Introduction to Apache Hadoop Ecosystem & Cluster in 2021
We live in a world where almost everything around us generates data. Most companies are now embracing the potential of data and integrating loggers into their operations with the goal of creating more and more data every day. This exacerbated the issue of data storage and retrieval efficiency, which cannot be accomplished with traditional tools. To overcome this problem, we need a more specialized framework that contains not just one component, but multiple components that are efficient at performing different tasks simultaneously. And nothing can be better than embracing the Apache Hadoop Ecosystem in 2021 in your company. Apache Hadoop is a Java-based framework that uses clusters to store and process large amounts of data in parallel. Being a framework, Hadoop is formed from multiple modules which are supported by a vast ecosystem of technologies. Let’s take a closer look at the Apache Hadoop ecosystem and the components that make it up.
What Is The Hadoop Ecosystem And Its Benefits?
The Hadoop ecosystem is a collection of big data tools and technologies that are tightly linked together, each performing an important function in data management. There are several advantages of using the Apache Hadoop ecosystem, and we have covered most of them in this section. Let’s take a look!
- Enhances data processing speed and scalability
- Offers high throughput & low latency
- Ensures minimum movement of data in Apache Hadoop cluster (Data Locality)
- Compatible with a wide range of programming languages and supports various file systems
- Open-source framework and fully customizable
- Cost-effective and resilient in nature
- Enables abstraction at different levels to make the work easier for the developers
- Guarantees distributed computing with the help of Hadoop cluster.
- Fault tolerant and backs up every data
- Flexible enough to store different types of data, and is capable of handling organized and unorganized data.
Major Components Of Hadoop Ecosystem
Mainly, the Hadoop Ecosystem comprises of four major components:
- Hadoop MapReduce — MapReduce is a programming paradigm that fastens data processing and enhances scalability in a Hadoop cluster. As a processing component, MapReduce is the most important element of Apache Hadoop’s architecture.
2. Hadoop Common — Hadoop Common is a collection of tools that complement the other Hadoop modules to drive better performance. It is an indispensable component of the Apache Hadoop Framework and holds together the entire Apache Hadoop Ecosystem.
3. Hadoop YARN — Apache Hadoop YARN is a resource and job scheduling manager that is responsible for decentralizing the tasks running in the Hadoop cluster and scheduling them to run on different cluster nodes.
4. Hadoop Distributed File System — HDFS is a distributed file system that distributes data in clusters with no defects, data consistency and high availability. It is a cost-effective method that utilizes commodity storage devices.
Apache Hadoop Ecosystem Architecture
Apache Hadoop Ecosystem is a master-slave architecture for data storage and organized data processing that is accomplished through the use of clusters.
- To Manage Data
- Oozie — Apache Oozie is a Hadoop workflow scheduler, and a system that manages the workflow of interdependent jobs. In Oozie, users can construct directed acyclic graphs of processes, which can be executed in parallel or sequentially.
- Flume — Apache Flume is a data ingestion tool that collects and transports large volumes of data from several sources, such as events, log files, and so on, to a central data repository.
- ZooKeeper — Zookeeper in Hadoop can be thought of as a centralized repository in which distributed applications can store and retrieve data. It helps distributed systems to work together as a single unit.
- Kafka — Kafka handles the streaming and analysis of data in real time. Large-scale message streams are supported by Kafka brokers in Hadoop for low-latency.
2. To Access Data
- Hive — Apache Hive is an open-source data warehousing solution built on the Hadoop platform. It helps in summarizing, analyzing and querying the data.
- Pig — Apache Pig is a powerful platform for developing programs that run on Apache Hadoop using a language called Pig Latin.
- Sqoop — Sqoop is an RDBMS connector designed to support bulk export and import of data from structured data stores to HDFS.
3. To Process Data
- MapReduce — MapReduce is a cluster management model used to handle large sets of data using a parallel, distributed method on a cluster. Mainly, it works in two stages — Map and Reduce. In Map tasks, data is divided and mapped whereas in Reduce tasks, the data is shuffled and reduced.
- Spark — Spark is an open-source distributed framework used to accelerate Hadoop cluster computing process for in-memory data processing.
- YARN — Initially named MapReduce 2, YARN is used to manage clusters and resources, ensuring that everything works well.
4. To Store Data
. HBase — HBase is an open-source distributed database and capable of handling huge databases. In conjunction with Hadoop MapReduce, HBase delivers powerful analytics capabilities.
. HDFS — HDFS is a column-oriented non-relational database management system with an in-memory processing engine that can optimally meet real-time data demands.
To Sum Up
As we’ve seen in this article, Apache Hadoop is supported by a large ecosystem of tools and technologies, making it a strong and profitable framework for any business like yours. Apache Hadoop has a good success rate and many companies like Netflix, Twitter, etc. have adopted this framework and earned billions of dollars. You too can earn profits by constructing an Apache Hadoop ecosystem in your company to process large volumes of data across clusters. But there is a possibility that you may fail to build the Hadoop ecosystem properly. In that instance, you can take the help of a third party like Ksolves for proper implementation of Apache Hadoop. Being the best Apache Hadoop developer in India and USA, consisting of 100+ agile experts from various domains, Ksolves can enhance your startup and make big data analysis a possibility for your company. We ensure the development of a powerful and reliable Apache Hadoop solution that is customized as per your needs. You can contact us anytime to avail Apache Hadoop development and consulting services.