Big Data is a big problem and handling it is a hefty task. To cope up with this problem and to get maximum benefits out of it, a set of good Big Data Analytic tools is required.
Here’s list of some of the most popular Big Data Analytic tools:
🚀 Apache Hadoop
Hadoop is one of the oldest and the most popularly used Big Data Analytic tools in the industry. It is an open-source framework developed by Apache. It stores and processes Big Data on the commodity hardware. it allows parallel processing of data using clustered architecture. It is written in Java.
🌟 Apache Spark
Apache Spark was developed so as to eradicate the drawbacks of Hadoop. It not only supports batch processing but also has the capability of processing data in real-time.. It provides an in-memory computing capability, making it faster than Hadoop for certain types of analytics tasks. Spark supports multiple programming languages and offers libraries for machine learning, graph processing, and streaming data. It has some high-level APIs in Java, Python, Scala and R.
☕️ Apache Flink
Apache Flink is again an open-source framework for Big Data Analytics. It also processes data in the form of streams, both bounded and unbounded.It provides event time processing, stateful computations, and fault tolerance, making it suitable for real-time analytics on streaming data.
🐆 Apache Storm
Apache Storm is also an open-source and distributed real-time computation system for processing Big Data. It is designed to process large volumes of streaming data and provides reliable, scalable, and fault-tolerant stream processing capabilities. It processes the data in the form of streams. Storm was originally developed by Twitter and later donated to the Apache Software Foundation.
🌸 Apache Kafka
It is also an open-source Big Data Analytics platform developed by LinkedIn. Kafka is a distributed streaming platform that is commonly used for building real-time data pipelines and streaming applications. It can handle high-throughput, fault-tolerant, and scalable data streaming, making it useful for processing and analyzing streaming data.It functions like a traditional messaging system by publishing and subscribing to the stream records.
MongoDB is a popular NoSQL database that is often used for big data analytics. It is a document-oriented database that provides high scalability and flexibility for handling large volumes of data. MongoDB allows for the storage and retrieval of unstructured and semi-structured data, making it suitable for handling diverse data types in big data environments. It offers powerful querying and indexing capabilities, and its distributed architecture enables horizontal scaling across multiple servers. MongoDB also supports data replication and high availability, ensuring data durability and reliability.
Tableau is a leading data visualization and business intelligence tool used for big data analytics. It provides a user-friendly interface that allows users to create interactive and visually appealing dashboards, reports, and charts. Tableau can connect to various data sources, including big data platforms, databases, spreadsheets, and cloud services, allowing for seamless integration and analysis of large datasets. It offers advanced analytics features such as data blending, calculated fields, and statistical modeling. Tableau also supports real-time data analytics and collaboration, enabling users to share and collaborate on visualizations and insights.
Elasticsearch is a distributed search and analytics engine that is often used for real-time data analysis and full-text search. It can handle large volumes of data and provides powerful querying capabilities, making it useful for big data analytics.
💡 Apache Cassandra
Apache Cassandra is a distributed NoSQL database that is designed for high scalability and fault-tolerance. It can handle large amounts of data across multiple nodes in a cluster and provides low-latency data access. Cassandra is often used in big data analytics applications that require high throughput and high availability.
Elasticsearch is a real-time distributed search and analytics engine that is often used in big data analytics workflows. It is capable of indexing and searching large volumes of data quickly and efficiently. Elasticsearch supports full-text search, aggregations, and analytics capabilities, making it useful for extracting insights from big data.
These are just a few examples of the many tools available for big data analytics. The choice of tools depends on specific use cases, requirements, and the expertise of the analytics team. It’s important to assess the features, scalability, performance, and compatibility of each tool to determine the best fit for a particular big data analytics project.