As technologies related to data like storage and computing evolved, so did the amount of data, the variety of data, and the data’s velocity. A school of thought emerged, where every piece of data was considered an important piece of the puzzle to help the businesses achieve the ultimate in efficiency, cut wastage, find new streams of revenue and positively impact the bottom line. The conventional methods of storing, retrieving, analyzing the data were found inadequate to handle the influx of data, particularly the 5 vs, velocity, volume, value, variety and veracity. Such data collectively was called Big Data. Along came Hadoop Ecosystem, which was, in fact, a system being developed for a faster search engine based on distributed computing.
Apache Hadoop is an open-source Big Data Framework designed for scalability and reliability. It allows for distributed processing of large data sets across clusters using simple programming models. Clusters can scale up from a single server to thousands of machines, each offering storage and computing. It is a highly available solution that detects failures and automatically adjusts to the loss of machines on the cluster without impacting operations.
In this article let us look at:
The Hadoop ecosystem is a suite of tools or components tightly coupled together, each of which has an individual function and role to play in the larger scheme of data handling. This entire suite of tools is called Hadoop Ecosystem and includes Apache projects and other commercial solutions. Typically, Hadoop Ecosystem consists of four primary components, namely.
All these components in the Hadoop ecosystem are based on the fundamental assumption that hardware will fail at some point in time, and the system handles such failures automatically recovering data and adjusting to the current state of the cluster.
There are other components of the Hadoop Ecosystem that are not really part of the original components of Hadoop but are typically part of the ecosystem in current implementations.
Hadoop Distributed File System is the distributed storage system that is at the core of the Hadoop Ecosystem, making it possible to store different types of data, structured, semi-structured and unstructured efficiently enough using commodity hardware. Under this level of abstraction, the distributed storage system consists of a few name nodes and several data notes spread across the cluster. Name node store s information about the data nodes and where each data node is located within the cluster. Each data node is replicated several times in different locations within the cluster to achieve a significant level of data resiliency and high data availability.
Yet Another Resource Negotiator is the brain behind the Hadoop ecosystem, initiating all processing activities, performing resource allocations and scheduling tasks.
YARN uses two crucial components, ResourceManager and NodeManager. ResourceManager receives requests, processes them to decide which NodeManagers to engage, and forwards them to corresponding NodeManagers. It also receives feedback from NodeManagers after the request processing is complete. NodeManagers are installed on every DataNode and are responsible for processing requests for the respective DataNode.
ResourceManager uses a few other components, Schedulers, ApplicationManagers. The Schedulers run scheduling algorithms and manage the allocation of resources. ApplicationManager accepts jobs, negotiates with containers and monitors the progress of jobs. ApplicationManager interacts with the ApplicationMaster present on each DataNode for the execution of tasks on the respective DataNode.
MapReduce is a programmable framework that helps in writing applications for processing large data sets using parallel and distributed algorithms. Map and Reduce are 2 functions in MapReduce, where Map performs the action of applying functions like filtering, grouping and sorting, while Reduce performs the aggregation on the results from Map functions.
Apache Spark is a framework used for executing real-time analytics in a distributed system. Spark uses in-memory computations to rapidly scale up the speed of data processing. The programs in Spark are written in Scala, R, SQL, Python or Java. Apache Spark and Hadoop are used together in many Hadoop implementations to process Big Data efficiently.
Apache Pig allows you to create Apache Hadoop programs at a high level using a language called Pig Latin, similar to SQL. Hive provides for a data warehousing component on the Hadoop distributed system using an SQL like interface.
HBase is a non-relational database management system capable of working with any kind of data on the distributed computing that Hadoop features.
Apache Mahout allows creating highly scalable Machine Learning applications on Hadoop. Spark MLib is the Apache Spark Machine Learning library.
Apache Zookeeper coordinates with various services running on the Hadoop distributed environment. Zookeeper saves time by synchronizing, configuration maintenance, naming and grouping.
Sqoop is a utility to help transfer structured or data like in Relational databases and NoSQL systems to Hadoop HDFS using bulk export and import.
Flume is a distributed service for performing tasks of collection, aggregation and moving of large chunks of log data. It is a reliable, highly available service with configurable reliability mechanisms.
Apache Ambari is a tool for Big Data administrators to be able to provision, manage and monitor a Hadoop cluster.
Big Data Architecture is simple to understand the system from a high level, but each of the components of the architecture is big subjects on its own. To learn Big Data, you need a training partner who can handhold you through the learning experience and guide your learning in the right direction.
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