10 Best Big Data Analytics Tools

Introduction

Have you wondered what is big data analysis, and why is it important? Big data is a collection of huge datasets produced by enterprises around the world. E.g., Flight black box data, stock exchange data, search engine data, etc. This raw data is useful only if we can draw meaningful information from it. Big data analytics is a process of analyzing the data computationally to reveal patterns, trends, statistical information, and performing large calculations. For this purpose, there are several big data analytics tools available in the market.

List of big data analytics Tools

Enlisted below are the 10 best big data analytics tools.

1. Xplenty
2. Microsoft HDInsight
3. Zoho Analytics
4. Skytree
5. Talend
6. Splice Machine
7. Spark
8. Plotly
9. Apache SAMOA (Scalable Advanced Massive Online Analysis)
10. R – Programming

1. Xplenty

It is a cloud-based ETL platform that helps to clean and transform data without any coding or deployment. It helps to integrate data from many wide ranges of sources and destinations into a single pipeline. Its data integration platform has a variety of tools to perform tasks like scheduling jobs, track job progress, sample data outputs, and execute both UPI and API. 

2. Microsoft HDInsight

Azure HDInsight is a cloud distribution of Hadoop components from the Hortonworks Data Platform (HDP). This can be used for applications in data warehousing, ETL, machine learning, and IoT. It has all the benefits that come from cloud computing like SLAs and the worldwide availability of a variety of clusters. It serves practical business purpose facilitates all ids Security and compliance is also taken care off. 

3. Zoho Analytics

It is a business intelligence application essentially for dashboarding and visual analysis. It can help in ranking reports, what-if analysis, executive dashboards, interactive reports. It also has a robust suite of data connectors to pull data from various resources.

4. Skytree

It is another tool that moves analytics to the next level. It is leading the way when it comes to machine learning. It facilitates the automation of building of most accurate machine learning models. Its provides speed and scalability. The platform can be accessed from an intuitive GUI or programmatic interface (CLI or Python). It also has built-in artificial intelligence for data scientists.

5. Talend

It is one of the most powerful data integration tool available in the market. It is developed in the Eclipse Graphical Development Environment. It was named as the leader in Gartner’s Magic Quadrant for Data Integrity and Data Quality Tools 2019. It aims to deliver compliant, accessible, and clean data. It maintains data quality, provides big data integration, cloud API services, provides data catalogue, and stich data loader. It comes with 5 products that differ in their functionality and pricing

• Talend Open Source
• Stitch Data loader
• Talend Pipeline Designer
• Talend Cloud data integration
• Talend Data Fabric

6. Splice Machine

It is a single platform that performs a function of a transactional database, analytical data warehouse, with native machine learning. Its scalable, real-time, AI-powered and easily used. Access to real-time data, full petabyte-scale analytical power, ML libraries, and speed provides data scientists with opportunities to build accurate models.

7. Spark

It is a very successful project in the Apache Software Foundation. It is an open-source cluster computing framework for real-time processing. Spark can access the data quickly and accelerate the speed of analytics. It supports multiple languages and allows the developers to write applications in Java, Python, R, and Scala. Spark has a rich source of SQL queries, machine learning algorithms, and complex analytics. Spark interface also provides fault tolerance and implicit data parallelism. Spark runs on Kubernetes, Apache Mesos, standalone Hadoop, or in the cloud.

8. Plotly

It is a tool for building visualizations. It aids the users to create dashboards and interactive charts that can be shared online. It an open-source plotting library for Python that has varied chart types for financial, statistical, geographic, and scientific purposes. This tool creates graphs efficiently, and they can be embedded and downloaded. 

9. Apache SAMOA (Scalable Advanced Massive Online Analysis)

It is an open-source data stream mining platform. The framework helps in developing new distributed stream mining algorithms and deploy those on a variety of stream processing engines. Users also get a library of machine learning algorithms. It has a WORA (Write Once Run Anywhere) architecture. It is fast, scalable, and simple to use. 

10. R – Programming

R is an open-source programming language used for data sciences. It is widely used by data miners for statistical analysis, graphics, data modelling, reporting, and machine learning algorithm. Its biggest advantages are its built-in functionality via R packages. It provides unmatched graphical and statistical capabilities, which makes it user-friendly.

Conclusion

Data analysis is the process of collecting, cleaning, and transforming data into useful information. To support big data operations, there are a variety of big data analytics tools available in the market. Some of the big data analytics tools are open source tools, and some are paid tools. Users need to choose the tools wisely as per their project needs.

Big data analysts are at the vanguard of the journey towards an ever more data-centric world. Being powerful intellectual resources, companies are going the extra mile to hire and retain them. You too can come on board, and take this journey with our Big Data Specialization course.

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