Exploring the differences between Apache Cassandra and Apache Hadoop Distributed File System

Both Apache Hadoop and Apache Cassandra are frameworks that belong to the Apache Software Foundation. However, these two frameworks are designed for different purposes. That's why we thought of doing a comparison between them. This understanding will help you to work with a data science services company and figure out the best approach to proceed with your machine learning model developments.

• Ability to deal with large datasets
  Both Apache Hadoop and Apache Cassandra can deal with large datasets. However, you should select out of these two depending on the nature of the data that you have. If you have a requirement to write large files to the framework, you may think about using the Apache Hadoop Distributed File System. That’s because it will take a massive file, split that into different small files, and distribute those files across multiple nodes. The reverse operation of it would take place when you are reading files.

• Ensuring the consistency of data
  Data consistency would ensure the number of nodes confirming replicas of data are stored, while the entire process completes successfully. The data consistency level would determine the total number of nodes that will need to respond before data returns to the end-users. Hadoop uses a sequential approach when replicating data. For example, it will initially refer to a few nodes and then proceed with doing the same with other nodes. But you can involve all nodes to create replicas with Apache Cassandra. Hence, it is a better solution available to ensure the consistency of data.

• Resistance to failure
  Both these frameworks come along with excellent failure resistance capabilities. That’s because both these frameworks will make multiple copies of the datasets and store them in multiple nodes. In case if some of the nodes fail, you can make sure that copies are available on other nodes. But when you take a look at the masterclass architecture of Cassandra, you will notice that it is a more reliable solution available.

• Indexing capabilities
  Both Cassandra and Hadoop can deal with large volumes of data. They will scan a few parts of the data before scanning the entire system. This would enhance the overall speed of the framework. This is where indexing would come into play.

Even though both these frameworks support indexing, you will notice how they follow different approaches for it. Cassandra has some special techniques, which help is to retrieve data faster and create multiple indexes. However, Hadoop will split the dataset and create indexes. This is pretty much similar to record-level indexing.

What is the best framework?
Now you are aware of the key differences between these two frameworks. While keeping the differences in mind, you can get the help of a data science consulting company like Hexaview Technologies to start using the best framework. If you have a need to store a massive dataset and process, we strongly encourage you to use Hadoop. But if you wish to work with multiple small records, Cassandra would be a good solution.