Hadoop is an open-source framework used for big data processes. It’s humongous and has many components. Each one of those components performs a specific set of big data jobs. Hadoop’s vast collection of solutions has made it an industry staple. And if you want to become a big data expert, you must get familiar with all of its components.
Don’t worry, however, because, in this article, we’ll take a look at all those components:
Introduction to the Hadoop Ecosystem
The Hadoop Ecosystem refers to a collection of open-source software tools and frameworks that work together to facilitate large-scale datasets storage, processing, and analysis. It offers a robust and scalable solution for handling big data challenges. The ecosystem comprises various components that address different aspects of data management and analysis.
What are the Hadoop Core Components?
Hadoop core components govern its performance and are you must learn about them before using other sections of its ecosystem. Hadoop’s ecosystem is vast and is filled with many tools. Another name for its core components is modules. There are primarily the following
Hadoop core components:
The full form of HDFS is the Hadoop Distributed File System. It’s the most critical component of Hadoop as it pertains to data storage. HDFS lets you store data in a network of distributed storage devices. It has its set of tools that let you read this stored data and analyze it accordingly. HDFS enables you to perform acquisitions of your data irrespective of your computers’ operating system. Read more about HDFS and it’s architecture.
As you don’t need to worry about the operating system, you can work with higher productivity because you wouldn’t have to modify your system every time you encounter a new operating system. HDFS is made up of the following components:
- Secondary NameNode
Name Node is also called ‘Master’ in HDFS. It stores the metadata of the slave nodes to keep track of data storage. It tells you what’s stored where. The master node also monitors the health of the slave nodes. It can assign tasks to data nodes, as well. Data nodes store the data. Data nodes are also called ‘Slave’ in HDFS.
Slave nodes respond to the master node’s request for health status and inform it of their situation. In case a slave node doesn’t respond to the health status request of the master node, the master node will report it dead and assign its task to another data node.
Apart from the name node and the slave nodes, there’s a third one, Secondary Name Node. It is a buffer to the master node. It updates the data to the FinalFS image when the master node isn’t active.
MapReduce is the second core component of Hadoop, and it can perform two tasks, Map and Reduce. Mapreduce is one of the top Hadoop tools that can make your big data journey easy. Mapping refers to reading the data present in a database and transferring it to a more accessible and functional format. Mapping enables the system to use the data for analysis by changing its form. Then comes Reduction, which is a mathematical function. It reduces the mapped data to a set of defined data for better analysis.
It pars the key and value pairs and reduces them to tuples for functionality. MapReduce helps with many tasks in Hadoop, such as sorting the data and filtering of the data. Its two components work together and assist in the preparation of data. MapReduce also handles the monitoring and scheduling of jobs.
It acts as the Computer node of the Hadoop ecosystem. Mainly, MapReduce takes care of breaking down a big data task into a group of small tasks. You can run MapReduce jobs efficiently as you can use a variety of programming languages with it. It allows you to use Python, C++, and even Java for writing its applications. It is fast and scalable, which is why it’s a vital component of the Hadoop ecosystem.
Working of MapReduce
MapReduce is a programming model and processing framework in Hadoop that enables distributed processing of large datasets. It consists of two main phases: the Map phase, where data is divided into key-value pairs, and the Reduce phase, where the results are aggregated. MapReduce efficiently handles parallel processing and fault tolerance, making it suitable for big data analysis.
YARN stands for Yet Another Resource Negotiator. It handles resource management in Hadoop. Resource management is also a crucial task. That’s why YARN is one of the essential Hadoop components. It monitors and manages the workloads in Hadoop. YARN is highly scalable and agile. It offers you advanced solutions for cluster utilization, which is another significant advantage. Learn more about Hadoop YARN architecture.
YARN is made up of multiple components; the most important one among them is the Resource Manager. The resource manager provides flexible and generic frameworks to handle the resources in a Hadoop Cluster. Another name for the resource manager is Master. The node manager is another vital component in YARN.
It monitors the status of the app manager and the container in YARN. All data processing takes place in the container, and the app manager manages this process if the container requires more resources to perform its data processing tasks, the app manager requests for the same from the resource manager.
Apache has added many libraries and utilities in the Hadoop ecosystem you can use with its various modules. Hadoop Common enables a computer to join the Hadoop network without facing any problems of operating system compatibility or hardware. This component uses Java tools to let the platform store its data within the required system.
It gets the name Hadoop Common because it provides the system with standard functionality.
Hadoop Components According to Role
Now that we’ve taken a look at Hadoop core components, let’s start discussing its other parts. As we mentioned earlier, Hadoop has a vast collection of tools, so we’ve divided them according to their roles in the Hadoop ecosystem. Let’s get started:
Storage of Data
Zookeeper helps you manage the naming conventions, configuration, synchronization, and other pieces of information of the Hadoop clusters. It is the open-source centralized server of the ecosystem.
HCatalog stores data in the Binary format and handles Table Management in Hadoop. It enables users to use the data stored in the HIVE so they can use data processing tools for their tasks. It allows you to perform authentication based on Kerberos, and it helps in translating and interpreting the data.
We’ve already discussed HDFS. HDFS stands for Hadoop Distributed File System and handles data storage in Hadoop. It supports horizontal and vertical scalability. It is fault-tolerant and has a replication factor that keeps copies of data in case you lose any of it due to some error.
You’d use Spark for micro-batch processing in Hadoop. It can perform ETL and real-time data streaming. It is highly agile as it can support 80 high-level operators. It’s a cluster computing framework. Learn more about Apache spark applications.
This language-independent module lets you transform complex data into usable data for analysis. It performs mapping and reducing the data so you can perform a variety of operations on it, including sorting and filtering of the same. It allows you to perform data local processing as well.
Tez enables you to perform multiple MapReduce tasks at the same time. It is a data processing framework that helps you perform data processing and batch processing. It can plan reconfiguration and can help you make effective decisions regarding data flow. It’s perfect for resource management.
You’d use Impala in Hadoop clusters. It can join itself with Hive’s meta store and share the required information with it. It is easy to learn the SQL interface and can query big data without much effort.
The developer of this Hadoop component is Facebook. It uses HiveQL, which is quite similar to SQL and lets you perform data analysis, summarization, querying. Through indexing, Hive makes the task of data querying faster.
HBase uses HDFS for storing data. It’s a column focused database. It allows NoSQL databases to create huge tables that could have hundreds of thousands (or even millions) of columns and rows. You should use HBase if you need a read or write access to datasets. Facebook uses HBase to run its message platform.
Solr and Lucene: Search and Indexing Capabilities
Strong search and indexing tools like Apache Solr and Lucene are fully compatible with the Hadoop Ecosystem. Full-text search, faceted search, and rich document indexing are all possible with the aid of the search platform Solr. A Java package called Lucene offers more basic search functionality. The search and retrieval capabilities of Hadoop applications are improved by combining Solr and Lucene.
Features of Solr and Lucene:
Full-Text Search: Solr and Lucene provide powerful full-text search capabilities, allowing users to perform complex search queries on large volumes of text data.
Scalability: Solr and Lucene can handle massive amounts of indexed data, making them suitable for enterprise-level search applications.
Rich Document Indexing: Solr supports various document formats, including PDF, Word, and HTML, allowing users to index and search within documents.
Faceted Search: Solr enables faceted search, allowing users to refine search results based on different categories or attributes.
Oozie: Workflow Scheduler for Hadoop
Oozie is a framework for Hadoop that manages workflow coordination and job scheduling. It enables users to build and control intricate workflows made up of several Hadoop tasks. Oozie allows extensibility through custom actions and supports a range of workflow control nodes. Users can oversee and automate the performance of data processing activities in a Hadoop cluster using Oozie.
Features of Oozie:
Workflow Orchestration: Oozie enables the definition and coordination of complex workflows with dependencies between multiple Hadoop jobs.
Scheduling Capabilities: Oozie supports time-based and event-based triggers, allowing users to schedule and automate data processing tasks.
Extensibility: Oozie allows the inclusion of custom actions, enabling the integration of external systems and tools into the workflow.
Monitoring and Logging: Oozie provides monitoring and logging capabilities, allowing users to track the progress of workflows and diagnose issues.
HCatalog: Metadata Management for Hadoop
A central metadata repository is offered by the Hadoop table and storage management layer known as HCatalog. It makes data exchange easier between various Hadoop components and outside systems. With the help of HCatalog’s support for schema evolution, users can change the structure of stored data without affecting data access. It offers a uniform view of the data, which facilitates the management and analysis of datasets throughout the Hadoop Ecosystem.
Features of HCatalog:
Metadata Management: HCatalog stores and manages metadata, including table definitions, partitions, and schemas, allowing easy data discovery and integration.
Schema Evolution: HCatalog supports schema evolution, allowing users to modify the data structure without impacting existing data or applications.
Data Sharing: HCatalog facilitates sharing between different Hadoop components, enabling seamless data exchange and analysis.
Integration: HCatalog integrates with external systems and tools, allowing data to be accessed and processed by non-Hadoop applications.
Avro and Thrift: Data Serialization Formats
Frameworks for data serialisation used in the Hadoop Ecosystem include Apache Avro and Thrift. They offer effective and language-neutral data serialization, simplifying data transfer across various platforms. Schema evolution is supported by Avro and Thrift, enabling schema evolution without compromising backward compatibility. Within the Hadoop Ecosystem, they are commonly utilized for data storage and exchange.
Features of Avro and Thrift:
Schema Evolution: Avro and Thrift support schema evolution, allowing for modifying data schemas without breaking compatibility with existing data.
Language-Independent: Avro and Thrift provide language bindings for various programming languages, enabling data interchange between systems written in different languages.
Compact Binary Format: Avro and Thrift use compact binary formats for efficient serialization and deserialization of data, reducing network overhead.Dynamic Typing: Avro and Thrift support dynamic typing, allowing flexibility in handling data with varying structures.
Apache Drill lets you combine multiple data sets. It can support a variety of NoSQL databases, which is why it’s quite useful. It has high scalability, and it can easily help multitudes of users. It lets you perform all SQL-like analytics tasks with ease. It also has authentication solutions for maintaining end-to-end security within your system.
You can use Apache Sqoop to import data from external sources into Hadoop’s data storage, such as HDFS or HBase. You can use it to export data from Hadoop’s data storage to external data stores as well. Sqoop’s ability to transfer data parallelly reduces excessive loads on the resources and lets you import or export the data with high efficiency. You can use Sqoop for copying data as well.
Developed by Yahoo, Apache pig helps you with the analysis of large data sets. It uses its language, Pig Latin, for performing the required tasks smoothly and efficiently. You can parallelize the structure of Pig programs if you need to handle humongous data sets, which makes Pig an outstanding solution for data analysis. Utilize our apache pig tutorial to understand more.
Flume lets you collect vast quantities of data. It’s a data collection solution that sends the collected data to HDFS. It has three sections, which are channels, sources, and finally, sinks. Flume has agents who run the dataflow. The data present in this flow is called events. Twitter uses Flume for the streaming of its tweets.
Apache Kafka is a durable, fast, and scalable solution for distributed public messaging. LinkedIn is behind the development of this powerful tool. It maintains large feeds of messages within a topic. Many enterprises use Kafka for data streaming. MailChimp, Airbnb, Spotify, and FourSquare are some of the prominent users of this powerful tool.
Learn more – Hadoop Components
In this guide, we’ve tried to touch every Hadoop component briefly to make you familiar with it thoroughly. If you want to find out more about Hadoop components and its architecture, then we suggest heading onto our blog, which is full of useful data science articles.
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