Hadoop is probably the very first open-source Big Data platform. It is highly scalable and runs on commodity hardware. It includes HDFS which is Hadoop Distributed File System. It can STORE a very large amount of UNSTRUCTURED data in a distributed fashion.

Hadoop also includes MapReduce which is a data processing framework. It processes data in a highly parallel fashion.

For a large quantity of data, the processing time is drastically reduced. There are so many API's and other tools available that can be integrated with Hadoop that further extends its usefulness and enhances its capability and makes it more suitable for Big Data.

The Hadoop Framework let the user write and test the distributed systems quickly.

It is fault-tolerant and automatically distributes the data across the cluster of machines. It makes use of massive parallelism. To provide high AVAILABILITY and fault-tolerance, Hadoop does not depend on the underlying hardware.

At the application layer itself, it provides such SUPPORT. We can add or remove nodes as per our requirements. You are not required to make any changes to the application.

Apart from being open-source, the other biggest advantage we have of Hadoop is its compatibility with almost all the platforms. The amount of data that is being generated is increasing by a very large quantity day by day. So, the need for data storage and processing will increase accordingly. The best part of Hadoop is that by adding more number of commodity machines you can increase the storage and processing power of Hadoop without any other investment in software or other tools.

Thus, just by adding more machines, we can accommodate the ever-increasing volume of data. Due to Fault-tolerant feature of Hadoop, the data as well as the application processing, both are protected against any hardware failure.

If a particular node goes down, the jobs are redirected automatically to other nodes. This ensures that 'Distributed Computing' does not fail.

There are multiple copies (by default 3) of data stored by the Hadoop automatically.

Hadoop provides more flexibility in terms of data CAPTURE and storage. You can capture any data, in any format, from any source into the Hadoop and store it as it is without any kind of preprocessing on it. Whereas in traditional systems, you are required to pre-process the data before storage.

So, in Hadoop, you can store any data and then later process it as per your requirements.

The ecosystem around Hadoop is very strong. There are so many tools available for different needs. We have tools for automatic data extraction, storage, transformation, processing, analysis etc.

There are a variety of cloud options available for Hadoop. So, you have a choice to use on-premise as well as cloud-based features/tools as per your requirements.

Thus, by considering all these features that Hadoop provides and the robustness, cost-effectiveness it offers and also by taking into consideration the nature of Big Data, we can say that Hadoop is more suitable for Big Data.