In the 1990’s, people in the IT field used to say ” let’s make a Beowulf cluster” to figure out problems and now they say “let’s use Hadoop” to solve the problems with large volumes of data. The world of big data is complicating with Digitalization and boom in the technology and massive amounts of data generating day by day are blocking the servers. Substantially, Hadoop came to the rescue and started speedy processing of big data. Before Hadoop, data storage was expensive and now the scenario is different.
Overview of Hadoop:
Hadoop is an open-source framework and is developed in the year 2005. It is utilized for accumulating and analyzing big data in a distributed manner on huge clusters of commodity hardware. With Hadoop’s commodity hardware, storing and processing of large datasets is cost-effective and efficient as well.
Efficiency can be measured by performing a batch process in parallel. There is no need for data to be moved across the network to the central processing node. It is something like breaking big problems into smaller problems and solving each independently and combining the outcomes of each and derive a final answer at the end. The cost-effectiveness can be assessed with the use of commodity hardware. The large datasets are divided and stored in the normal sized local disks.
Every day, a lot of data is generated in the web. Google needs to index these data to have an effective search for a web crawler, which is a tough task. To simplify this, Google created MapReduce programming type of processing.
MapReduce uses two functions, Map and Reduce. Map’s activity is to convert dataset into value pairs and Reduce’s activity is to join the outcomes of map activity into a single output. Developers who were working on Apache’s ‘Nutch’ web crawler project adopted this MapReduce approach for problem-solving and then developed “Hadoop”.
Hadoop is comprised of four components:
As explained earlier, This component helps in computing large data projects by breaking into smaller pieces and combines the result of each piece and find a final answer for the project.
Hadoop Distributed File System
This HDFS component provides distributed storage of big data files. HDFS divides the big data into smaller parts and distributes it across the nodes in the cluster. This component also creates copies of data for repetition and reliability purpose.
If any node fails then automatically HDFS will access the data from one of its replicas. In this component, data managed may be structured or unstructured and supports almost any format of data. However, Hadoop does not require HDFS but file systems such as MapR or S3 can be used in Hadoop.
Yet Another Resource Negotiator(YARN)
This component helps in scheduling services and managing Hadoop cluster’s computing resources. With YARN, Hadoop can process other frameworks besides MapReduce. In addition to batch job processing, This feature supports the real-time and interactive computation of streaming data.
This library supports and utilizes the other three components.
Though Hadoop can run on a single machine, it runs on cluster configuration. A cluster can range from few nodes to thousands of nodes.
Here are some of the reasons, why Hadoop is preferred by organizations. Hadoop is used for its ability to manage, store and analyze unstructured data and structured data at low cost, flexibility, scalability, and reliability.
Hadoop is an open-source platform that runs on low-cost commodity hardware when compared to other proprietary software.
Unlike traditional RDBMS, there is no need to create a structured schema before storing the data. Here, data can be stored in any format, including structured or semi-structured format, and then disintegrate and apply the schema to the data when read.
The distributed processing of data local to each and every node in the cluster allows Hadoop to manage, store, analyze and process data at petabyte measure or scale.
Sometimes large computing clusters are sloped to the failure of individual nodes in the cluster. Hadoop software is fundamentally strong- when a node fails in the cluster then processing is re-directed to remaining nodes in the cluster and automatically data is re-replicated in preparation for forthcoming node failures.
Hadoop Evolving & Growing:
It wouldn’t be an exaggeration to say that Hadoop has evolved as a rescue to manage big data and is the only cost-effective and scalable open-source platform available in big data management space. The below picture may give you some insights of Hadoop market.
Business Capabilities of Hadoop
Since many years, organizations are wrestling to figure out how to handle with the new data type that is emerging from various sources and disparate systems. Today’s digital world generates data from small devices like smartphones to heavy machines like production line sensors.
Apache Hadoop came as a rescue to process and store unstructured big data, providing the businesses a recognizable competitive edge across industries in different essential functional spaces. Among them, below five business functionalities play a key role in benefitting the enterprises.
Business people and customers use different platforms and strategies for communications and transactions. With the boom in the digital arena and increasing number of social media channels, data silos are flooded with volumes of data and have become difficult for businesses to understand it and what customers want.
Hadoop can be adopted and used to cost-effectively analyze and integrate distinct data and gain key insights of the customer needs, improve revenue and enhance personalized real-time customer relationships.
Special use cases in Marketing Optimization includes advertising optimization, social media analysis, clickstream analysis, recommendation engine and targeting, and 360-degree customer view.
For instance, SPINS, a business intelligence, and data analytics company, provides retail measurement, customer insights, analytics reporting, product libraries, and consulting services for manufacturing and retail clients. SPINS using MapReduce operating on Cisco UCS servers to consume retail POS data and to manage key processing for analytics.
Security & Risk Management:
As security violations and fraudulent acts are becoming more frequent and complex, legacy security solutions are not that capable to challenge these issues and protect the company assets.
Hadoop enables your organization pace up threat analysis, analyze large volumes of dissimilar data in real-time, increases the ability to assess risks with highly developed machine learning modules.
For instance, Managed Security Service Provider(MSSP), a leading solution company in providing security services implemented Cisco UCS CPA for big data with MapReduce and developed their security solutions to the next level.
This joint solution helped the company for a solution to execute real-time analysis on big data and protected and defend against the organized and sophisticated attackers. Special use cases in this segment include application log monitoring, event management and security information, risk modeling, fraud detection and network intrusions detection.
Enterprise Data Hub(EDH):
Hadoop can benefit as a cost-effective EDH to transform, filter, cleanse, store, analyze and derive new value from all types of data. Framing a successful Enterprise Data Hub starts with choosing the right technology in 3 key entities: data processing platform, a foundation system to drive EDH software and infrastructure.
For instance, Cisco UCS integrated infrastructure for big data can be dependably run your EDH. this solution liberates a highly scalable platform that is demonstrated for enterprise applications and can be set up with a Hadoop distribution like MapReduce, well-suited to take the benefits of the I/O bandwidth and compute of Cisco UCS. Special use cases in EDH include gathering raw data in a data silo, big data exploration, data refining, mainframe optimization and data warehouse optimization.
Internet of Things(IoT):
In the near future, Cisco assesses with an approximate calculation that there will 10 billion affiliated things delivered annually, and a total of 50 billion things in practice. You can discover huge opportunities for your business by using Hadoop architecture, that protects and access the big data that comes from these devices.
Special use cases in IoT include group IoT(tourist group, a family in a smart house), personal IoT( fitness devices, smartphones), community IoT( roads and smart cities) and industrial IoT( retailer supply chains, smart factories). The duo, MapReduce, and Cisco facilitate you such an architecture that can manage huge amounts of big data in real-time scenarios on the Internet of Things.
To be successful and competitive, organizations need to regularly be looking for methods to improve their business profitability and productivity. And if felt that analyzation and optimization are satisfactory, profound changes can be made in operational landscape for greater improvements.
By noting a wide variety of particulate measurements from sensors, you can identify patterns in operations to find new techniques to optimize your business. Both Cisco and Hadoop can provide you the strength to calculate data at the speed your organization demands.
Special use cases in Operational Intelligence include preventive maintenance, logistics and supply chain, assembly line quality assurance, smart meter analysis, exploration and production optimization.