Why Hadoop, Why not anything else?
Big Data practitioners deal with datasets so large that it is almost impossible to control them using standard software practices. They often work with loosely structured petabytes, exabytes, zettabyte, and yottabytes of data that are incomplete and unreachable.
What makes a Big Data expert’s effort simpler?
‘Hadoop’. The open-source software framework that is used to store data and run applications on clusters. Hadoop breaks the big data into blocks thus providing a colossal storage. It concurrently processes data and has a framework that can build and run software applications to manage the enormous data volumes.
Hadoop databases are designed to hold unstructured data. They have a very high degree of fault tolerance, are highly scalable, and can operate at very low-costs.
Can a training on Big Data/Hadoop help?
Well, Hadoop is not a single monolithic product, as it is often visualized. It is but a cluster of technologies and open-source products that combine HDFS, MapReduce, Pig, Hive, HBase etc. Depending on the requirement of the BI/DW projects these products can be combined to give the maximum benefits.
Stacking Hbase over HDFS to achieve performance like DBMS, or when to deploy MapReduce or Hadoop based on the data storage constraint, are some of the very common tasks performed by BI/DW professionals. All these require a thorough understanding of the state of each individual tool and the tasks, and how Hadoop best fits in.
A well-designed training in Big Data analytics uses Apache’s open source products and combines them to suit various project types, and industry requisites. Along with the detailed explanation of the core components such as HDFS and YARN, the training includes knowledge of the programming language Pig, the runtime component Hive, the noSQL database HBase, and many more essential elements such as Spark, Flume etc.
Facts about Hadoop that a prospective trainee needs to know!
- HDFS or Hadoop Distributed File System is used to import data
- Hadoop uses a combination of MapReduce, Hive and Pig to Extract and Transform data
- Sqoop is used to export the processed data to external databases
- To combine multiple datasets, and to create an enormous database, various join functionalities of Pig and Hive are used
- To analyze the stored data, and to gather intelligent information from it, tools such as MapReduce, Pig, Hive are used.
- Oozie is used to manage applications and simplify workflow
Hadoop applications are capable of scalable, fault-tolerant, low-cost solutions for managing Big Data. Huge volumes of both structured as well as unstructured data can be organized and managed with proficient knowledge of Big Data and Hadoop. This core technology grants the opportunity to capture, format, manipulate, store, and analyze data to help gain useful insights to drive businesses.
