Cassandra vs Hadoop

What is Cassandra? What is Hadoop? Both Cassandra and Hadoop are names that often come up when dealing with big data analytics. Hadoop has a storage part named Hadoop Distributed File System (HDFS) that allows datasets to be processed faster and more efficiently than with parallel file systems. But Hadoop is not the only option when working with big data. For example, there is Cassandra, which is a renowned NoSQL DBMS for real-time applications. Below, we are going to contrast Cassandra and Hadoop, and see the differences.

What is Hadoop?
Hadoop is an open-source software framework that is used for distributed storage and for processing big data. It is an entire ecosystem with integrated distributed computing tools. It focuses on near-time and batch-oriented analytics on historical data. It comprises of four fundamental components, which are Hadoop Distributed File System (HDFS), which splits data into small files that are replicated on multiple servers for fault tolerance constraints; MapReduce, a programming paradigm for handling and processing large data sets by splitting requests into smaller requests that are sent to multiple smaller servers to be processed in parallel; Common/Core, a package that contains Hadoop’s libraries and utilities; and YARN, a resource management platform for managing resources and scheduling tasks. Due to the nature, Hadoop excels in more batch-oriented analytics. (Read also : Hadoop vs NoSQL)

What is Cassandra?
On the other hand, Cassandra is a free, open-source distributed NoSQL database management system designed to handle large amounts of data with high availability and literally no single point of failure. It uses an eventual consistency model instead of the immediate consistency model so that data are always ready although not fully synced yet. With no single point of failure, Cassandra ensures that data can always be provided. Thus, Cassandra makes an excellent choice for handling and processing big data in real-time. Modern web, mobile, and IoT applications would require such real-time analytics capability. You can see another comparison between MongoDB vs Cassandra.

Complementing Hadoop and Cassandra
Since Hadoop and Cassandra are actually two different things, they can be used to complement each other.Hadoop has its own database management system, but using Cassandra on it can make an environment that can handle both near-time and real-time data. As a matter of fact, Cassandra has Hadoop integration, complete with the MapReduce support. By using both, you can rely on Hadoop’s superiority in handling cold, batch-oriented data and also Cassandra’s prowess in handling the hot real-time and online data.

- Free, open-source distributed NoSQLDBMS- Open-source software framework with integrated distributed computing tools
- True continuous availability and no single point of failure- Strong consistency model
- Excels in handling and processing real-time data- Excels in handling and processing batch-oriented data

Hadoop is a software framework, an entire environment with integrated distributed analytics tools. It excels in processing near-time, batch-oriented data. On the other hand, Cassandra is a NoSQL DBMS offering high availability, great for processing real-time online data. Cassandra has Hadoop integration, allowing you to use Cassandra in the Hadoop environment.

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