14 best big data analytics tools and software of 2023 mbahguru.co.id

Big data analytics tools play a critical role in analyzing, processing, and storing vast amounts of data generated by organizations and individuals. With the exponential growth of data, the need for advanced big data analytics tools has become increasingly important. In this article, we will take a closer look at the 14 most popular big data analytics tools that are widely used today.

1. Apache Hadoop

Apache Hadoop is an open-source Java-based platform that is used to process and store big data. This platform offers efficient and parallel data processing, thanks to its cluster system. Data can be processed by multiple structured and unstructured computers, making it accessible across multiple platforms. Tech giants like Amazon, Microsoft, IBM, and others use Apache Hadoop as one of the best big data analysis tools.

Features:

• Free storage solution for businesses
• Efficient and can be installed on commodity hardware
• Hadoop Distributed File System (HDFS) provides quick access to data
• Scales up to handle large amounts of data
• Easy to implement with MySQL, JSON, and highly flexible

2. Apache Cassandra

Apache Cassandra is a distributed database with a NoSQL engine that is used to retrieve large data sets. This big data tool has been praised for its high availability, scalability, and speed, making it a popular choice among tech companies. It can handle petabytes of data with minimal downtime and delivers thousands of operations every second. Apache Cassandra was created in 2008 by Facebook.

Features:

• Quick and efficient data storage on commodity hardware
• Supports structured, semi-structured, and unstructured data
• Easy data replication across multiple data centers
• Immediate node replacement in case of failure

3. Qubole

Qubole is a big data analytics tool that uses ad-hoc analysis in machine learning to fetch data from the value chain. It offers end-to-end services to move data pipelines with reduced time and effort. Qubole supports Azure, AWS, and Google Cloud services, reducing cloud computing costs by 50%.

Features:

• Predictive analysis to target more acquisitions
• Multi-source data migration to one location
• Real-time system monitoring and insights

4. Xplenty

Xplenty is a data pipeline tool that uses minimal code to build pipelines. It offers sales, marketing, and support solutions and features an interactive graphical interface. Xplenty offers cost-effective hardware and software solutions and provides support via chat, email, phone, and virtual meetings. It can process data for big data analysis over the cloud and segregate it using Xplenty.

Features:

• Deploy integrated apps on-premises and in the cloud
• Regular algorithm and certificate verification, along with SSL/TSL encryption
• Supports databases, warehouses, and salesforce

5. Apache Spark

Apache Spark is a big data processing tool that can handle a large scale of data processing and various tasks. Spark’s easy-to-use APIs and ability to handle multi-petabytes of data make it a popular choice among data analysts. It’s also highly suitable for ML and AI, which is why tech giants are increasingly turning to Spark.

Features:

• Users can choose the programming language they want to run
• Spark Streaming handles data streaming

6. MongoDB

MongoDB is a free, open-source, document-oriented (NoSQL) database that is used to store a large amount of structured information. MongoDB’s support for multiple programming languages, such as Jscript, Python, and Ruby, makes it a popular choice among developers.

7. Apache Storm

Apache Storm is a robust and user-friendly big data analytics tool that is designed to handle large amounts of real-time data processing. Small companies, especially those without resources for big data analytics, often use Storm due to its lack of programming language barriers and support for any language. It was designed with fault tolerance and horizontal scalability in mind and is used by many tech giants today, such as NaviSite, Twitter, and Zendesk.

Features:

• Apache Storm provides real-time data processing capabilities, which are essential for many businesses today.
• Its design enables it to handle large amounts of data, making it suitable for both small and large companies.
• Apache Storm supports any programming language, making it accessible to a wide range of users.

8. Amazon Web Services (AWS)

Amazon Web Services (AWS) is a popular cloud-based platform for big data analytics that provides a range of tools and services for storing, processing, and analyzing large amounts of data. It is a cost-effective solution that is suitable for businesses of all sizes, and its scalability and flexibility make it an attractive option for companies that need to process large amounts of data.

Features:

• AWS offers a wide range of big data analytics tools and services, making it a one-stop-shop for many businesses.
• Its scalability and flexibility make it suitable for businesses of all sizes.
• AWS provides cost-effective big data analytics solutions, making it an attractive option for companies on a budget.

9. Apache Nifi

Apache Nifi is an open-source platform that is used for automating and managing the flow of data between systems. It is a powerful tool for processing big data and is suitable for businesses of all sizes. Apache Nifi is known for its ease of use, scalability, and flexibility, making it a popular choice for many businesses that need to process and manage large amounts of data.

Features:

• Apache Nifi is easy to use and provides a visual interface for managing the flow of data.
• Its scalability and flexibility make it suitable for businesses of all sizes.
• Apache Nifi can be used to automate and manage the flow of data between systems, making it a powerful tool for big data analytics.

10. IBM InfoSphere

IBM InfoSphere is a comprehensive big data analytics platform that provides a range of tools and services for storing, processing, and analyzing large amounts of data. It is a cost-effective solution that is suitable for businesses of all sizes, and its scalability and flexibility make it an attractive option for companies that need to process large amounts of data.

Features:

• IBM InfoSphere provides a wide range of big data analytics tools and services, making it a one-stop-shop for many businesses.
• Its scalability and flexibility make it suitable for businesses of all sizes.
• IBM InfoSphere provides cost-effective big data analytics solutions, making it an attractive option for companies on a budget.

11. HPCC Systems

HPCC Systems is an open-source big data analytics platform that provides a range of tools and services for storing, processing, and analyzing large amounts of data. It is a cost-effective solution that is suitable for businesses of all sizes, and its scalability and flexibility make it an attractive option for companies that need to process large amounts of data.

Features:

• HPCC Systems provides a wide range of big data analytics tools and services, making it a one-stop-shop for many businesses.

12. Tableau

Tableau is a powerful data visualization tool that enables users to explore and analyze complex data sets in a visual format. It provides an intuitive interface for users to create interactive charts, graphs, and dashboards, allowing them to easily share their insights with others.

13. Talend

Talend is a comprehensive big data platform that offers a wide range of data integration, data management, and big data services. It streamlines the process of connecting various data sources and automates the data integration process, making it easier for users to manage their data.

14. Power BI

Power BI is a business intelligence tool that allows users to create interactive and visually appealing reports and dashboards. It integrates with a wide range of data sources and offers advanced analytics capabilities, making it a valuable tool for data-driven organizations.

15. KNIME

KNIME is an open-source data analytics platform that enables users to create complex data pipelines and perform advanced data analysis. With its intuitive interface and drag-and-drop functionality, it makes it easy for users to integrate and visualize data from multiple sources.