What is HDFS?
The storage system in the Hadoop framework that has a collection of open-source software applications to solve different problems is called Hadoop Distributed File System. This has the primary name node, and the nodes are organized in the same space as the data center. Data is distributed to different nodes for storage as it breaks down into smaller units. This is the primary storage system used in all Hadoop applications. It is written in Java and has high-performance access to data. Data is stored in a distributed approach to different nodes. HDFS reserves large files and helps the users in Hadoop.
It has NameNode, DataNode, Job Tracker, Task Tracker, and Secondary Name Node services. It also provides by default 3 replications of data across the cluster, which helps retrieve the data if one node is down due to failure. For example, if there is one file with a size of 100 MB, this file gets stored across in 3 replications taking up a total of 300 MB with the two extra files as backup. NameNode and Job Tracker are called Master Nodes, whereas DataNode and Task Tracker are called Slave Nodes.
The metadata is stored in NameNode, and the data is stored in the blocks of different DataNodes based on the availability of free space across the cluster. If the metadata is lost, it will not work, and as the NameNode saves the metadata, it should have highly reliable hardware. The Secondary NameNode acts as a standby node for NameNode during failure. If a DataNode fails, then the metadata of that DataNode is removed from the NameNode, and the NameNode takes the metadata of the newly allocated DataNode instead of the failed one.
How does HDFS make Working so Easy?
It provides the feature of replicating the data among the DataNodes. Thus, in case of any failure in the cluster, it is easy to keep the data safe as the Data becomes available on other Nodes. Also, one does not need to have highly reliable hardware across the cluster. The DataNodes can be cheap hardware, and only one highly reliable NameNode storing the metadata is required.
What can you do with HDFS?
One can build a robust system to store massive data that is easy to retrieve and provides fault tolerance and scalability. Moreover, it is easy to add inexpensive hardware and can be easily monitored through one of the slave services.
It is the backbone of Hadoop and provides many features to suit the Big Data environment’s needs. Working with it makes it easier to handle large clusters and maintain them. In addition, it is easy to achieve scalability and fault tolerance through HDFS.
One of the advantages of using it is its cost-effectiveness. Organizations can build a reliable system with inexpensive hardware for storage, and it works well with Map Reduce, which is the processing model of Hadoop. In addition, it is efficient in performing sequential reads and writes, the access pattern in Map Reduce Jobs.
As HDFS is designed for Hadoop Framework, knowledge of Hadoop Architecture is vital. Also, the Hadoop framework is written in JAVA, so a good understanding of JAVA programming is crucial. Moreover, it is used along with Map Reduce Model, so a good understanding of the Map-Reduce job is a bonus. Apart from the above, a good understanding of Database, practical knowledge of Hive Query Language, and problem-solving and analytical skill in a Big Data environment are required.
Why should we use HDFS?
With the increase in data volume every second, storing the huge amount of data that can be up to Terabytes in size and having a fault-tolerant system has made it popular for many organizations. It keeps the files in blocks and provides replication. The unused space in a block can be used for storing other data. NameNode stores the metadata, so it has to be highly reliable. But the DataNodes storing the actual data are inexpensive hardware. So because of two of its most prominent advantages, it is highly recommended and trusted.
The amount of data produced from unnumbered sources is massive, making the analysis and storage even more difficult. For solving these Big Data problems, Hadoop has become so popular with its two components, HDFS and Map Reduce. As the data grows every second of every day, the need for technologies like HDFS even grows more as the organizations cannot just ignore the massive amount of data.
Why do we need HDFS?
Organizations are rapidly moving towards a direction where data has utmost importance. The Data gathered from many sources and also data generated by their Businesses every day are equally important. So adopting a model like It may suit their very well to their needs along with reliability.
Who is the right audience for learning HDFS Technologies?
Anyone dealing with the analysis or storage of a huge amount of data can find it very helpful. Even those who had used Databases earlier and understood the growing need in the market to provide a robust system help them understand the new approach of getting to know Big Data.
How will this Technology help you in Career Growth?
As organizations are adopting the Big Data technology to store the data, then to analyze it and sample to build a better Business, with the help of technologies like Hadoop, it certainly boosts one’s career. It is one of the most reliable models in Hadoop, and working with it gives outstanding opportunities.
Today HDFS is being used by some of the biggest companies because of its fault-tolerant architecture and cost-effectiveness. As the data grows every second, the need to store it even increases day by day. Organizations rely upon the data and its analysis. So with this trend in Business, certainly provides a perfect platform where the data is stored and is not lost if there is any disruption.
This has been a guide to What is HDFS?. Here we discussed the basic concept, scope, skills required, and advantages and career growth in HDFS. You can also go through our other suggested articles to learn more –
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