Today, we live in the Big Data era, where data volumes have outgrown a single machine's storage and processing capacities, and the multiple types of data formats that need to be processed have expanded tremendously. This brings 2 primary challenges:
- How to store and work with large amounts of data and its variety
- How to assess & exploit these large data points for competitive advantage.
As years went by and data production grew, more volumes and more formats appeared. As a result, multiple processors were required to process data to save time. However, due to the overhead network that was created, a single storage unit became a bottleneck. This has contributed to the use of a distributed storage unit for each processor, which has made access to data simpler. This approach is known as parallel processing of distributed storage-several machines run processes on separate storage sites.
By overcoming all obstacles, Hadoop fills this hole, by using parallel processing. So, what about Hadoop or HDFS?-HDFS stands for Hadoop distributed file system. It distributes the computing tier using MapReduce programming. It is connected to the low-cost commodity servers called the Cluster. To monitor the processing, HDFS consists of a Master Node or NameNode. These nodes are used for data collection & processing.
All data computations were conducted during the last decade by raising the processing capacity of a single computer by increasing the number of processors and increasing the RAM, but they had physical limitations. Scalability was restricted by physical size & little or limited tolerance of fault. How does Hadoop address such challenges?
Several machines are connected in parallel in a cluster work for the faster crunching of data. The work is assigned to another machine automatically if anyone machine fails. MapReduce splits large jobs into smaller bits to be performed in parallel. Interested in Hadoop learn it from the best Hadoop training institute in Bangalore .
Benefits of using HDFS are:
- It has very cost-effective storage as the commodity servers to decrease the cost per terabyte.
- Hadoop serves virtually unlimited scalability. Without any modifications to existing data, new nodes may be added to have the ability to process any volume of data with no archiving required.
- The parallel processing system reduces processing time.
- HDFS is Flexible and schema-less, any data format can be stored be it structured & unstructured.
- In Hadoop any node failure is covered by another node automatically,
