In today’s world, 90% of the data has been created in the last two years. This data volume explosion, variety, and velocity of data, is termed as Big Data. If you can hitch or crunch it, it will revolutionize the way you do business. One of the popular tools to do just this is Hadoop.
It was only about ten years ago that Hadoop, this shiny new technology for Big Data, first came into the market. Things changed fast and Hadoop is now a billion-dollar market, underpinning big data efforts by companies of all stripes.
Hadoop is a highly scalable storage platform, its community is fast evolving and has a very prominent role in its eco-system. Hadoop enables businesses to easily access new data sources and tap into different types of data (both structured and unstructured) to generate value from that data. Hadoop tools are designed to do such complicated tasks and scale up from single servers to thousands of machines, each offering local computation and storage. In this article we will visit some of the essential Hadoop tools for crunching big data –
Apache Hadoop is a free, Java-based programming framework, inspired by Google’s MapReduce framework that supports the processing of large data sets in a distributed computing environment. Generally, the entire group of map and reduce tools are termed as “Hadoop”. Hadoop runs applications on systems with thousands of nodes involving thousands of petabytes. With Hadoop, programmers can write code for data analysis. Hadoop also manages fault and error from any individual machine.
HDFS (Hadoop Distributed File System):
Hadoop Distributed File System (HDFS) is a distributed file system under Apache license that offers a basic framework for splitting up data collections between multiple nodes. HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware. In HDFS, the large files are broken into blocks where many nodes hold those blocks from a file for maintaining steady streaming.
HBase is a key/value store, column-oriented open source and non-relational database management system that runs on top of HDFS. Specifically it is a sparse, consistent, distributed, multidimensional, sorted map. HBase applications are written in Java which comprises a set of tables that store the data, search it and automatically share the table across multiple nodes so that MapReduce jobs can run it locally.
Pig Latin is a simple-to-understand data flow language used in the analysis of large data sets. Pig scripts are automatically converted into MapReduce jobs by the Pig interpreter, so you can analyze the data in a Hadoop cluster even if you aren’t familiar with MapReduce. Pig Latin is filled with abstractions for handling the data. Pig also allows the user to define their own functions.
Apache Ambari is the go-to tool for management of Hortonworks Data Platform. It is open operational framework for provisioning, managing and monitoring Apache Hadoop clusters. It offers a web based GUI (Graphical User Interface) with wizard scripts for setting up clusters with most of the standard components.
Apache Hive regulates the process of extracting bits from all the files in HBase. It supports analysis of large datasets stored in Hadoop’s HDFS and compatible file systems. It also provides an SQL like language called HSQL (HiveSQL) that gets into the files and extracts the required snippets for the code.
The misleading term “NoSQl” is “Not Only SQL” which is more cloud friendly approach. It is unlike RDBMS. Some Hadoop clusters integrate with NoSQL data stores that come with their own mechanisms for storing data across a cluster of nodes. This allows them to store and retrieve data with all the features of the NoSQL database, after which Hadoop can be used to schedule data analysis jobs on the same cluster.
Apache Sqoop is specially designed to transfer bulk data efficiently from the traditional databases into Hive or HBase. Sqoop is a command line tool, mapping between the tables and the data storage layer, translating the tables into a configurable combination of HDFS, HBase or Hive.
Apache ZooKeeper is offered as part of the Hadoop ecosytem as a centralized service that maintains, configures information, gives a name and provides distributed synchronization across a cluster. It imposes a file system-like hierarchy on the cluster and stores all of the metadata for the machines, so we can synchronize the work of the various machines.
A Java Web-application framework allowing to combine multiple Map/Reduce jobs into a logical unit of work. On breaking the project in to multiple Hadoop jobs, Oozie starts processing them in sequence. It manages the workflow by scheduling.
Some other Hadoop tools for crunching the data are Flume, Spark, MongoDB, Cassandra, Avro and Mahout.
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