MongoDB Vs HBase Vs Cassandra: What Should You Use for Big Data? – Starweaver
MongoDB Vs HBase Vs Cassandra: What Should You Use for Big Data?

MongoDB Vs HBase Vs Cassandra: What Should You Use for Big Data?

Many companies have found that there are a lot of benefits to working with big data. They know this is one of the best options to help them learn about their customers and make predictions on how things will go in the future and which products they should release as well.

While gathering the data, it is important to take some time to put the data into some databases. This helps keep it safe and sorts it out for an analysis later on. There are many great databases that you can choose from, but knowing which one is right for you can be a challenge.

Three amazing options to use with your database include MongoDB, HBase, and Cassandra. All of them can help you with your big data, but knowing the right one can be a challenge and will depend on the type of data you want to work with and how you plan to use that data later on.

What is MongoDB?

The first option that we will take a look at is known as MongoDB. This is a highly flexible and scalable NoSQL database management platform. It is based on documents, which allows it to work with different models of data while storing data in the right key to value sets when necessary.

This framework was first developed in order to accommodate different models of data and to be a solution that developers need when they work with lots of data, especially when the data is too large for relational models to work with at all. It is free and open-sourced to use, which makes it the perfect option for a lot of companies to choose from.

There are a ton of great features that come with MongoDB and some of these include:

  1. This framework is a query language that supports text search and all of the other operations that you need.
  2. It will not need as many input and output operations because it relies on embedded data models. This helps you to get a faster query in the process.
  3. This option will also provide fault tolerance because it can create some replica datasets. This is important because replication can help make sure that data is stored on more than one server.
  4. It also features sharding, which helps horizontal scalability to become possible. This is good because it will support all of the increasing data needs for a lower cost than other options.
  5. It works with more than one storage engine to help split up the workload.

What is HBase?

The second option that you can use is HBase. This is a little different compared to the MongoDB that we talked about before in that it works on a column-oriented non-relational database management system that can work well with Hadoop. HBase is going to be a fault-tolerant way to store any of the sparse sets of data that you have, which can be pretty common when you use big data. It is also fantastic when you need to process data in real-time or when you need to read and write access to the big data volumes.

This is not a relational database system so it will not support languages like SQL. It is written out in Java, which is great because a lot of the data you will gather for big data will come from the Java coding language. The system is designed to scale linearly too, and it is comprised of standard tables with columns and rows like a traditional database. This can make it easier to sort out and read through the data.

HBase is going to rely on a framework known as ZooKeeper to help with the high-performance coordination. This is built into the database, but if you are looking to run a production cluster, you should create your own ZooKeeper cluster to work with as well. It also works with Hive, which is a query engine that helps you to batch process your big data when necessary.

What is Cassandra?

The third option is known as Apache Cassandra. This is a NoSQL database with a lot of power, capable of handling a large amount of data and records through many different servers. Many companies need to work with this when it is time to scale it, helping them to deploy it well when the need arises. It is actually one of the most efficient databases that use NoSQL today and can be great when handling all of that data.

With this database, it will rely on a data processing engine that works the best with any data that is stored in a tabular form so it won’t work with any of the relational databases. It is strong and powerful, with everything that you need to hold onto the data, no matter how much data you need to store and hold onto for a while. It is also scalable, highly consistent, and fault-tolerant so it may be the exact option you need for your data.

Which One is Best for Big Data?

All three of these databases are amazing when it comes to handling big data. They can become scalable if you would like, they do not rely on SQL, and they don’t have the same restraints as some of the relational databases. This makes them a good option if you have a lot of data that is hard to sort out in traditional databases.

The one you choose will really depend on the amount and type of data that you would like to focus on. It can also depend on the experience you have with big data and what you hope to do with the data as well. For example, HBase is a good option if you need a database that is not relational, but still has the columns and tabs that a traditional database does.

Choosing a good database is an important option to work with. It will ensure that your data is safe and secure so you can go through and analyze it, using it to promote your business and help it succeed in the future.


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