What is the Hadoop Ecosystem? – The Complete Guide

Doug Cutting and Mike Cafarella created Hadoop, a data storage and processing system. The name “Hadoop” was inspired by a toy elephant that belonged to Doug Cutting’s (one of Hadoop’s developers) children. They chose to call it Hadoop because, like the toy elephant, it was durable and capable of handling massive volumes of data. In this blog, we will explore the meaning and components of the Hadoop ecosystem.

What is the Hadoop Ecosystem?

The Hadoop ecosystem is a group of free and open-source programs and frameworks that support Hadoop in tackling various big data processing and analytics challenges. These add-ons strengthen Hadoop’s capabilities and make it a strong platform for managing and drawing conclusions from huge and complex datasets. Let’s take a brief look at the main elements of the Hadoop ecosystem.

• HDFS
• YARN
• MapReduce
• Apache Spark
• HIVE
• PIG 
• HBase
• Drill
• Zookeeper
• Oozie
• Mahout
• MLlib
• Flume
• Sqoop
• Ambari
• Solr 
• Lucene

You can also learn more about data analysis by taking this in-depth SQL course. 

Components of the Hadoop Ecosystem

Now, let us understand all of the components within the Hadoop ecosystem in detail below.

1. Hadoop Distributed File System (HDFS) 

Hadoop’s core storage system, HDFS, is designed to deal with large files by breaking them into small blocks and spreading the data among multiple computers. Copying each block several times across different systems, it ensures that the information stays reliable. This scalable architecture provides fault tolerance when handling a vast amount of data. HDFS, or Hadoop Distributed File System, consists of two major components: the NameNode and the DataNode. 

i. NameNode 

The NameNode is also referred to as the Master node. It does not contain any of the actual data or dataset, but rather stores Metadata, which includes details about how many blocks are there and where they’re located. This consists of both files and directories alike. Thus, it requires less storage but demands higher computational resources.

ii. DataNode

DataNode is also referred to as Slave and is responsible for storing actual data in HDFS.  All your stored information is kept on DataNodes, which necessitates more space for storage needs. These are generally commodity machines (similar to what you find in home computers) situated within a distributed environment. It carries out read or write requests issued by the clients. 

2. Yet Another Resource Negotiator (YARN)

YARN, the resource management system of Hadoop empowers numerous processing engines to work in parallel. By distinguishing resource administration from task execution, YARN permits a variety of applications, such as MapReduce, Spark, and Hive to productively use cluster resources. It makes sure that cluster assets are fully maximized by optimizing their allocation, scheduling, and monitoring. 

YARN has three main components:

• Resource Manager: The Resource Manager has control over allocating resources for applications in the system.
• Node Managers: They are responsible for allocating CPU, memory, and bandwidth on each machine before notifying the Resource manager. 
• Application Manager: Lastly, the role of an Application Manager lies between these two, i.e., interacting with both managers as necessary when specific requirements are met by either side.

3. MapReduce

MapReduce is a programming model and associated processing engine in Hadoop used for parallel computation of large datasets. It involves the following two main steps.

• The Map Step: It splits data into small components to be processed independently, and then transforms them into key-value pairs. 
• The Reduce Step: In this step, the results are collated and summarized to arrive at an overall output.

The features of MapReduce include the following:

• MapReduce offers simplicity as it can be written in any language, such as Java, C++, and Python.
• The scalability of MapReduce allows it to process petabytes of data with ease. 
• By parallel processing, problems that take days to solve are completed within hours or minutes using MapReduce.
• Fault tolerance is another valuable feature provided by this distributed computing framework. If one copy of the data becomes unavailable, a different machine has access to it, which ensures task completion without disruption.

3. Apache Spark

Hadoop works seamlessly with Apache Spark, a widely used data processing engine. It allows fast calculations due to its capability of in-memory data processing. Furthermore, it provides libraries for machine learning operations, graph computations, and real-time streaming, all while accommodating multiple programming languages. The following are the features of Apache Spark:

• Apache Spark is a platform that provides for process-intensive tasks, such as batch processing, interactive or iterative real-time processing, graph conversions, and visualization. 
• Due to its use of in-memory resources, it is more efficient than other systems when optimized correctly.
• It tends to best suit the needs of applications requiring real-time data.

4. Apache Hive

The Apache Hive is a powerful data warehouse and query language designed for use with Hadoop. It enables those familiar with SQL to access and analyze the data stored in their Hadoop cluster quickly and easily using an easy-to-use interface that doesn’t require extensive coding experience or knowledge of complex programming languages. 

It provides users without strong development skills the chance to take full advantage of big data processing on a distributed computing platform. Hive uses a language called HiveQL (HQL). The three main functions of Hive include data summarization, query, and analysis. The main parts of Hive include:

• Query Compiler: This compiles HiveQL into a Directed Acyclic Graph (DAG).
• Metastore: It stores the metadata.
• Hive Server: This provides a thrift interface and JDBC/ODBC server.
• Driver: This manages the lifecycle of a HiveQL statement.

5. Apache PIG 

Apache Pig makes it easy to analyze large datasets with Hadoop through its programming language called “Pig Latin”. It simplifies the process of writing complex transformations and analytical jobs by automatically translating your script into MapReduce tasks that are executed on a distributed system without needing long lines of code. Some of the features of Apache Pig include: 

• Pig allows users to create their own functions for special-purpose processing. 
• The system optimizes execution automatically, allowing the user to focus on semantics rather than efficiency. 
• It can analyze both structured and unstructured data types equally well.

6. Apache HBase

HBase, an Apache-distributed and column-oriented database built on top of Hadoop, allows for large datasets to be accessed with random read/write abilities in real-time. This makes it a great choice for applications like operational data warehouses that need prompt access to information or analytics programs requiring up-to-date statistics. HBase has two main components: the HBase Master and RegionServer.

i. HBase Master 

HBase Master manages the RegionServers responsible for hosting and managing data, but it does not store any of the actual data itself. It works to ensure an even distribution of workload between all servers.

• HBase Master is responsible for maintaining and monitoring the Hadoop cluster. 
• It also provides an interface for creating, updating, and deleting tables, allowing users to perform administrative tasks. 
• It controls failover handling in case of a node failure.  
• HMaster handles data definition language (DDL) operations. 

ii. RegionServer

RegionServer is a worker node in charge of receiving, processing and responding to client requests related to read, write, update, or delete. It runs on every HDFS DataNode of the Hadoop cluster.

7. Drill

Apache Drill, a distributed SQL query engine on Hadoop, allows for ad hoc queries and analysis of multiple data sources like the HDFS (Hadoop Distributed File System), NoSQL databases, and storage systems. It has an easy-to-use SQL interface which eliminates any need to preprocess data and makes it easier for users to access large amounts of information.

• Drill provides an extensible architecture at all layers. This includes the query layer, query optimization, and client API. It enables organizations to customize any layer for their specific needs.
• It offers a hierarchical columnar data model that can represent highly complex and dynamic data while allowing efficient processing. 
• Apache drill does not need schema or type details about the raw material before it starts executing queries. Record batches are used as basic units of analysis where the schema is dynamically discovered. 
• Drill does not require the same type of centralized metadata that other SQL on Hadoop technologies do. Users don’t have to create and manage tables in a centralized system before they can query data.

8. Sqoop

Sqoop is a component of the Hadoop ecosystem that simplifies data transfers between Hadoop and external databases. It allows users to move structured information from third-party systems, such as MySQL, Oracle, and SQL Server, into the distributed storage system of Hadoop for additional analysis or processing. By bridging this gap between classic databases and modern big data technology like Apache’s offering, Sqoop facilitates efficient integration workflows.

The client machine collects code and sends it to Sqoop, which is then passed on to the Task Manager. The Task Manager connects with an enterprise data warehouse, document-based systems, or relational database management system (RDBMS), allowing for tasks from those sources to be transferred into Hadoop.

9. Zookeeper

Zookeeper is a tool for managing and coordinating distributed systems within the Hadoop ecosystem. It offers clustered coordination services based on synchronization primitives as well as an organized hierarchical namespace to store configuration data. 

Zookeeper ensures dependability in complex application networks by providing consistency and reliability through efficient synchronization techniques. Therefore, making it easier to develop strong applications. The features of Zookeeper include: 

• Zookeeper is fast and well suited to workloads where reads of data are more frequent than writes, with an ideal read/write ratio of 10:1. 
• Additionally, it maintains a record of all transactions for better reliability and auditing capabilities.

10. Oozie

Oozie is a workflow scheduler and coordinator that streamlines the management and scheduling of data processing jobs in Hadoop. By providing users with an intuitive way to arrange complex workflows involving multiple tasks, Oozie makes these processes more manageable while allowing for triggers or dependencies between them. Oozie can be divided into two categories: workflow and coordinator: 

• Oozie Workflow: The purpose of an Oozie workflow is to store and execute Hadoop tasks, such as MapReduce, Pig, or Hive operations. 
• Oozie Coordinator: An Oozie coordinator runs workflows based on certain predetermined schedules while monitoring data availability simultaneously

11. Mahout

Mahout is a machine-learning library found within the Apache Hadoop framework. It provides scalable algorithms and tools that enable developers to create, implement, and maintain various types of machine learning models like classification, clustering, collaborative filtering, and recommendation systems. Mahout makes it possible to process large datasets to generate intelligent applications or solutions. Mahout has some algorithms that include the following:

• Clustering: It divides items into groups based on similarities so that the items in each group are alike or similar to each other.
• Collaborative Filtering: This looks at your pattern as to what you are doing and suggests items. For example, Amazon will suggest products to you based on your purchase history.
• Classification: It looks at existing groupings and puts items that don’t have a category yet into the most appropriate one.  
• Frequent Pattern Mining: It looks at items within a group (like the products in an online shopping cart or search terms someone uses) and discovers what is commonly seen alongside.

12. MLlib

MLlib is an Apache Spark-based library that provides a wide range of machine-learning algorithms and tools to build models. It utilizes the distributed processing power of Spark, allowing for faster and more scalable processing on vast datasets. MLlib can be used to develop classification, regression, clustering, and recommendation systems to better understand data across various industries such as finance or healthcare.

13. Flume

Flume is a distributed, reliable, and scalable system for collecting large amounts of log data from multiple sources and sending it to Hadoop for storage and analysis. It provides an adaptable framework that quickly obtains information from web servers, social media networks, and sensors before delivering the influx of data to be studied in Hadoop. Let’s take a look at the diagram below to get an understanding of how Flume is structured. The flume agent has three components: a Source, a Channel, and a Sink:

• The Source accepts data from an incoming streamline and stores it in the Channel.
• The Channel acts as local storage or primary storage between where the data is initially received to its permanent home on HDFS (Hadoop Distributed File System).
• Finally, the Sink collects this stored information from the Channel and then commits/writes it within HDFS permanently.

14. Ambari

Ambari is a user-friendly platform for managing and monitoring the Hadoop ecosystem. It offers an easy-to-use web interface that enables users to conveniently install, configure, and monitor their Hadoop clusters. This makes it much simpler for people to get up and running with their system as well as keep track of its performance over time. The following are the features of Ambari:

• Ambari simplifies the installation, configuration, and management of clusters at scale. 
• It facilitates a centralized security setup to reduce the complexity of administering and configuring cluster security settings.
• It is highly extensible and customizable, allowing for custom services under management. 
• Moreover, it provides full visibility into cluster health with a comprehensive approach to monitoring solutions.

15. Solr

Solr is an open-source search platform with tight integration into the Hadoop ecosystem that provides full text, faceted, and indexing capabilities. Companies involved in e-commerce, content management systems as well and data analysis rely on Solr to build up their customized search applications for powerful searching abilities.

16. Lucene 

Lucene is the foundation of Solr and provides high-speed, full-text search capabilities. It allows for indexing as well as searching through text documents. Well known for its speediness in retrieving meaningful data from a large volume of textual information, Lucene is commonly used by many search engines.

Conclusion

By leveraging the components of the Hadoop ecosystem, businesses can gain access to specialized capabilities, especially those that aid in data processing, analytics, workflow management, and integration. With these tools at their disposal, they are better equipped to maximize big data potential which guides corporate decision-making processes. Having an understanding of how each component functions in Hadoop is crucial for successful analysis and implementation.