Data Crunching : Everything You Need to Know

Introduction To Data Crunching

In information science, data crunching is a method that is used to cover the analysis of data and make useful decisions from the vast amount of data and information (big data) if possible. It also refers to the early phase of data processing in which fresh data sets or disorganized data sets are crushed to meet proper research and exploration. It includes planning, system modelling, or application that is being used. Data is everywhere, and it is sorted, processed, and maintained in a structured form before performing iterations or running algorithms. The data which is already processed and imported into one system known as the crushed data.

Best Hadoop Tools that Aid in Big Data Crunching & Management

Hadoop plays a significant role in Big Data Management. Due to that, a lot of big data testing companies today use a wide variety of Hadoop tools for data creation, data processing, and storing the massive amounts of data applications that may run in the clustered systems.

Let’s know about these major Hadoop tools for crunching Big Data:

1. Apache Mahout

It is a distributed linear algebra framework from Apache and mathematically Scala DSL (domain-specific language) designed to help data scientists, statisticians, and mathematicians execute their own algorithms. It effectively supports clustering, collaborative filtering, and classification to gain better insights from the existing Big Data Sets. Machine learning focuses on the field of artificial intelligence, and this tool is also designed based on similar ideas and helps obtain future results based on past performance. 

Official Website: http://mahout.apache.org/

2. Sqoop

Sqoop is the other best tool or command-line interface application designed to transfer vast amounts of data between relational databases and Hadoop. With Sqoop, it is extremely easy to transform the data in Hadoop MapReduce and get the data back into an RDMS. It supports incremental load functionality and uses the Yarn framework to import and export the data in parallel form.

Official Website: https://sqoop.apache.org/

3. Hive

It is a utility tool that makes it easy to perform queries and handle many datasets presented in cloud databases. The framework is providing for the processing of both structured and unstructured data. HIVE is created by Facebook for people who are proficient in using SQL queries. It is a data warehousing component that uses a SQL like an interface to read, write, and manage large data sets in a distributed environment.

Official Website: https://hive.apache.org/

4. Hadoop Distributed File System

The Hadoop Distributed File system is the backbone or the core component of the Hadoop Ecosystem that makes it possible to save different types of large data sets from the structured, semi-structured, or unstructured form. It gives a level of abstraction over the system to view the entire HDFS as a single entity. With HDFS, it is easy to maintain log files and store the data across several nodes.

Official Website: http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/HdfsDesign.html

5. MapReduce

The software framework called MapReduce is used by big data testers for writing applications and processing large data sets using parallel and distributed algorithms inside the Hadoop environment. There are two separate functions in MapReduce that are Map () and Reduce (). With the Map function, one can perform grouping, sorting, and filtering of data. However, the purpose of the Reduce function is to summarize and integrate the results which are produced by the Map function. The Key-Value pair (K, V) is the result generated by the Map function as the input for the Reduce function.

Official Website: https://hadoop.apache.org/docs/r1.2.1/mapred_tutorial.html

Why Need of Data Crunching Techniques

When it comes to saving time, copying huge numbers of data from one website to another, or transferring the millions of purchase records from Oracle database tables to the CRM package of your company, data crunching techniques can be used at that time to design and perform analysis. You can consider data crunching for the following situations:

• If you have a lot of data and there are too many complications, you can then break that data into the available list and represent in just a few lines.
• If unit testing gives you aggression while producing the correct output, you can think of data crunching and perform the process in many cases.
• When the enterprise-scale infrastructure doesn’t support well or when it is impossible to make the system compatible with hundreds of thousands of servers.
• If the issue occurs due to the speed of the disk, your network, or database, on that point, you can consider the basic program rather than the trickiest codes.

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