data storage Flexibility Big data and analytics Internet of Things (IoT) Fast Processing Content Management System E-Commerce Social Media and Messaging Fault tolerance

3. Qubole

Qubole is a cloud-based platform that is used for data analytics services. The tool is used to manage, explore, and process big data in the cloud. It is an open-source platform that is used to derive the information stored inside the data.

Analytic Tools in Big Data 2024
Type of tool	Cloud Based data analytic tool
Availability	Open Source platform
Support	Multiple Platform support such as Amazon, Microsoft, IBM, etc.
Applications	Data Analytics Data warehousing Machine learning workloads Batch processing Real-time analytics IoT data analytics

4. Spark

Spark is a powerful data analytic tool that is used to process large data. It can easily integrate with AI and machine learning algorithms today.

Analytic Tools in Big Data 2024
Type of tool	Java Based data analytic tool
Availability	Open Source platform
Support	Multiple Platform support such as Amazon, Microsoft, IBM, etc.
Applications	Data Analytics Data warehousing Machine learning workloads Batch processing Real-time analytics IoT data analytics

6. MongoDB

It is a free open source non-relational (NoSQL) database tool which is used to manipulate and store large data in a structured manner. It can support multiple languages such as Python, Jscript, Ruby, etc. It is one of the popular NoSQL tools among developers.

Analytic Tools in Big Data 2024
Type of tool	NoSQL database
Availability	Open Source platform
Support	Multiple languages such as Python, Javascript, Jscript, Ruby, etc.
Applications	Maintain data model Content Management System (CMS) Real-time big data analytics Mobile applications Customer Relationship Management Internet of Things (IoT) Platforms

7. Apache Storm

It is an open-source platform that is used to process large amounts of data in real-time. It provides a platform to prepare scalable and fault-resistant applications.

Analytic Tools in Big Data 2024
Type of tool	Java-based data analytic tools
Availability	Open Source platform
Support	Multiple Platforms such as Amazon, IBM, Microsoft, etc.
Applications	Suitable for applications with low latency Real time processing Scalable and parallel processing Fraud detection Predictive Analytics Supply Chain Optimization

8. Statistics Analytic System

SAS is the platform that helps to perform data analysis on datasets. It uses various statistical techniques, and data visualisation tools to perform analysis.

Analytic Tools in Big Data 2024
Type of tool	Static analytic tools
Availability	Open Source platform
Support	Multiple Platforms support
Applications	Business Analytics Market Research Medical Research Environmental and social science Quality Control and Manufacturing Psychology and Behavioral sciences

9. Rapid Miner

Rapid Miner provides an environment for data preparation, machine learning, deep learning, predictive analytics, and text mining. It is used to make extractions of insights easier which can be accessed easily by all users.

Analytic Tools in Big Data 2024
Type of tool	Data Science Platform
Availability	Open Source platform
Applications	Customer analytics Predictive Analytics Financial Fraud Detection Supply Chain Optimization Sentiment Analysis Human Resource Analytics

10. Data Pine

Data Pine is used for data analytics in many major business firms all around the world. It is used to perform data analytics, data extraction, and processing of data based on customers’ requirements. It consists of the predictive tool with the option of integrating different add-on tools such as visual analytic tools, widgets, etc.

Analytic Tools in Big Data 2024
Type of tool	Analytic tools for big data
Availability	Open Source platform
Applications	It is used to explore, monitor, and visualise the data available or shared.

Also Read: 3D Data Visualization Tools, Online and Examples

Important Steps to Perform Data Analytics

The four major steps to perform data analytics are given below.

1. Identify business problem

The first major process in data analytics is to analyse the right problem and objectives. Some major questions which can be used to define and analyse your goal are as follows.

What is the need of the company?
What are the problems that can be solved?
What is the role of data in approaching the solution?
What is the type of data required?
What are the methods and tools required to solve the problem?
How to share the data output?

2. Collect and Store data

Now, it is important to store the collected data properly by carefully classifying and labelling it. Data must be separated based on their types such as company data, machine data, or open data. There are two major types of data, quantitative data and qualitative data.

The data must be collected based on the goal of the business. Different types of techniques can be used to store and collect data effectively.

3. Clear and Prepare Data

After collecting and storing data it must be cleared and processed well before moving ahead. Nowadays, quality is more important to extract crucial information. Raw, unfiltered data or incomplete data can deliver wrong information or output. Some important things must be kept in mind while clearing data.

Redundancy must be avoided. There must not be any duplicate rows, columns, or cells.
Null values must be carefully handled.
Managing extra-large values is important.
Avoiding unclear, incomplete data for processing.

4. Analyze Data

In this step, data must be analysed to find patterns, relations, and connections to derive important predictions and insights from the data. It is the work of a data scientist expert. There are different data analysing techniques available.

5. Data Visualization

Communicating the output based on effective data visualisation techniques is important. Data visualisation is an important step to convert the data output into a visual context that can be displayed to the users. Some major data visualisation packages in Python are Matplotlib, Bokeh, Seaborn, Plotly, etc.

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Analytic Tools in Big Data FAQs

What are big data analytic tools?

Analytic tools are specifically designed to handle large datasets and derive important insights and information. The best use of data analytics is to predict trends and patterns within data.

Which is the best data analytic tool?

Some of the best data analytic tools are Apache Hadoop, Cassandra, Qubole, MongoDB, Apache Spark, etc. Read the above complete article to have a good understanding.