Some Essential Tools of the Hadoop Ecosystem

When it comes to crunching Big Data, there is a wide array of tools for extraction, storage, preprocessing, processing, analysis, and integration. Some of the tools are:


Apache Hadoop is an open-source framework for scalable, distributed storage and processing. This big data framework allows distributed processing of large data sets that includes both structured and unstructured data. The ability to store different types of data in a distributed and fault tolerant manner are enabled by its Distributed Filesystem (HDFS). It is scalable and cost-efficient for large scale data storage. Apache Hadoop provides a cost-effective way for data storage and processing, thereby enabling organizations to exploit the business value of raw data and migrate their workload to Hadoop.


Apache Hive is a data warehouse built on top of Hadoop. It is widely used by data analysts to query and manage large datasets. It provides a mechanism to make the data accessible using SQL-like language called Hive QL. This open-source data warehousing framework was initially developed at Facebook. Using Hive QL, we can structure the data by defining a schema and query the data stored in HDFS.


The tool allows users to import and export data between RDBMS (relational databases) and HDFS. It allows easy integration with Hadoop based systems like Hive, HBase, Oozie. Sqoop automates the data transfer between Hadoop and external structured datastores. Since this tool facilitates bulk transfer of data between Hadoop and the relational databases, organizations can depend of Sqoop to efficiently do this job. Some of the features provided by Sqoop are:

  • Data import: Import a table, import all tables, and import a complete database
  • Parallel Data Transfer
  • Quick data copies


Apache Flume is a continuous data ingestion mechanism to collect, aggregate and move large amount of streaming data into the HDFS (Hadoop Distributed File System). It was originally designed by Cloudera engineers as a log aggregation system which later evolved to handle streaming event data. While the traditional method of moving data logs led to delays, had limited scalability, and low throughput. With Flume, a large amount of streaming event data can be moved from multiple sources to Hadoop for storage and analysis. Some of the features provided by Apache Flume are:

  • Ingestion of streaming data: from various sources to Hadoop.
  • Horizontal scalability: ingest new data streams (events, logs) as required
  • High throughput, low latency


Apache Pig is a platform for analyzing large data sets. Using a simple scripting language Pig Latin, ETL (Extract, Transform, Load), data analysis, iterative data processing. Pig Latin can be used by users to write complex MapReduce transformation. Pig was developed at Yahoo Research in 2006. As per wiki, the researchers wanted to find a way to create and execute MapReduce jobs on large data sets. Currently, it is one of the top level Apache projects. Some of the features of Apache Pig are:

  • Ease of programming: Pig programs are easy to write, complex, interrelated data transformation can be simplified and encoded as data flow sequences.
  • Extensible: Users can create or develop custom functions for processing data.


Apache Spark is an open-source big data processing framework, developed originally at UC Berkeley’s AMP Lab. This data analytics cluster computing framework allows the user to write fast, distributed programs. Spark has the capability to quickly process and query huge data sets. Being a fast, in-memory data processing engine, Spark enables applications in Hadoop clusters to run 100x faster in memory. It also allows users to execute streaming, machine learning workload, and supports SQL queries

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