What is Hadoop?

Learn more about this big-data powerhouse

Hadoop is an open-source software utility for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power, and the ability to handle virtually limitless concurrent tasks or jobs. It’s based on the Google File System (GFS).

Hadoop is made up of components such as:

  1. Hadoop Distributed File System (HDFS), the bottom layer component for storage. HDFS breaks up files into chunks and distributes them across the nodes of the cluster.
  2. Yarn for job scheduling and cluster resource management.
  3. MapReduce for parallel processing.
  4. Common libraries needed by the other Hadoop subsystems.

Why Hadoop?

Hadoop runs small software on distributed systems with thousands of nodes with petabytes of data and information. It has a distributed file system, called the Hadoop Distributed File System or HDFS, which enables fast data transfer among the nodes by breaking down the data that it processes into smaller pieces, which are called blocks.

These blocks are eventually distributed throughout a cluster, which allows the map and reduce functions to be executed on smaller subsets instead of on one large data set. This boosts efficiency and processing time, and it shoots up the scalability necessary for processing vast amounts of data.

Various application-execution engines, such as Apache Spark, Apache Storm, Apache Tez, and MapReduce, make it possible to execute analytics=application steps across the cluster on a parallel-processing system.

This is achieved by taking the application logic to the data, rather than taking the data to the application. That’s to say, duplicates of the application or each task within an application are run on every server to process local data physically stored on that server. This method effectively prevents moving data elsewhere in the cluster to be processed.

Hadoop controls costs by storing data at a more affordable rate per terabyte than other platforms. Hadoop delivers compute and storage for hundreds of dollars per terabyte, instead of thousands to tens of thousands of dollars per terabyte.

Hadoop abilities have moved far beyond just web indexing and are now used in various industries for a huge variety of tasks that all share the common theme of high variety, volume, and velocity of data — both structured and unstructured.

Hadoop has fault tolerance and doesn’t get to corrupt data when something fails. Also, if each node experiences high rates of failure when running jobs on a large cluster, data is replicated across a cluster so it can be recovered easily in the face of the disk, node, or rack failures.

Examples of what can be built with Hadoop:

Search: Yahoo, Amazon, Events
Log processing: Facebook, Yahoo
Data warehouse: Facebook, AOL
Video and image analysis: New York Times, Eyealike


HDFS stands for the Hadoop Distributed File System, and it’s the primary data storage system used by Hadoop applications. It basically splits the data unit into smaller units called blocks and stores them in a distributed method.

It employs two architecture s— NameNode (master node) and DataNode (slave node) — to implement a distributed file system that provides high-performance access to data across highly scalable Hadoop clusters that make use of the disk storage on every node in the cluster.

HDFS holds a very large amount of data and provides easier access. To store such huge data, the files are stored across multiple machines. These files are stored in a redundant fashion to rescue the system from possible data losses in case of failure. Various types of data in many different formats can be loaded into and stored in HDFS.

HDFS supports the rapid transfer of data between compute nodes. When HDFS takes in data, it breaks the information down into separate blocks and distributes them to different nodes in a cluster, thus enabling highly efficient parallel processing.

HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware. The file system duplicates, or copies, each piece of data multiple times and distributes the duplicates to individual nodes, having at least one duplicate on different server racks. With this method, the data on nodes that crash can be found elsewhere within a cluster. This ensures processing can continue while data can be recovered.

MapReduce is the data processing layer of the Hadoop System. It’s a software framework and model that allows you to write applications for processing and retrieving a large amount of data. MapReduce runs these applications in parallel and batches on a cluster of low-end machines. It does all these in a reliable and fault-tolerant manner.

MapReduce works in two phases: the map phase and the reduce phase. Each phase works on a part of the data and distributes the load across the processing cluster.

Map phase: This basically loads, parses, transforms, and filters each datum that passes through it. It divides the data into smaller subsets and distributes those subsets over several nodes in a cluster. Each node in the system can go over this process again, resulting in a multilevel tree structure that divides the data into ever-smaller subsets.

Reduce phase: This works on a subset of output data from the map phase. It basically applies grouping and aggregation to this intermediate data from the map phase. The reduce phase collects all the returned data and combines them into some sort of output that can be reused.

What Is Big Data?

Big data is a term used to define a collection of data that’s huge in size and yet growing exponentially with time. It treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with.

Big data is basically a bunch of large datasets (structured, semistructured, and unstructured) collected over time to extract useful information using advanced computing operations. It can’t be processed using traditional computing techniques but is instead used in machine-learning projects, prediction models, and other data-analytics projects.

Big-data projects can impact the growth of an organization. Organizations use the data accumulated over time in their systems to improve operations, provide better customer service, create personalized marketing campaigns based on specific customer preferences, and, ultimately, increase profitability.

For instance, big data can provide organizations key insights into their customers which can then be used to refine marketing campaigns and techniques in order to increase customer engagement and conversion rates.

Big data is also used by medical researchers to identify disease risk factors and by doctors to help diagnose illnesses and conditions in individual patients.

Advantages of Hadoop

  1. It helps reduces the costs of data storage and management.
  2. The Hadoop infrastructure has inbuilt fault tolerance features, and, hence, Hadoop is highly reliable.
  3. Hadoop is an open-source software, and, hence, there’s no licensing cost.
  4. Hadoop has the inbuilt capability of integrating seamlessly with cloud-based services and, thus, helps a lot in scaling.
  5. Hadoop is very flexible in terms of the ability to deal with all kinds of data.
  6. It’s easier to maintain a Hadoop environment, and it’s economical as well.
  7. Hadoop brings better career opportunities and job employment.

You can check out a Full Course on Hadoop where you will learn everything on Hadoop and also get a Certification

Better Programming

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Oyetoke Tobi Emmanuel

Written by

Software Engineer. Python and JavaScript Developer. Technical Writer. https://citguru.github.io. Subscribe to my YouTube channel: http://bit.ly/oyetoketoby

Better Programming

Advice for programmers.

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