All You Need To Know About Hadoop

 Hadoop is the trending and popular platform for big data processing and it is used to store and process huge amounts of data. Hadoop is the best and open-source software framework that provides numerous components for global companies to implement various big data projects. It is offering massive storage space for all kinds of data with exponential processing power and the ability to support an unlimited number of virtual tasks. Hadoop is developed using Java and it is sponsored by Apache Software Foundation.

Brief History of Hadoop

Search engines were created to help locate information that people are searching on the internet. Initially, the search results are returned by humans manually and when the number of pages is added to millions, automation became essential for returning search results. Then came Yahoo and AltaVista, Nutch, and Google. The main goal of these search engines is to deliver web search results faster using data distribution techniques and calculations.

The Nutch project is created by Doug Cutting and Mike Cafarella and it was divided into several parts. Then Cutting has kept the name for distributed computing and processing and the name was Hadoop which is his son’s yellow elephant plush. In 2008, Yahoo has offered the open-source project Hadoop and then today the framework and ecosystem of Hadoop are managed by Apache Software Foundation.

Hadoop 1 was introduced to the public in November 2012 as a monolithic component with the features like resource management, execution engine, and user API. The second version Hadoop 2.0 was introduced with the features like layered architecture, resource management with YARN, execution engine with Tez, and User API with Hive, Cascading, and Pig. The current version of Hadoop is 2.6.5 and it offers various data processing and analysis features for global users.

Cloud Service Providers are supporting Hadoop components with basic services like AWS Elastic Compute Cloud and Simple Storage Services. AWS Elastic MapReduce, Google Cloud Dataproc, and Microsoft Azure HDInsight are also offering services for performing various Hadoop tasks.

Why Hadoop?

The benefits of Hadoop are enormous and it provides possibilities of storing and processing vast amounts of data faster than to be linked with social networks and IoT easily and effectively.

The Distributed Computing Model allows big data to be processed quickly with Hadoop and it provides greater processing power for the greater number of computing nodes used.

Hadoop offers protection to hardware failures for protecting data and processed applications. If any node goes down, tasks will be redirected to another possible node with the assurance of distributed computing does not fail. The copies of data are stored automatically in the Hadoop platform.

There is no need for pre-processing of data before storing them which is required in traditional relational database applications and it is possible to store unlimited data and decide how to use them in the future.

Hadoop will accept data in various formats such as images, videos, and text. Hadoop is an open-source framework and users can rely on standard machines for storing and processing large amounts of data. It is also possible to adapt the system to help with more data by simply adding nodes with a minimal requirement of the administration process.

Challenges of using Hadoop

MapReduce is not suitable for all the data problems as it is useful for processing simple queries that can be divided into independent units. It is also not effective for iterative and interactive analytical processing as nodes are not communicate with each other.

There is always a large skill gap as it is very difficult to find proficient Java programmers required for productive MapReduce processing.

The security concern of fragmented data is another challenge and it is not possible without third-party tools and technologies that are emerging in today’s market.

There is no intuitive and complete tool for data management in the Hadoop platform and the same for cleaning, metadata, data quality, standardization, and data governance.

What are the uses of Hadoop in businesses?

Hadoop is used by many popular companies for big data processing as it provides unimaginable functionalities for searching millions of web pages that are relevant for search queries. Following are the advantages of Hadoop for today’s businesses.

Low-cost storage and data archiving

Data processing platforms will become very useful for storing and combining data using the modest cost of standard machines. The data that is coming from social networks, scientific data, machines, or clickstreams are using this low-cost storage facility that makes the possibility of information safety.

Sandbox for analysis and discovery

The sandbox is designed to process large amounts of data that are of different shapes. Hadoop provides the ability to handle analytical algorithms and the analytical tools help the businesses operate more efficiently by discovering new opportunities and gaining competitive advantages. It offers opportunities for medium innovation with minimum investment.

Data Lakes

Data lakes are supported in data storage with original format. The main goal is to offer a raw and unrefined view of data that helps data scientists and analysts to discover insights easily. It helps them to find new and complex queries with constraints and it becomes necessary for developing logical data structures according to the data federation techniques.

Complement for Data Warehouses

Hadoop is used along with data warehouse platforms and the datasets and new types of data are offloaded directly from data warehouses to Hadoop. The primary goal of businesses is to have the proper platform for storing and processing data of various schemes and formats with various use cases that can be integrated with different levels easily.

IoT with Hadoop

The main goal of the Internet of Things (IoT) is connecting objects to know what to interact with and when to act through continuous data streaming facilities. Hadoop is used to store billions of transactions and the massive storage allows the big data platform to use a sandbox for discovering new patterns with prescriptive instructions.

Recommendations Engine

Hadoop is used in building recommendation systems on the web and many top companies are using this framework for analytical tools to gain kind of services. Facebook is used to suggest friends for people, LinkedIn is used to refer jobs that people are interested in, and Netflix is using content recommendations.

The architecture of the Hadoop Framework

There are four core modules in the core Apache Foundation Hadoop Framework and it has libraries and utilities used by other modules.

HDFS (Hadoop Distributed File System) is a scalable and java-based platform used to store data on various machines.

YARN (Yet Another Resource Negotiator) allows users to manage the resources for data processes that are carried in the Hadoop Platform.

MapReduce is the framework used in parallel processing that combines two stages that are Map step that allows users to retrieve entities to divide them into smaller nodes and the master node combines the responses of all subproblems to offer a final result.

Following are the various components that work with the Hadoop framework for achieving a high level of reputation for Apache projects.

• Ambari — A web interface for managing, configuring, and validating Hadoop services and components.
• Cassandra — A distributed database system
• Flume — A software for collecting and aggregating large amounts of data streams under HDFS
• HBase — A non-relational and distributed database used to run on top of Hadoop particularly for MapReduce tasks.
• HCatalog — A storage and table management tool that enable users to share and access data
• Hive — A data warehouse and SQL query language that presents data in tabular form
• Oozie — Used to allow the planning of tasks in the framework
• Pig — A platform for data manipulation for HDFS along with the compiler for MapReduce
• Solr — A scalable search tool that includes indexing, central configuration, and recovery.
• Spark — A open-source cluster computing framework for analytical processing.
• Sqoop — A connection and transfer mechanism for migrating data between Hadoop and relational database applications.
• Zookeeper — An application used to coordinate distributed treatments.

The future of the Hadoop platform

The global market value of Hadoop was $ 5 billion in 2015 as per the study of Zion Research and it is expected to reach $59 billion in 2025. The following are the reasons to prove that the rise is possible

• The increased volume of structured and unstructured data in large enterprises.
• Various sectors like healthcare, manufacturing, finance, defense, and biotech are in major need of fast and efficient data solutions to monitor data processes.
• The development and updates bring new opportunities for global learners.
• The security and distribution for high-level distributed data processing.

Global big companies such as Amazon Web Services, Teradata Corporation, IBM Corporation, Cisco Systems, Oracle Corporation, VMWare Solutions, and OpenX are using Hadoop and it is the big reason for assured growth of the market value of the Hadoop platform.

Conclusion

The latest version of HDFS opens up new perspectives for machine learning and deep learning professionals as it offers a big space for storing algorithms in a safe environment. The learning of the Hadoop system brings global wide opportunities for the learners with promising career growth. Enroll at Softlogic Systems to learn the Best Hadoop Training in Chennai with Best Practices.