Top 10 Hadoop Analytics Tools For Big Data
Hadoop is an open-source framework written in Java that uses lots of other analytical tools to improve its data analytics operations. The article demonstrates the most widely and essential analytics tools that Hadoop can use to improve its reliability and processing to generate new insight into data. Hadoop is used for some advanced level of analytics, which includes Machine Learning and data mining.
There is a wide range of analytical tools available in the market that help Hadoop deal with the astronomical size data efficiently. Let us discuss some of the most famous and widely used tools one by one. Below are the top 10 Hadoop analytics tools for big data.
1. Apache Spark
Apache spark in an open-source processing engine that is designed for ease of analytics operations. It is a cluster computing platform that is designed to be fast and made for general purpose uses. 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
2. Map Reduce
MapReduce is just like an Algorithm or a data structure that is based on the YARN framework. The primary feature of MapReduce is to perform the distributed processing in parallel in a Hadoop cluster, which Makes Hadoop working so fast Because when we are dealing with Big Data, serial processing is no more of any use.
Features of Map-Reduce:
- Fault Tolerance
- Paraller Processing
- Tunable Replication
- Load Balancing
3. 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. The one who is having knowledge of SQL can comfortably use Apache Hive. The query language of high is known as HQL or HIVEQL.
Features of Hive:
- Queries are similar to SQL queries.
- Hive has different storage type HBase, ORC, Plain text, etc.
- Hive has in-built function for data-mining and other works.
- Hive operates on compressed data that is present inside Hadoop Ecosystem.
4. Apache Impala
Apache Impala is an open-source SQL engine designed for Hadoop. Impala overcomes the speed-related issue in Apache Hive with its faster-processing speed. 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:
- In Memory data processing
5. Apache Mahout
The name Mahout is taken from the Hindi word Mahavat which means the elephant rider. Apache Mahout runs the algorithm on the top of Hadoop, so it is named Mahout. Mahout is mainly used for implementing various Machine Learning algorithms on our Hadoop like classification, Collaborative filtering, Recommendation. Apache Mahout can implement the Machine algorithms without integration on Hadoop.
Features of Mahout:
- Used for Machine Learning Application
- Mahout has Vector and Matrix libraries
- Ability to analyze large datasets quickly
6. Apache Pig
This Pig was Initially developed by Yahoo to get ease in programming. Apache Pig has the capability to process an extensive dataset as it works on top of the Hadoop. Apache pig is used for analyzing more massive datasets by representing them as dataflow. Apache Pig also raises the level of abstraction for processing enormous datasets. Pig Latin is the scripting language that the developer uses for working on the Pig framework that runs on Pig runtime.
Features of Pig:
- Easy To Programme
- Rich set of operators
- Ability to handle various kind of data
HBase is nothing but a non-relational, NoSQL distributed, and column-oriented database. HBase consists of various tables where each table has multiple numbers of data rows. These rows will have multiple numbers of column family’s, and this column family will have columns that contain key-value pairs. 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
8. Apache Sqoop
Sqoop is a command-line tool that is developed by Apache. The primary purpose of Apache Sqoop is to import structured data i.e., RDBMS(Relational database management System) like MySQL, SQL Server, Oracle to our HDFS(Hadoop Distributed File System). Sqoop can also export the data from our HDFS to RDBMS.
Features of Sqoop:
- Sqoop can Import Data To Hive or HBase
- Connecting to database server
- Controlling parallelism
Tableau is a data visualization software that can be used for data analytics and business intelligence. It provides a variety of interactive visualization to showcase the insights of the data and can translate the queries to visualization and can also import all ranges and sizes of data. Tableau offers rapid analysis and processing, so it Generates useful visualizing charts on interactive dashboards and worksheets.
Features of Tableau:
- Tableau supports Bar chart, Histogram, Pie chart, Motion chart, Bullet chart, Gantt chart and so many
- Secure and Robust
- Interactive Dashboard and worksheets
10. Apache Storm
Apache Storm is a free open source distributed real-time computation system build using Programming languages like Clojure and java. It can be used with many programming languages. Apache Storm is used for the Streaming process, which is very faster. We use Daemons like Nimbus, Zookeeper, and Supervisor in Apache Storm. Apache Storm can be used for real-time processing, online Machine learning, and many more. Companies like Yahoo, Spotify, Twitter, and so many uses Apache Storm.
Features of Storm:
- Easily operatable
- each node can process millions of tuples in one second
- Scalable and Fault Tolerance