Big data frameworks allow businesses to get insights from data that helps in decision-making. Check out the list of the top 5 open source big data tools.
Big data analytics software becomes an essential part of businesses because of large amounts of data. Data is meaningless until you process it and get useful information from it. Big data frameworks help companies with big data processing. In this article, we will focus on the following top 5 open source big data tools.
Hadoop is a robust, reliable, and scalable open source big data tool. It has three main components such as HDFS (High Distributed File System), MapReduce, and YARN. NameNodes and DataNodes are the two types of nodes that make up HDFS’ storage layer. NameNode stores the metadata about a block’s location. DataNodes store the block and submit block reports to NameNode after a certain amount of time has passed. The Map phase and the Reduce phase are the two stages of the MapReduce processing layer. It’s designed to handle data that’s distributed through several nodes at the same time. YARN is the work scheduling and resource management layer in Hadoop big data.
Following are the key features of Hadoop:
- Faster data processing
- Distributed processing
- Fault tolerance
- Reliable and scalable
- Easy to use and cost-effective
Apache Storm is an open source real-time data processing tool. It’s an easy-to-use big data processing platform that can be used with any programming language. It is viable to both small and large companies. It is highly scalable and, by adding resources in a linear fashion, can sustain performance even as the load grows. Hadoop processes data in batches, while Apache Storm processes data streams in real time. Apache Storm can be used for existing queuing and database technologies. It is written in Java and all the source code is available at GitHub.
Apache Storm comes up with the following important features:
- Real-time data processing
- Fast and reliable
- Highly scalable and parallelizable
- Use with any language
- Integrate with queuing and database systems
It is a free and open source big data processing engine. Apache Spark is built on Hadoop MapReduce. Apache Spark extends the Hadoop MapReduce model to allow for more types of computations to be done more efficiently, such as interactive queries and stream processing. It supports in-memory cluster computing functionality that increases the processing speed of an application. Additionally, Apache Spark is capable of handling a wide range of workloads, including iterative algorithms, collaborative queries, and streaming. Fault tolerance, advanced analytics, lazy evaluation, real-time stream processing, in-memory data processing, and several other features are included out of the box. It is written in Java, Scala and comes with all the documentation regarding development and deployment. Therefore, all the source code is available at GitHub.
Apache Spark offers the following key points:
- Real-time stream processing
- Support multiple languages
- Integrated with Hadoop
- Advanced analytics
- In-memory computing
Cassandra is a free and open source distributed NoSQL database. It can handle massive amounts of data and one of the best NoSQL databases for big data. Apache Cassandra is a big data database that is highly scalable, high-performance, and highly accessible. Allows for the management of large amounts of data distributed through many servers. It works similarly to relational databases in that it organizes data into rows and columns. The Cassandra Query Language (CQL) is a SQL-like query language.
Apache Cassandra supports the following important features:
- Fast linear-scale performance
- Flexible data storage
- Fast writes
- Elastic scalability
TDengine is an open source big data tool. It is a free big data platform for the Internet of things (IoT). It is highly scalable, reliable, and high-performance software for big data processing. TDengine has zero management and you can quickly install and run it. It offers functionality such as caching, stream computing, message queuing, and many more to reduce operating costs. TDengine can be easily integrated with other tools without a single line of code including Telegraf, Grafana, Matlab, R MQTT, OPC, Hadoop, Spark, and many more. All the source code is available at GitHub.
TDengine comes up with the following key features:
- Powerful data analysis
- Support integration with other tools
- 10x Faster on Insert/Query Speeds
- Full Stack for Time-Series Data
- Consume less computing resources
We have discussed the top 5 open source big data tools in this tutorial. We have covered important features for every big data processing tool. You can also visit links under Explore section for the detailed information. Hope this guide helps you choose the right free big data tool for your needs.
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