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5 Best Hadoop Big-Data Analytics Tools

Analyzing data is the first logical step before rational decision making. This is particularly true in the case of every corporation operating in various verticals across the world. Although big data is not such a novel concept and many businesses are aware of it, optimal utilization of it is not such an easy task to come by, even in the case of big corporations.

Big data is being used by large organizations to understand the data that they have access to on day-to-day bases and to utilize that information to identify, plan, and execute modern business strategies. Big data and its proper utilization can also help in making an operation cost efficient by understanding it better.

This is where Hadoop comes in; an open-source framework written in Java that uses lots of other systematic tools to expand its data analytics operations. The Hadoop framework is used globally for data storage and running applications on clusters. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually infinite simultaneous tasks. There is a roster of analytical tools that help Hadoop deal with the mammoth-sized data more efficiently. 

Hadoop is renowned for its capability to offer a robust analytics infrastructure. Below we have listed the 5 best tools that anyone involved in Big-Data can utilize:

Apache Hive

Apache Hive is a data warehousing tool that is built on top of the Hadoop, and data warehousing is nothing but storing the data at a fixed location generated from various sources. Hive is one of the best tools used for data analysis on Hadoop. If you have a basic knowledge of SQL you can comfortably use Apache Hive. The query language of high is known as HQL or HIVEQL. Apache Hive is considered as one of the best tools to be used for data analysis.

Features of Hive:

  • Queries are similar to SQL queries.
  • Hive has different storage types; HBase, ORC, Plain text, etc.
  • Hive has an in-built function for data-mining and other works.
  • Hive operates on compressed data that is present inside the Hadoop Ecosystem.

Apache Spark

Apache spark is an open-source processing engine that is designed for the ease of analytical operations. It is a cluster computing platforms that is designed to be rapid and made for general purpose users. Spark is designed to cover various batch applications, machine learning, streaming data processing, and interactive queries.

Features of Spark:

  • In memory processing
  • Tight Integration Of component
  • Easy and In-expensive
  • The powerful processing engine makes it so fast
  • Spark Streaming has high level library for streaming process

Apache Impala

Apache Impala is an open-source SQL engine designed specifically for Hadoop. Impala with its faster-processing speed is a massive upgrade compared to Apache Hive. Apache Impala uses similar kinds of SQL syntax, ODBC driver, and user interface as that of Apache Hive. Apache Impala can easily be integrated with Hadoop for data analytics purposes.

Features of Impala:

  • Easy-Integration
  • Scalability
  • Security
  • In Memory data processing

Map Reduce

MapReduce is just an algorithm or can be classified as a data structure that is based on the YARN framework. The primary feature of Map Reduce is to perform distributed processing parallel to a Hadoop cluster, which makes it distinguishably agile.

Features of Map Reduce:

  • Scalable
  • Fault Tolerance
  • Parallel Processing
  • Tunable Replication
  • Load Balancing

HBase

HBase is a non-distributed, column-based oriented, and non-relational database. It consists of multiple tables and these tables consist of many data rows. These data rows further have multiple column families and the column’s family each consists of a key-value pair. HBase works on the top of HDFS (Hadoop Distributed File System). We use HBase for searching small size data from the more massive datasets.

Features of HBase:

  • HBase has Linear and Modular Scalability
  • JAVA API can easily be used for client access
  • Block cache for real time data queries

Your business might be sitting on a huge pile of meaningless data. Let us help you out in sorting your house, and turn your data layers into a goldmine for your business, Get in touch with one of Makeen’s Big Data experts today.

Book your meeting here: https://www.makeen.io/lets-talk/