Hadoop
Hadoop in Data Engineering: A Comprehensive Guide
Data engineering is the process of designing, building and maintaining data pipelines that collect, transform and deliver data for various purposes such as analytics, machine learning and business intelligence. Data engineering requires skills in programming, databases, distributed systems and cloud computing.
One of the most popular and widely used technologies for data engineering is Hadoop. Hadoop is an open-source software framework that enables distributed storage and processing of large-scale data sets across clusters of commodity hardware. Hadoop has become synonymous with big data because it can handle any kind of data, whether structured, unstructured or semi-structured, at a very high speed and low cost.
In this blog post, we will explore what Hadoop is, how it works, what are its main components and why it matters for data engineering.
What is Hadoop?
Hadoop was created by Doug Cutting and Mike Cafarella in 2005 as a project to support the development of Nutch, an open-source web crawler. The name Hadoop comes from Cutting's son's toy elephant. The project was inspired by two papers published by Google: one on the Google File System (GFS) and another on MapReduce , a programming model for parallel processing of large data sets.
Hadoop consists of two core components: Hadoop Distributed File System (HDFS) and MapReduce. HDFS is a distributed file system that stores data across multiple nodes in a cluster. It provides high availability, fault tolerance and scalability by replicating data blocks on different nodes. MapReduce is a programming model that allows users to write applications that process large amounts of data in parallel on multiple nodes using key-value pairs.
HDFS and MapReduce are complemented by several other components that form the Hadoop ecosystem. These include:
YARN (Yet Another Resource Negotiator): A resource management layer that allocates CPU, memory and disk resources to different applications running on the cluster.
Hive: A data warehouse system that provides SQL-like query language (HiveQL) for analyzing structured and semi-structured data stored in HDFS.
Pig: A scripting language (Pig Latin) for performing complex data transformations and analysis on HDFS using MapReduce.
Spark: A fast and general-purpose engine for large-scale data processing that supports batch, streaming, interactive and machine learning applications. Spark can run on top of Hadoop or independently.
HBase: A column-oriented database that provides random access to large-scale structured or semi-structured data stored in HDFS.
Sqoop: A tool for transferring bulk data between HDFS and relational databases such as MySQL or Oracle.
Flume: A tool for collecting, aggregating and moving large amounts of log or event data from various sources to HDFS.
Kafka: A distributed messaging system that enables high-throughput ingestion of streaming data from various sources to HDFS or other destinations.
Oozie: A workflow scheduler that coordinates the execution of multiple jobs or tasks based on dependencies and triggers.
How does Hadoop work?
The basic workflow of using Hadoop for data engineering can be summarized as follows:
Data ingestion: The first step is to collect raw data from various sources such as web logs, social media posts, sensor readings etc., using tools like Flume or Kafka. The ingested data can be stored directly into HDFS or processed by other tools like Spark Streaming before storing into HDFS.
Data storage: The second step is to store the ingested or processed data into appropriate formats such as text files, JSON files, Parquet files, ORC files etc., depending on the type , structure , size , compression ratio , schema evolution , query performance etc., of the desired output . The stored files are divided into blocks (typically 128 MB each) , which are replicated across multiple nodes (typically three times) , ensuring high availability , fault tolerance , scalability , load balancing etc.,
Data processing: The third step is to process the stored data using various tools depending on the use case and requirements such as batch processing, streaming processing, interactive analysis, machine learning etc., For example, one can use MapReduce, Spark, Pig, or Hive to perform ETL (extract-transform
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