Understand: How the components of the Hadoop ecosystem fit in with the data processing lifecyclE?
Hadoop is a framework under Big Data, a collection of huge data sets helps in the processing of these heavy data sets. It consists of various modules supported by a large ecosystem of different technical elements. In this context, the Hadoop Ecosystem is a powerful platform or suite that provides resolutions to various Big Data issues. There are several components of the Hadoop Ecosystem that have been deployed by various organizations for various services. Moreover, these components of the Hadoop Ecosystem are developed to deliver an explicit function.
In this article, we will come to know about the different components of the
Hadoop ecosystem and its usefulness in the data processing lifecycle.
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Components of the Hadoop ecosystem
There are four major components of Hadoop such as HDFS, YARN, MapReduce & Common utilities. But some other components collectively form a Hadoop ecosystem that serves different purposes. These are;
Mahout, Spark MLib
Let’s discuss the above-mentioned Hadoop ecosystem components in detail.
HDFS or Hadoop Distributed File System is the major component of the Hadoop ecosystem. It is responsible for storing large data sets inclusive of structured or unstructured data. Moreover, it stores them across different nodes and also manages the metadata in the form of log files.
The core components of HDFS are as follows;
NameNode is the primary node that includes metadata of all the blocks within the cluster. It also manages the Data Node that stores the actual data. The Data Nodes are commodity hardware in the distributed ecosystem that runs on the slave machine. Moreover, it makes the Hadoop ecosystem cost-effective.
HDFS works at the heart of the system by maintaining all the coordination among the clusters and hardware. It helps in the data processing lifecycle as well.
It is one of the core data processing components of the Hadoop ecosystem.
MapReduce is a software framework that helps in writing applications by making the use of distributed and parallel algorithms to process huge datasets within the Hadoop ecosystem. Moreover, it transforms big data sets into an easily manageable file. MapReduce also takes care of failures of systems by recovering data from another node in the event of break down.
There are two important functions of MapReduce, namely Map() and Reduce(). Map() — function performs different actions like sorting, grouping, and filtering of data. Besides, it organizes this data in the form of a group. It takes in key-value pairs and generates the results as key-value pairs.
Reduce() — function aggregates the mapped data. Moreover, the Reduce()
function takes the results generated by the Map() as input and makes together
those tuples into smaller sets of tuples.
YARN or Yet Another Resource Negotiator is considered as the brain of the
Hadoop ecosystem. It helps to manage resources across clusters and performs the processing jobs like scheduling and resource allocation. YARN has two major kinds of components: Resource & Node managers.
Resource Manager: This is the major node in the data processing
department. Therefore, it receives process requests & distributes resources for the applications within a system and schedules map-reduce jobs.
Node Manager: These are installed on the DataNode that works in the
allocation of resources. Such as CPU, memory, bandwidth per system, and
monitors their usage & activities.
Application Manager: It acts as an interface between the Resource and
Node Managers and communicates as required. Moreover, it is the
component of the Resource Manager. Another component of the Resource
Manager is Scheduler.
Spark is al platform that unifies all kinds of Big Data processing like batch
processing, interactive or real-time processing, and visualization, etc. It includes several built-in libraries for streaming, SQL, ML, and graph processing purpose.
Moreover, Spark provides a lightning-fast performance for batch and stream
processing. It also handles the process of consumptive tasks like above.
Apache Spark consumes in-memory resources as well, thus being faster in terms of optimization.
Hive is based-out of SQL methodology and interface and its query language are known as HQL. The Hive supports all types of SQL data that makes the query processing simpler & easier. Moreover, the Hive comes with two basic components: Such as JDBC Drivers and the HIVE Command-Line. It is highly scalable and it allows both real-time and batch processing facilities. Furthermore, the HIVE also executes various queries by using MapReduce. Hence, a user doesn’t need to write any code in low-level MapReduce.
Pig works on a pig Latin language, a Query processing language similar to SQL. It structures the data flow, processes, and analyzes large data sets stored in HDFS.
Pig does the execution of commands and also takes care of all the MapReduce
activities. Later the processing ends, PIG stores the output in HDFS. Pig includes specially designed components like Pig Runtime & Pig Latin.
Mahout provides a platform that allows Machine Learning ability to a system or application. Machine learning helps the system to develop itself based on some past data or patterns, user interaction, or based on algorithms. Moreover, it provides different types of libraries that are nothing but the concepts of Machine learning. These are collaborative filtering, clustering, and classification. bIt's a NoSQL database built on top of the HDFS system. It supports all kinds of data and provides the capabilities of Google’s Big Table. Thus, it can work on Big Data sets very effectively. Moreover, HBase is an open-source and distributed database. It provides real-time read/write access to big data sets efficiently.
There are two major components of HBase such as:
There was a huge problem of managing coordination and synchronization among the different components of Hadoop that resulted in inconsistency. Zookeeper overcomes all these problems by performing synchronization, inter-component communication, grouping, and so on.
The component Ambar is responsible for managing, monitoring, and securing the Hadoop cluster effectively.
Hue is the full form for Hadoop User Experience. It’s an open-source web
interface for Hadoop & it performs the following operations:
Upload the data and browse it.
Table queries in HIVE and Impala
Moreover, Hue makes Hadoop easier to use.
Sqoop is one of the components of Hadoop that imports data from external
sources into the Hadoop Ecosystem components. Such as; HDFS, Hive, HBase, and many more. It helps to transfer data from Hadoop to other external sources and it also works with RDBMS like Teradata, Oracle, MySql, etc.
Flume is a distributed, reliable, and available component service for efficiently collecting, and moving huge amounts of streaming data from different web servers into HDFS. Moreover, it has three different components: Source, channel, and sink.
It simply performs the task of a scheduler that schedules various jobs and binds them together as a single unit.
Big Data processing lifecycle
Big Data processing lifecycle includes four different stages: Ingest, Processing,
Analyze, and Access. Each stage has a different strategy and each stage includes the usage or help of components of the Hadoop ecosystem. Let us elaborate them in detail.
This is the first stage of Big Data processing. Here, the data is ingested or
transferred to Hadoop from different sources like relational databases, systems, or local storage files. Moreover, in this stage the component Sqoop transfers data from RDBMS to HDFS and Flume transfers event data.
Processing is the second stage in this lifecycle where the data is stored and
processed. The data is stored in the HDFS, and the NoSQL distributed data, HBase,etc. Spark and MapReduce perform a data processing job at this stage.
Analyzing is the third stage where the data is analyzed by processing different
frameworks like Pig, Hive, and Impala.
Here, the component Pig converts the data by using a Map and Reduce and then analyzes it. Moreover, the Hive is also based on the map and reduces
programming. This is most suitable for structured data much effectively.
The fourth & final stage in this lifecycle is Access performed by tools such as Hue and Cloudera Search. In the Access stage, the analyzed data can be accessed by users and clients as well.
Thus, we reach to a conclusion in this article where we learned about How the
components of the Hadoop ecosystem fit in with the data processing lifecycle.
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