Top 7 Hadoop Tools to Make Your Big Data Journey Easy:

Apache Hadoop is an open source framework that is used to efficiently store and process large datasets ranging in size from gigabytes to petabytes of data. Instead of using one large computer to store and process the data, Hadoop allows clustering multiple computers to analyze massive datasets in parallel more quickly.

Top 7 Hadoop Tools You Should Master:

1) HDFS

Hadoop Distributed File System, which is commonly known as HDFS is designed to store a large amount of data, hence is quite a lot more efficient than the NTFS (New Type File System) and FAT32 File System, which are used in Windows PCs. HDFS is used to carter large chunks of data quickly to applications. Yahoo has been using Hadoop Distributed File System to manage over 40 petabytes of data.

2) HIVE

Apache, which is commonly known for hosting servers, have got their solution for Hadoop’s database as Apache HIVE data warehouse software. This makes it easy for us to query and manage large datasets. With HIVE, all the unstructured data are projected with a structure, and later, we can query the data with SQL like language known as HiveQL.

HIVE provides different storage types such as plain text, RCFile, Hbase, ORC, etc. HIVE also comes with built-in functions for the users, which can be used to manipulate dates, strings, numbers, and several other types of data mining functions.

3) NoSQL

Structured Query Languages have been in use since a long time, now as the data is mostly unstructured, we require a Query Language which doesn’t have any structure. This is solved mainly through NoSQL.

Here we have primarily key pair values with secondary indexes. NoSQL can easily be integrated with Oracle Database, Oracle Wallet, and Hadoop. This makes NoSQL one of the widely supported Unstructured Query Language.

4) Spark

Coming to hadoop analytics tools, Spark tops the list. Spark is a framework available for Big Data analytics from Apache. This one is an open-source data analytics cluster computing framework that was initially developed by AMPLab at UC Berkeley. Later Apache bought the same from AMPLab.

Spark works on the Hadoop Distributed File System, which is one of the standard file systems to work with BigData. Spark promises to perform 100 times better than the MapReduce algorithm for Hadoop over a specific type of application.

5) MapReduce

Hadoop MapReduce is a framework that makes it quite easy for the developer to write an application that will process multi-terabyte datasets in parallel. These datasets can be calculated over large clusters. MapReduce framework consists of a JobTracker and TaskTracker; there is a single JobTracker which tracks all the jobs, while there is a TaskTracker for every cluster-node. Master i.e., JobTracker, schedules the job, while TaskTracker, which is a slave, monitors them and reschedule them if they failed.

6) Avro

With this tool, we can quickly get representations of complex data structures that are generated by Hadoop’s MapReduce algorithm. Avro Data tool can easily take both input and output from a MapReduce Job, where it can also format the same in a much easier way. With Avro, we can have real-time indexing, with easily understandable XML Configurations for the tool.

7) Flume

LOGs are generated whenever there is any request, response, or any type of activity in the database. Logs help to debug the program and see where things are going wrong. While working with large sets of data, even the Logs are generated in bulk. And when we need to move this massive amount of log data, Flume comes into play. Flume uses a simple, extensible data model, which will help you to apply online analytic applications with the most ease.

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