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Community Blog Introduction to Hadoop in Big Data Ecosystem

Introduction to Hadoop in Big Data Ecosystem

In this tutorial, you will get some information of Hadoop in big data ecosystem and how to configure Hadoop.

Whenever we talk about big data, it is not uncommon to hear the phrase Hadoop.

Hadoop is an open source framework that manages distributed storage and data processing for big data applications running in clusters. It is mainly used for batch processing. The core parts of Apache Hadoop are Hadoop Distributed File System (HDFS) and MapReduce.

Since data is large, Hadoop splits the files into blocks and distributes them across nodes in a cluster, which means every node has a copy of the data.

  1. HDFS - It is the primary storage system used by Hadoop applications. HDFS is a distributed file system that stores files as Data Blocks and replicates it over other nodes.
  2. MapReduce – MapReduce receives data from HDFS and splits the input data initially. Now that processing can be done on all data parts simultaneously, which we call distributed processing.

When you want to get data into a big data environment, you can try the following:

  1. Sqoop - The word Sqoop is derived from "SQL + Hadoop", which clearly defines that it helps in transferring data between Hadoop and relational database servers. Thus when the data is structured and in batches, you can use Sqoop as a loading tool to push it into Hadoop.
  2. Apache Flume - A Data Flow used for efficiently collecting, aggregating, and pushing large amounts of streaming data into Hadoop.
  3. Kafka - It is used on real-time streaming data to provide real time analysis. Thus when data is unstructured and streaming, Kafka and Flume together make the processing pipelines.

Hadoop also plays an important part in the storage and processing of the data, for more information please go to Drilling into Big Data – A Gold Mine of Information (1).

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