Introduction to Hadoop for Data Engineers
Hadoop is a distributed system for storing and processing large datasets. It is an open-source Apache project that can handle data of different types, sizes, and formats. Hadoop has become an essential tool for data engineers working with big data. In this blog post, we will introduce the fundamental concepts of Hadoop, its different components, and usage.
Hadoop Architecture
Hadoop architecture consists of two main components - Hadoop Distributed File System (HDFS) and Yet Another Resource Negotiator (YARN).
Hadoop Distributed File System
HDFS is the primary storage system of Hadoop. It stores data in a distributed manner by replicating it across multiple servers. HDFS follows the "write once, read many times" approach, where the data is written to HDFS once and then read by multiple users multiple times. HDFS offers high availability as it replicates data across servers, which means the data can be accessed even if one server fails.
Yet Another Resource Negotiator
YARN is Hadoop's processing layer. It manages and allocates resources to various applications running on a Hadoop cluster. YARN enables Hadoop to support multiple programming models such as batch processing, streaming data processing, and interactive SQL queries. YARN has two main components - Resource Manager and Node Manager.
Resource Manager
The Resource Manager manages the resources of the entire Hadoop cluster. It receives requests from clients to execute jobs and allocates resources to them. The Resource Manager also keeps an eye on the overall health of the cluster.
Node Manager
The Node Manager runs on each node of the Hadoop cluster and manages resources available on that node. It communicates with the Resource Manager to allocate and release resources as per the requirements of the job.
Hadoop Components
Apart from HDFS and YARN, Hadoop has other components that enable and enhance its functionality.
MapReduce
MapReduce is a programming model for processing and generating large datasets. It is a batch processing model that breaks down a large data set into smaller chunks and processes them in a parallel manner. The MapReduce model consists of two phases - Map and Reduce.
The Map phase performs filtering and sorting of the input data and produces intermediate key-value pairs. The Reduce phase takes the output of the Map phase and produces the final output by aggregating the data.
Hive
Hive is a data warehousing tool that provides SQL-like querying capabilities on top of Hadoop. It allows users to perform data analysis and ad-hoc querying on large datasets. Hive translates SQL-like queries into MapReduce jobs that Hadoop can execute.
Pig
Pig is a high-level platform for creating programs that run on Hadoop. It provides a simple and powerful scripting language called Pig Latin for writing Hadoop jobs. Pig Latin is optimized for data processing and can handle complex data operations with ease.
HBase
HBase is a non-relational, distributed database that stores data in a tabular format. It is designed to handle large datasets with high velocity. HBase is ideal for applications that require real-time read/write access to data.
Usage
Hadoop is widely used in various industries such as finance, healthcare, energy, and retail. It is used for various use cases such as fraud detection, customer segmentation, sentiment analysis, and log analysis.
Conclusion
Hadoop is an essential tool for storing and processing large datasets. It offers high scalability, fault-tolerance, and cost-effectiveness. In this blog post, we introduced the fundamental concepts of Hadoop, its different components, and usage. With this knowledge, data engineers can leverage Hadoop to build robust and scalable data pipelines.
Category: Distributed System