What is Mapreduce in Hadoop?

204 million emails, 1.74 million Instagram pictures, 4.2 million Facebook likes, 200,000 Facebook photos, 347,222 tweets, and 300 hours of Youtube video uploads is data that is generated on social platforms per minute. More volumes of data come from other major sources like machine data from IoT devices and transactional data from business transactions. Processing this kind of data in real-time in structured, semi-structured, and unstructured forms require robust systems and frameworks.

Popular frameworks like Spark and Hadoop were developed to handle massive data sets and professionals with credentials like Hadoop certification have a better chance of being recruited to handle these systems.

Importance of Hadoop in big data

Traditional database management systems were only effective as long as they were handling smaller data sets. Came big data and their processing power became limited and capacity too small. With too many applications generating data for businesses to draw insights, discover hidden patterns, and make important decisions, there is a need for a system with the capacity to store data in different forms and process it as required.

Hadoop is important for handling big data in several ways within its three main Vs, volume, variety, and velocity.

  1. Scalability. Hadoop is a distributed framework and as such scales fast and easily on demand. It has the capacity to store and process increasing volumes of data generated mostly from social networks and IoT devices.
  2. High computing power. HDFS coupled with the MapReduce model makes fast and real-time analytics possible for better insights and decision making. Processing power is increased by simply adding computer nodes.
  3. Accommodates variety. Hadoop’s storage can accommodate data in its structured (for example numbers, dates, and addresses), semi-structured (for example JSON and XML files), and unstructured (for example documents, audios, and videos) formats. Storage is simple without the need to alter data to suit any predefined schema and drawing a range of insights from the same data set is possible.
  4. Fault tolerance. In Hadoop, data stored in nodes are replicated in other nodes such that if a node is down, data is available for processing in the other nodes in the cluster. This protects data and processes from hardware failures and other system issues making it fault-tolerant.
  5. Cost-effective. Hadoop is an open-source framework hence it is available to users for free. It also uses low-cost commodity servers which means that users will not incur high initial costs in adopting Hadoop.

What is Hadoop?

Formerly Apache Hadoop, Hadoop is an open-source distributed file system framework used for storing and processing large sets of data. The distributed file system works by storing and running database processes in clusters of commodity servers. Within the clusters, data is stored in nodes and replicated almost instantly in other nodes to enable fast, efficient, and safeguarded data storage and processing. This makes it the best option in big data environments where easy scalability, parallel data processing, and complex analytics are the core of operations. Hadoop is managed by Apache Software Foundation.

Hadoop consists of four key modules including.

  1. Hadoop distributed file system (HDFS) –  As mentioned above, the distributed file system, unlike traditional database management systems, stores, and processes data by replicating it in nodes within clusters. As such, Hadoop provides a high throughput best for big data operations. 
  2. Yet another resource negotiator (YARN). A management platform that monitors and manages resource allocation and scheduling jobs in clusters within the Hadoop ecosystem. YARN consists of 4 components including the resource manager (consisting of job scheduler and application manager), node manager for each node, Application Master, and container (physical resources like CPU cores, RAM, and disks within a single node).
  3. Hadoop Common. Also known as Hadoop Core, is a collection of the libraries and utilities required to run Hadoop modules.
  4. MapReduce. MapReduce is a programmable framework that supports distributed and parallel data processing across clusters in HDFS. It features that map and the reduce functions.

What is MapReduce?

As we have already seen, MapReduce is one of the core components of Hadoop. MapReduce framework, as Hadoop’s processing engine, enables parallel and distributed processing across data sets. This is the feature behind Hadoop’s fast processing and easy scalability.

Parallel processing happens where processing tasks are split into smaller chunks and then run at the cluster nodes within which data is stored. Processes within nodes run concurrently. The results for each task are then put back together into one meaningful result that the user can apply to business operations.

Parallel processing takes place within two main MapReduce functions derived from its name.

  • Map. The map function processes sets of input data. Data sets are split, sorted, and grouped into smaller batches. Elements of these smaller sets are broken down into key/value pairs (tuples) and processed to generate output key/value pairs.
  • Reduce. Once the mappers have completed the processing, the reduce function takes the key/value output from them and aggregates it back to results for users. It is important to note that the reduce function cannot be launched while the mappers are still processing. Processing has to be complete for the reduce function to run.

Benefits of MapReduce

The MapReduce function in Hadoop offers several advantages in processing and deriving value from big data. Firstly, it Enables easy scalability. This is the one advantage that Hadoop boasts of when it comes to handling big data and it draws from the MapReduce component. Large sets of data stored in HDFS can be processed fast and easily on demand.

Secondly, it is flexible. The MapReduce function allows businesses access to and process multiple sources and types of sets to derive value. It also supports several programming languages including Java, Python, and C++.

Why should you learn Hadoop?

Some big names using Hadoop frameworks include Facebook, Twitter, LinkedIn, Yahoo, Amazon, eBay, Netflix, and many others. More than 27,000 companies are currently using Hadoop and many more are relying on big data analytics to solve business problems, gain insights, and draw up strategies for business and competitive growth. As had been predicted, at least 80% of fortune 500 companies will have adopted Hadoop to run their big data initiatives.

Hadoop is today one of the major big data technologies that organizations are leveraging for growth and competition. Big data has certainly redefined the business landscape as it allows them to process large volumes of data from various sources in real-time. Hadoop is not only useful enterprise-wide but also at the departmental level as departments access data that is relevant to them.

What does this mean?

The high adoption of big data has created immense opportunities for data analysts, data scientists, and other professionals with Hadoop skills. Because big data is everywhere, literally, and its impact in the business world cannot be ignored, it is important for individuals to upgrade their skills by taking a training course in Hadoop and honing their skills. While this is not the only way to acquire information and experience, it is the surest way of getting started.