Hadoop is a framework useful for storing data and running applications. Providing large storage for any kind of data is one of its main features. In addition, it carries humongous processing power due to which it is capable of handling multiple concurrent tasks at a time. This course has high demands and many organizations provide Big Data Hadoop Training Institute in Noida.
Modules Of Hadoop
- Hadoop Distributed File System (HDFS)- HDFS is a distributed file system capable of running on standard low-end hardware. This system ensures better data throughput along with great fault tolerance and large dataset support.
- Yet Another Resource Negotiator (YARN)- YARN is responsible for managing and monitoring the cluster nodes. In addition, it also helps in scheduling jobs and tasks.
- MapReduce- It is useful for programs in conducting parallel data computation. In addition, the input data is converted into computed datasets.
- Hadoop Common- It comes with common Java libraries that are useful all over the module.
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Components Of Hadoop
With the introduction of Hadoop, using storage and processing capacity in cluster servers has become much easier. It acts as a building block for building other applications. In the past few years, the Hadoop ecosystem has shown significant growth due to its various features. This ecosystem consists of a number of applications and tools to collect, store, analyze and manage big data. Given below are some of its most popular and used components.
- Spark- This is an open-source platform useful for huge data workloads in distributed processing. In addition, it ensures quick performance, general batch processing, streaming analysis, machine learning, and graph database.
- Presto- This open-source tool of Hadoop is beneficial for supporting ANSI SQL standards, including complex queries, aggregations, joins, and window functions. In addition, it’s capable of processing data from multiple sources like (HDFS) and Amazon S3.
- Hive- This tool lets users leverage Hadoop MapReduce by the use of the SQL interface. This results in executing analytics at a large scale.
- HBase- HBase is an open-source database that runs with Amazon S3. Above all, it uses Hadoop Distributed File System (HDFS) and is a distributed big data store created with tables with uncountable rows and columns.
- Zeppelin- This can be termed as a notebook that allows a user to explore interactive data.
Benefits Of Hadoop For Big Data
- Resilience- It ensures resilience as data stored in a node automatically replicates in the other nodes of a cluster. It enables fault tolerance and provides backup of data in case a node goes down.
- Scalability- Hadoop runs on a distributed environment and therefore, it’s scalable. It makes the setups capable of expanding as per the need. This helps in storing up to multiple petabytes of data in the setups.
- Low Cost- This is open-source software and it has low costs compared to relational database systems.
- Data Diversity- This platform stores data into three formats that are unstructured, semi-structured, and structured. It allows the dumping of data in any format without validating a predetermined schema. However, data fits into any schema while retrieving. It’s beneficial as it derives multiple insights using the same data.
- Speed- It ensures faster execution of complex queries with the help of distributed file system, concurrent processing, and MapReduce model.
Conclusion
In conclusion, Hadoop is an open-source framework useful for storing big data. One can earn about the functioning of this tool through Big Data Hadoop Online Training. It has enormous storage capacity and brilliant processing power. It is further categorized into a few modules and components. It provides scalability and resilience. Moreover, it is pocket-friendly and ensures data diversity.