Top and trending Hadoop tools in Big Data

Big Data Certification is one of the most desired skills of 2017 for IT professionals. It gives you an edge over other Professionals to accelerate career growth. The certificate is proof that you can be responsible enough handle huge amount of data. Big Data professionals have many benefits such as better career growth, salary and job opportunities.

Importance of Big Data:

Big Data is becoming popular all over the world. Companies across all verticals like retail, media, pharmaceuticals and much more are pursuing this IT concept. Big Data Hadoop tools and techniques help the companies to illustrate the huge amount of data quicker; which helps to raise production efficiency and improves new data‐driven products and services.

Uses of Hadoop in Big Data:

A Big data developer is liable for the actual coding/programming of Hadoop applications. Mentioned below is some information on Hadoop architecture

• It includes the variety of latest Hadoop features and tools
• Apache Hadoop enables excessive data to be streamlined for any distributed processing system over clusters of computers using simple programming models.
• Hadoop has two chief parts – a data processing framework and a distributed file system for data storage. 
• It stocks large files in the range of gigabytes to terabytes across different machines.
• Hadoop makes it easier to run applications on systems with a large number of commodity hardware nodes.

9 most popular Big Data Hadoop tools:

To save your time and help you pick the right tool, we have constructed a list of top Big Data Hadoop tools in the areas of data extracting, storing, cleaning, mining, visualizing, analyzing and integrating.

Data Extraction Tool:

Talend

Talend is a software vendor specializing in Big Data Integration. Talend Open Studio for Data Integration helps you to efficiently and effectively manage all facets of data extraction, data transformation, and data loading using of their ETL system.

How Talend’s ETL Works:

In computing, Extract, Transform, Load (ETL) refers to a process in database usage and especially in data warehousing. Data extraction is where data is extracted from data sources; data transformation where the data is transformed for storing in the proper format; data loading where the data is loaded into the final target database.

Features of Talend:

ETL tool boosts developer productivity with a rich set of features including:

• Its graphical integrated development environment
• Drag-and-drop job design
• More than 900 components and built-in connectors
• Robust ETL functionality: string manipulations, automatic lookup handling

Pentaho

Pentaho Data Integration also called Kettle is the component of Pentaho responsible for the Extract, Transform and Load (ETL) processes. PDI is created with a graphical tool where you specify what to do without writing code to indicate how to do it.

How PentahoWorks:

PDI can be used as a standalone application, or it can be used as part of the larger Pentaho Suite. As an ETL tool, it is the most popular open source tool available. PDI supports a vast array of input and output formats, including text files, data sheets, and commercial and free database engines.

Important Features of Pentaho

• Graphical extract-transform-load (ETL) designing system
• Powerful orchestration capabilities
• Complete visual big data integration tools

Data Storing Tool:

MongoDB

MongoDB is an open source database that uses a document-oriented data model. MongoDB

How it Works:

MongoDB stores data using a flexible document data model that is similar to JSON. Documents contain one or more fields, including arrays, binary data and sub-documents. Fields can vary from document to document.

Some features of MongoDB Tool

MongoDB can be used as a file system with load balancing and data replication features over multiple machines for storing files. Following are the main features of MongoDB.

• Ad hoc queries
• Indexing
• Replication
• Load balancing
• Aggregation
• Server-side JavaScript execution
• Capped collections

Hive

Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop.

How it Works:

Hive has three main functions data summarization, query and analysis.It supports queries expressed in a language called HiveQL, which automatically translates SQL-like queries into MapReduce jobs executed on Hadoop.

Features of Apache Hive

Apache Hive supports analysis of large datasets stored in Hadoop’s HDFS and compatible file systems such as Amazon S3 file system. Other features of Hive include:

• Index type including compaction and Bitmap index as of 0.10
• Variety of storage types such as plain text, RCFile, HBase, ORC, and others
• Operating on algorithms including DEFLATE, BWT, snappy, etc.

Sqoop

Sqoop (SQL-to-Hadoop) is a big data tool that offers the capability to extract data from non-Hadoop data stores, transform the data into a form usable by Hadoop, and then load the data into HDFS

How it Works

Sqoop got the name from SQL + Hadoop. It supports incremental loads of a single table or a free form SQL query as well as saved jobs which can be run multiple times to import updates made to a database since the last import.

Here are some important and usable features of Sqoop

• Parallel import/export
• Import results of SQL query
• Connectors for all major RDBMS Databases
• Kerberos Security Integration
• Support for Accumulate

Data Mining Tool:

Oracle Data Mining

Oracle Data Mining (ODM), a component of the Oracle Advanced Analytics Database Option, provides powerful data mining algorithms that enable data analysts to discover insights, make predictions and leverage their Oracle data and investment

How it Works

Oracle Corporation has implemented a variety of data mining algorithms inside the Oracle relational database. With Oracle Data Mining system, you can build and apply predictive models inside the Oracle Database to help you predict customer behavior, develop customer profiles, identify cross-selling opportunities and detect potential fraud

Features of Oracle Data Mining

Oracle Data Miner tool is an extension to Oracle SQL Developer, work directly with data inside the database using

• Graphical “drag and drop” workflow and component pallet
• Oracle Data Miner work flows capture and document the user’s analytical methodology
• Oracle Data Miner can generate SQL and PL/SQL scripts

Data Analyzing Tool:

HBase:

HBase is an open source, non-relational, distributed database and is written in Java. It is developed as part of Apache Software Foundation’s Apache Hadoop project and runs on top of HDFS (Hadoop Distributed File System), providing Bigtable-like capabilities for Hadoop

How it works

Apache HBase is a NoSQL database that runs on top of Hadoop as a distributed and scalable big data store. HBase can leverage the distributed processing of the Hadoop Distributed File System. It is meant to host large tables with billions of rows with potentially millions of columns and run across a cluster of commodity hardware

The Apache HBase is consist of the following features:

• Linear and modular scalability
• Convenient base classes for backing Hadoop
• Easy to use Java API for client access
• Block cache and Bloom Filters for real-time queries
• Query predicate push down via server side Filters
• Support for exporting metrics

Pig

Apache Pig is a high-level platform for creating programs that run on Apache Hadoop. Pig is complete, so you can do all required data manipulations in Apache Hadoop with Pig.

How it Works

Pig enables data workers to write complex data transformations without knowing Java. Pig is called Pig Latin, and appeals to developers already familiar with scripting languages and SQL

.

Pig Features:

Pig Tool has the following key properties:

• It is trivial to achieve parallel execution of simple
• Permits the system to optimize their execution automatically
• Users can create their own functions to do special-purpose processing.

Data Integrating Tool:

Zookeeper

Apache Zookeeper is a coordination service for distributed application that enables synchronization across a cluster. Zookeeper in Hadoop can be viewed as centralized repository where distributed applications can put data and get data out of it.

How it Works

Zookeeper provides an infrastructure for cross-node synchronization and can be used by applications to ensure that tasks across the cluster are serialized or synchronized. ZooKeeper allows developers to focus on core application logic without worrying about the distributed nature of the application.

Features of Zookeeper

ZooKeeper will help you with coordination between Hadoop nodes. Following mention the important features of it

• Managing and configuration of nodes
• Implement reliable messaging
• Implement redundant services
• Synchronize process execution

Further, if you want to start a career in Big Data and learn Hadoop and other Big Data technologies, I would recommend you to go through a structured training on Big Data and Hadoop. If you have a structured training in Big Data and Hadoop, you will find it very easy to land up a dream job in Hadoop.

Hope this post helps you. If you have any questions you can comment below and I will be glad to help you out.