Big Data Hadoop & Spark

What is HDFS? An Introduction to HDFS

Hadoop is a critical big data framework, which has now been implemented in thousands of organisations. Hadoop frameworks make big data analytics easier, which is important since a large number of organisations today use data analytics in order to generate insights into how they should function to be better.

HDFS or Hadoop Distributed File System is one of the most basic components of the big data framework that is Hadoop. HDFS is cutting edge owing to its capability of storing and retrieving multiple files at the same time, all at extremely high speeds.

HDFS architecture ensures that it can run on commodity hardware so that it can process a large amount of unstructured data. Owing to this feature, it is incredibly fault-tolerant – identical copies of the data are stored at multiple locations within the hardware, and an inability to retrieve data from one location does not cripple the system. The same data can be extracted from other locations quickly.

Features of HDFS

HDFS, when used, improves the data management layer in a huge manner. The cluster is, therefore, able to manage a large amount of data concurrently, thus increasing the speed of the system. HDFS is also storing terabytes and petabytes of data, which is a prerequisite in order to analyse such large amounts of data properly.

Operator intervention is not required in most cases, as HDFS can easily manage multiple thousands of nodes quickly and efficiently. The architecture of the system uses the best of both worlds – distributed and parallel computing – at the same time, so as to run the system at a quicker pace compared to other systems. HDFS also has another important feature called the capability to rollback. This means that the system is allowed to return back to its previous version even after updates are carried out. This is extremely useful especially since there could be many bugs that could cripple the system in beta updates. However, the highlight of HDFS is that the integrity of the data is maintained at all times, and the stored data is virtually incorruptible. This, as mentioned earlier, is because the system stores data at multiple locations so that it is never lost because of any software or hardware issues. Therefore, HDFS has high degrees of reliability and efficiency compared to the existing systems.

Why Should You Use HDFS?

The obvious reason is the safety that the integrity of data is maintained always. The data is stored in three sets of identical copies at multiple locations, thus ensuring that your data is never wiped out regardless of any accidents or bugs. If the data you get is in the streaming format, HDFS is perfect for that. This means that the system is more suited for applications which require batch processing, rather than interactive ones. HDFS works better for data with high throughput rather than low latency.

The sheer size of the amount of data than HDFS can work with is one important reason why you should implement it. the system is capable of working with extremely large sets of data, which could range into the terabytes. Tens of millions of files can be supported in a moment, in the system. The aggregate data bandwidth of HDFS is extremely high, and the focus is always on scaling out with ease.

Another advantage of HDFS is that it is extremely portable, which will be required for most large organisations today. It can work on many types of commodity hardware with ease, without any problems associated with compatibility.

If you are looking to get into data analytics, starting with Hadoop would be ideal. You can check out the courses on big data at Acadgild, for more information!

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