Most Important Hadoop Analytics Tools in 2020 – Take a Lunge into Analytics
Study different Hadoop Analytics tools for analyzing Big Data and generating insights from it. Hadoop is an open-source framework developed by the Apache Software Foundation for storing, processing, and evaluating big data. The aim of designing Hadoop was to build a reliable, cost-effective, highly available framework that effectively stores and processes the data of varying formats and sizes.
In this tutorial, we will study the various Hadoop Analytics tools. The article enlists the top analytics tools used for processing or analyzing big data and generating insights from it.
Let us now explore popular Hadoop analytics tools.
Top Hadoop Analytics Tools
1. Apache Spark
Apache Spark is a popular open-source unified analytics engine for big data and machine learning. Apache Software Foundation developed Spark for speeding up the Hadoop big data processing. Apache Spark extends the Hadoop MapReduce model to use efficiently for more types of computations like interactive queries, stream processing, etc. Apache Spark allows batch, real-time, and advanced analytics over the Hadoop platform. Apache Spark offers in-memory data processing for the developers and the data scientists
Spark has become the default execution engine for workloads such as batch processing, interactive queries, and streaming, etc.
Companies like Netflix, Yahoo, eBay, and many more, have deployed Spark at a huge scale.
Features of Apache Spark:
• Speed: The powerful processing engine permits Spark to process the data rapidly on a large-scale. Apache Spark can run applications in Hadoop clusters 100 times faster in memory and ten times faster on the disk.
• Ease of use: Apache Spark can work with various data stores (such as OpenStack, HDFS, Cassandra) due to which it provides more flexibility than Hadoop. It supports both real-time and batch processing and offers high-level APIs in Java, Scala, Python, and R.
• Generality: It contains a stack of libraries, including MLlib for machine learning, SQL and DataFrames, GraphX, and Spark Streaming. We can combine these libraries in the same application.
• Runs Everywhere: Spark can run on Hadoop, Kubernetes, Apache Mesos, standalone, or in the cloud.
2. MapReduce
MapReduce is the heart of Hadoop. It is a software framework for writing applications that process large datasets in parallel across hundreds or thousands of nodes on the Hadoop cluster.
Hadoop divides the client’s MapReduce job into many independent tasks that run in parallel to give throughput. The MapReduce job is divided into map task and reduce task. Programmers generally write the entire business logic in the map task and state light-weight processing like aggregation or summation on the reduce task. The MapReduce framework works in two phases- The map phase and the Reduce phase. The input to both the phases is the key-value pair.
Features of Hadoop MapReduce:
- Scalable: The MapReduce framework is scalable. Once we write a MapReduce program, we can easily expand it to work over a cluster having hundreds or even thousands of nodes.
- Fault-tolerance: It is highly fault-tolerant. It automatically recovers from failure.
3. Apache Impala
Apache Impala is an open-source tool that disables the slowness of Apache Hive. Apache Impala is a local analytic database for Apache Hadoop. With Impala, we can query data stored either in HDFS or HBase in real-time. It uses the same metadata, ODBC driver, SQL syntax, and user interface as Apache Hive, thus offering a well-known and uniformed platform for batch or real-time queries. We can integrate Apache Impala with Apache Hadoop and other BI tools to provide a cost-effective platform for analytics.
Features of Impala:
- Security: It is integrated with Hadoop security, and Kerberos thus ensures security.
- Expand the Hadoop user: With Apache Impala, users using SQL queries or BI applications can interrelate with more data through metadata store from the source through analysis.
- Scalability: It scales linearly, even in multi-tenant environments.
- In-memory data processing: Apache Impala supports in-memory data processing means that without any data movement, Impala easily accesses and evaluates the data stored on Hadoop DataNodes. Therefore, it reduces cost due to reduced data movement, modeling, and storage.
- Faster Access: Impala provides faster access to data when compared to other SQL engines.
- Easy Integration: We can integrate Apache Impala with BI tools like Tableau, Pentaho, Zoom data, etc.
4. Apache Hive
Apache Hive is a java based data warehousing tool developed by Facebook for analyzing and processing large data. Apache Hive uses HQL(Hive Query Language) alike to SQL that is transformed into MapReduce jobs for processing huge volumes of data. Apache Hive offers support for developers and analytics to query and evaluate big data with SQL like queries(HQL) without writing the complex MapReduce jobs.
Users can interrelate with the Apache Hive through the command line tool (Beeline shell) and JDBC driver. With Apache Hive, one can evaluate or query the huge volumes of data stored in Hadoop HDFS without writing complex MapReduce jobs.
Features of Apache Hive:
• Apache Hive supports client-application written in any language like Python, Java, PHP, Ruby, and C++.
• Hive generally uses RDBMS as metadata storage, which considerably reduces the time taken for the semantic check.
• Hive Partitioning and Bucketing improves query performance.
• Hive is fast, scalable, and extensible.
• It supports Online Analytical Processing and is an efficient ETL tool.
• It provides support for User Defined Function to support use cases that are not supported by Built-in functions.
5. Apache Mahout
Apache Mahout is an open-source framework that generally runs together with the Hadoop infrastructure at its background to manage huge volumes of data. The name Mahout is derived from the Hindi word “Mahavat,” which means the rider of an elephant. As Apache Mahout runs algorithms on the top of the Hadoop framework, thus named as Mahout.
We can use Apache Mahout for implementing scalable machine learning algorithms on the top of Hadoop using the MapReduce paradigm. It is a library of the scalable machine learning algorithm. Formerly, Mahout uses the Apache Hadoop platform, but now it focuses more on Apache Spark. Apache Mahout is not restricted to the Hadoop based implementation; it can run algorithms in the standalone mode as well.
Mahout implements famous machine learning algorithms such as Classification, Clustering, Recommendation, Collaborative filtering, etc.
Features of Mahout:
• It works well in the distributed environment since its algorithms are written on the top of Hadoop. It uses the Hadoop library to scale in the cloud.
• Mahout offers a ready-to-use framework to the coders for performing data mining tasks on large datasets.
• Apache Mahout lets the application to quickly evaluate the large datasets.
• Apache Mahout includes various MapReduce enabled clustering applications such as Canopy, Mean-Shift, K-means, fuzzy k-means.
• It also includes vectors and matrix libraries.
• Apache Mahout exposed various Classification algorithms such as Naive Bayes, Complementary Naive Bayes, and Random Forest.
6. Pig
Pig is an alternative approach to make MapReduce job easier. Yahoo developed Pig to provide ease in writing the MapReduce. Pig enables developers to use Pig Latin, which is a scripting language designed for pig framework that runs on Pig runtime. Pig Latin is SQL like commands that are converted to MapReduce program in the background by the compiler. Apache Pig translates the Pig Latin into MapReduce program for performing large scale data processing in YARN.
Pig works by loading the commands and the data source. Then we perform several operations like sorting, filtering, joining, etc. At last, based on the requirement, the results are either dumped on the screen or stored back to the HDFS.
Features of Pig:
- Extensibility: Users can create their function for performing specific purpose processing.
- Solving complex use cases: Pig is best suited for solving complex use cases that include multiple data processing having multiple imports and exports.
- Handles all kinds of data: Structured and Unstructured can be easily analyzed or processed using Pig.
- Optimization Opportunities: In Pig, the execution of the task gets automatically optimized by the task itself. Thus programmers need to focus on semantics rather than efficiency.
- It provides a platform for building data flow for ETL (Extract, Transform, and Load), processing, and analyzing massive data sets.
7. HBase
HBase is an open-source distributed NoSQL database that stores sparse data in tables consisting of billions of rows and columns. It is written in Java and modeled after Google’s big table. HBase provides support for all kinds of data and built on top of Hadoop. Apache HBase is used when we need to search or retrieve a minimal amount of data from large data sets.
For example: If we are having billions of customer emails and we need to find out the customer name who has used the word replace in their emails. The request needs to be processed quickly, and for such problems, HBase was designed.
There are two main components in HBase. They are:
• HBase Master: HBase Master negotiates load balancing across the region server. It is not the actual data storage. It controls the failover, maintains, and monitors the Hadoop cluster.
• Region Server: Region Server is the worker node that handles the read, write, update, and delete requests from the clients. Region Server runs on the DataNode in HDFS.
Features of HBase:
- Scalable storage.
- It Supports fault-tolerant features.
- Support for real-time search on sparse data.
- Support easily consistent read and writes.
8. Apache Storm
A storm is an open-source distributed real-time computational framework written in Clojure and Java. With Apache Storm, one can reliably process unbounded streams of data (ever-growing data that has a beginning but no defined end). Apache Storm is simple and can be used with any programming language. We can use Apache Storm in real-time analytics, continuous computation, online machine learning, ETL, and more. Including many, Yahoo, Alibaba, Groupon, Twitter, Spotify uses Apache Storm.
Features of Apache Storm:
- It is scalable and fault-tolerant.
- Apache Storm guarantees data processing.
- It can process millions tuples per second per node.
- The storm is easy to set up and operate.
9. Tableau
Tableau is a powerful data visualization and software solution tool in the Business Intelligence and analytics industry. It is the best tool for transforming the raw data into an easily understandable format with zero technical skill and coding knowledge. Tableau allows users to work on the live datasets and to spend more time on data analysis and offers real-time analysis.
Tableau turns the raw data into valuable insights and enhances the decision-making process. Tableau provides a rapid data analysis process, which results in visualizations that are in the form of interactive dashboards and worksheets. It works in synchronization with the other Big Data tools.
Features of Tableau:
• With Tableau, one can make visualizations in the form of Bar chart, Pie chart, Histogram, Gantt chart, Bullet chart, Motion chart, Treemap, Boxplot, and many more.
• Tableau is highly robust and secure.
• Tableau offers a large option of data sources ranging from on-premise files, relational databases, spreadsheets, non-relational databases, big data, data warehouses, to on-cloud data.
• Tableau permits you to collaborate with different users and share data in the form of visualizations, dashboards, sheets, etc. in real-time.
10. R
R is an open-source programming language written in C and Fortran. It facilitates Statistical computing and graphical libraries. We can use R for performing statistical analysis, data analysis, and machine learning. It is platform-independent and can be used across multiple operating systems.
R consists of a massive collection of graphical libraries like Plotly, ggplotly, and more for making visually appealing and elegant visualizations. R language is mostly used by the statisticians and data miners for developing statistical software and data analysis.
R’s biggest benefit is the vastness of its package ecosystem. R facilitates the performance of different statistical operations and helps in generating data analysis results in the text as well as graphical format.
Features of R:
- R provides a wide range of Packages. It has CRAN, which is a repository holding 10,000 plus packages.
- R provides the cross-platform capability. It can run on any OS.
- R is an interpreted language. It does not require any compiler to compile the code. Thus, R script runs in very little time.
- R can handle structured as well as unstructured data.
- The graphics and charting benefits that R provides are unmatchable.
11. Talend
Talend is an open-source platform that simplifies and automates big data integration. It provides various software and services for data integration, big data, data management, data quality, cloud storage.
It helps businesses in taking real-time decisions and become more data-driven.
Talend provides numerous connectors under one roof, which in turn will allow us to customize the solution as per our need.
It offers various commercial products like Talend Big Data, Talend Data Quality, Talend Data Integration, Talend Data Preparation, Talend Cloud, and more.
Companies like Groupon, Lenovo, etc. use Talend.
Features of Talend:
- Talend simplifies ETL and ELT for Big Data.
- It accomplishes the speed and scale of Spark.
- It handles data from multiple sources.
12. Lumify
It is open-source, big data fusion, analysis, and visualization platform that supports the development of actionable intelligence.
With Lumify, users can discover complex connections and explore relationships in their data through a suite of analytic options, including full-text faceted search, 2D and 3D graph visualizations, interactive geospatial views, dynamic histograms, and collaborative workspaces shared in real-time.
Using Lumify, we can get a variety of options for analyzing the links between entities on the graph. Lumify comes with the precise ingest processing and interface elements for images, videos, and textual content.
Features of Lumify:
• It’s infrastructure permits attaching new analytic tools that will work in the background to monitor changes and assist analysts.
• Lumify is Scalable and Secure.
• Lumify offers support for a cloud-based environment.
• Lumify allows us to integrate any open Layers-compatible mapping systems like Google Maps or ESRI, for geospatial analysis.
13. KNIME
KNIME stands for Konstanz Information Minner. KNIME is an open-source, accessible data-analytics platform for evaluating big data, data mining, enterprise reporting, text mining, research, and business intelligence. KNIME helps users to analyze, manipulate, and model data through Visual programming. KNIME is a good alternative for SAS.
It offers statistical and mathematical functions, machine learning algorithms, advanced predictive algorithms, and much more. Many companies, including Comcast, Johnson & Johnson, Canadian Tire, etc. use KNIME.
Features of KNIME:
• KNIME offers simple ETL operations.
• One can easily integrate KNIME with other languages and technologies.
• KNIME provides over 2000 modules, a broad spectrum of integrated tools, advanced algorithms.
• KNIME is easy to set up and doesn’t have any stability issues.
14. Apache Drill:
Apache drill is a low latency distributed query engine inspired by Google Dremel. Drill permits users to explore, visualize, and query large datasets using MapReduce or ETL without having to fix to a schema. It is designed to scale to thousands of nodes and query petabytes of data.
Through Apache Drill, we can query data just by mentioning the path in SQL query to a Hadoop directory or NoSQL database or Amazon S3 bucket. Through Apache Drill, developers don’t need to code or build applications. Regular SQL queries will help the users to get data from any data source and in any specific format.
Features of Apache Drill:
• Apache Drill allows developers to reuse their existing Hive deployments.
• Make UDF creation easier through the high performance, easy to use Java API.
• Apache Drill has a specialized memory management system that eliminates garbage collections and optimizes memory allocation and usage.
• For performing a query on data, Drill users are not required to create or manage tables in the metadata.
15. Pentaho
Pentaho is a tool with a slogan to turn big data into big insights. It is data integration, orchestration, and a business analytics platform that provides support ranging from big data aggregation, preparation, integration, analysis, prediction, to interactive visualization.
Pentaho offers real-time data processing tools for boosting digital insights. It allows companies to analyze big data and generate insights from it, which helps companies to develop a profitable relationship with their customers and run their organizations more efficiently and cost-effectively.
Features of Pentaho:
• Pentaho can be used for big data analytics, embedded analytics, cloud analytics.
• Pentaho supports Online Analytical Processing (OLAP)
• One can use Pentaho for Predictive Analysis.
• It provides a User-Friendly Interface.
• Pentaho provides options for a wide range of big data sources.
• Pentaho permits enterprises to analyze, integrate, and present data through comprehensive reports and dashboards.
Hadoop Analytics Tools Summary
In this tutorial, we have discussed several Hadoop analytics tools such as Apache Spark, MapReduce, Impala, Hive, Pig, HBase, Apache Mahout, Storm, Tableau, Talend, Lumify, R, KNIME, Apache Drill, and Pentaho.
We have discussed all these analytics tools in Hadoop along with their features. This part of the tutorial also explained some other tools built on top Hadoop like Hive, HBase, etc.
Don’t miss the amazing Career Opportunities in Hadoop.