Big Data Ecosystem

Big Data Ecosystem

Hadoop is a distributed system oriented batch , cut for processing  large data sets. Hadoop users find themselves manipulating the HDFS file system or develop low MapReduce programs often from scratch. Subprojects to Hadoop born of this  and provide mechanisms and features that simplify the handling and processing of large  data sets. We briefly present a few in this section. A full list can be found here: Big Data Ecosystem .

What HBase?

HBase allows integration with Hadoop with a key storage system / value commonly  called binary storage or key / value store in English. This sub-project Hadoop is also   inspired by the project BigTable of Google.

What Hive?

Hive creates a relational database in the HDFS file system. The project allows developers  to write queries in a language called SQL near HiveQL, which are then translated as MapReduce programs on the cluster. The advantage is to provide a language that developers know for writing MapReduce programs.

What Pig?

The project Pig Hive is positioned as in the sense that it provides developers with level language (DSL) dedicated to the analysis of large data volumes. It is then for  developers used to create scripts via Bash or Python, for example. Furthermore, Pig is  extensible in the sense that, if a function is not available, it is possible to enrich via  specific developments in a low-level language (Java, Python ...). In the same vein the  project Pig , thereScalding that draws the power of Scala language to develop  MapReduce programs.

What Sqoop?

Sqoop is a project that helps to interact with relational database management systems to Hadoop. The project allows you to import and export data to and from a database.

What Mahout?

Mahout provides implementations of algorithms to make intelligence. It provides, for  example, algorithms for data partitioning and the automatic classification in a MapReduce environment.

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