Problem Statement
Direct business impacts of enormous volumes of structured and unstructured data need to be more adequately analyzed and used.
Solution
Apache Hadoop
Outcome
Big data is generated at high speed, traditional RDBMS systems have not been able to handle such rapid growth. Moreover, they are also unable to process unstructured data. It was very difficult to handle such a huge amount of rapidly growing heterogeneous data and process this data at high speed. So there was a need for such a system that is able to efficiently process large data sets. Hadoop was created to solve the scenario. HDFS is a component of Hadoop that solves the storage problem of a large dataset using distributed storage, while YARN is a component that solves the processing problem that drastically reduces the processing time.
Hadoop is an open-source software framework for storing and processing large data sets using a distributed large cluster of commodity hardware. It was developed by Doug Cutting and Michael J. Cafarella and licensed under Apache. It is written in Java and was developed based on an article written by Google on the MapReduce system and uses functional programming concepts.
Hadoop tools are defined as a framework required to process large amounts of data distributed in the form and clusters to perform distributed computations. A few tools used in Hadoop for data manipulation are Hive, Pig, Sqoop, HBase, Zookeeper and Flume, where Hive and Pig are used to query and analyze data, Sqoop is used to move data and Flume is used. to process data streams to HDFS.
- Hive
Hive can handle many types of file formats like sequential file, ORC file, text file etc. Partitioning, grouping and indexing are available for faster execution. Compressed data can also be loaded into a sub-register table. Managed or internal tables and external tables are prominent features of Hive.
- Pig
Users may have their own functions to perform a certain type of data processing. It is easy to write codes in Pig relatively also the code length is less. The system can automatically optimize execution.
- Sqoop
Sqoop is used to transfer and load data from HDFS to Relational Databases (RDBMS) and vice versa. We can transfer data to HDFS from RDBMS, Hive etc and can process and export it back to RDBMS. We can add data to the table many times. We can also create a Sqoop job and execute it ‘n’ times.
- HBase
The database management system on the top of the HDFS layer is calledHBase. HBase is a NoSQL database that is built on top of HDFS. HBase is not a relational database; it does not support structured query languages. HBase uses HDFS distributed processing.
Hadoop can perform big data computations. To handle this, Google developed a Map-Reduce algorithm and Hadoop runs this algorithm. It will play a major role in statistical analysis, business intelligence and ETL processing. Ease of use and cheaper availability. It can process terabytes of data, analyze it and provide value from the data without any hassle and without losing information.