Hadoop Ecosystem is not a programming language nor a service; rather, it is a framework or platform that solves issues with large amounts of data. It is a suite that incorporates many services, such as ingesting, storing, analyzing, and preserving within itself.
What is Hadoop Ecosystem?
Hadoop is a generic phrase that may be used to refer to any of the following:
- The whole Hadoop ecosystem, includes both the core modules and associated sub-modules.
- Hadoop’s fundamental modules, such as Hadoop Distributed File System (HDFS), Yet Another Resource Negotiator (YARN), MapReduce, and Hadoop Common. These are the fundamental components of a standard Hadoop installation.
- The Hadoop-related sub-modules are Apache Hive, Apache Impala, Apache Pig, Apache Zookeeper, and Apache Flume. These related pieces of software may be used to modify, enhance, or expand Hadoop’s fundamental capabilities.
What are Hadoop Ecosystem Modules?
Hadoop ecosystems are categorized into 4 major modules. These modules operate together to deliver services like data absorption, analysis, storage, and maintenance. Most tools or solutions are utilized to augment or assist these key components. The modules are listed below:
- HDFS: Hadoop Distributed File System (HDFS) – HDFS is a Java-based technology that enables big data sets to be fault-tolerantly stored across nodes in a cluster.
- YARN: Yet Another Resource Negotiator (YARN) – Hadoop’s YARN is used to manage cluster resources, plan and schedule activities, and schedule jobs.
- MapReduce: The MapReduce framework combines a programming approach with a big data processing engine to facilitate the parallel processing of massive datasets. In the beginning, Hadoop simply supported MapReduce as an execution engine. However, Hadoop eventually expanded to include support for more frameworks, such as Apache Tez and Apache Spark.
- Hadoop Common: The other Hadoop modules are supported by Hadoop Common, which offers a collection of services that may be used across libraries and utilities.
What are Hadoop Ecosystem Components?
HDFS: The Hadoop Distributed File System is the hub and center of the whole data management infrastructure. With this feature, we can control massive data sets that span both structured and unstructured data stores. At the same time, it keeps the Metadata updated through log files. HDFS’s NameNode and DataNode are its auxiliary parts.
NameNode: NameNode is the primary Daemon in Hadoop HDFS. This part is responsible for managing the filesystem’s namespace and controlling how users may access files. This node, which maintains information such as the total number of blocks and where they are located (Metadata), is often referred to as the Master node. File system actions such as renaming, shutting, and opening files are all part of its makeup.
DataNode: The second part, known as the DataNode, is the slave Daemon. This part of HDFS is responsible for storing data or blocks while also processing read and write requests from clients. This implies that DataNode must also create, delete, and replicate replicas in accordance with directives from the Master NameNode.
Two system files make up the DataNode; one stores information, while the other keeps track of block metadata. The Master and Slave daemons do a “handshake” during programme startup to check the namespace and software version. In case of discrepancies, the DataNode would be shut off immediately.
MapReduce: MapReduce processing is crucial to Hadoop. This simple framework allows programmers to handle enormous amounts of organized and unstructured data. It supports parallel data processing on several nodes utilizing cheap commodity hardware. Clients schedule MapReduce tasks. User requests are split into smaller, self-contained tasks. These MapReduce workloads are divided into smaller tasks that commodity computer nodes and clusters can perform.
Map phase and Reduce phase accomplish this. The Map phase performs “Mapping” a dataset creates a key/value pair dataset. The Reduce phase performs the programmer-specified modifications to the output by the InputFormat class.
MapReduce requires programmers to define two primary operations. The Map method does the actual work of processing data. When applied to the intermediate data returned by the map function, the Reduce function generates the final output by summarising and aggregating the data.
YARN: Hadoop YARN, put simply, is an upgraded and more modern alternative to MapReduce. YARN is also utilized for processing task sequences and scheduling their execution. YARN, on the other hand, is Hadoop’s layer for managing resources, and it treats data processing jobs as independent Java programs.
YARN is the framework’s OS, allowing for streamlined management of tasks like batch processing and storing data. By far surpassing MapReduce, YARN enables developers to create dynamic, real-time streaming applications.
By using YARN, developers may deploy as many apps as they need to a single node. It provides a reliable and steady base for managing and sharing system resources for optimum productivity and adaptability.
Examples of Popular Hadoop-Related Software:
Here’s the list of popularly used alongside the core Hadoop modules but not technically part of them are the following software packages:
- Apache Hive: Data warehouse software, Apache Hive, is a Hadoop component that provides access to the HDFS file system and a SQL-like query language, HiveQL.
- Apache Impala: It is the native analytic database for Hadoop and is freely available to the public.
- Apache Pig: It is an abstraction over MapReduce used for analyzing big data sets in the form of data flows; it is often used in conjunction with Hadoop. Joining, filtering, sorting, and loading are all possible using Pig.
- Apache Zookeeper: Highly dependable distributed processing is made possible by Apache Zookeeper, a centralized service.
- Apache Sqoop: When you need to move large amounts of data from Apache Hadoop to more conventional, structured data stores like relational databases, you may rely on Apache Sqoop.
- Apache Oozie: Workflow scheduling in Apache Oozie is used to coordinate tasks running on Apache Hadoop. Actions in an Oozie Workflow task are represented as a Directed Acyclic Graph (DAG).
How to Use Hadoop for Analytics?
Hadoop in Corporate Data Centers:
This is frequently the most efficient and cost-effective choice for companies that already have the resources they need on hand. If not, the cost and time commitment associated with assembling the necessary IT infrastructure might overwhelm available resources. There is little doubt that this alternative gives enterprises more say over data privacy and security.
Hadoop Using Cloud:
Cloud-based services provide faster installation, lower upfront costs, and less upkeep. Although limited, these services speed up massive data processing at a minimal cost. Cloud providers use common hardware for data and analytics.
On-premises Reliability, privacy, and security are all offered by Hadoop service providers. These merchants provide all necessities. They ease the procedure by providing the necessary tools, programs, and services. Since your infrastructure is already on-site, however, you may utilize data centers just like major corporations.