Difference between Hadoop and Data warehouse

This article will provide you a clear comparison between Hadoop and Data warehouse. Before moving on to that first, let us understand what are Hadoop and data warehouse.

What is Hadoop?

Hadoop is an open-source system for managing massive datasets in a distributed computing setting. It offers a reliable and scalable platform that enables the distributed processing of massive data across numerous machines or clusters.

Hadoop has 2 main components which are explained below

1. HDFS (Hadoop Distributed File System): HDFS is a distributed file system that stores data across numerous computers in a cluster. For effective storage and retrieval, it divides big files into segments and distributes them throughout the cluster. Data is duplicated across several nodes, assuring data availability even if one or more nodes fail, enabling rapid processing and fault tolerance.
2. MapReduce: MapReduce is a programming model and processing framework used to analyze and process large datasets in parallel. It divides a computation task into smaller sub-tasks that can be executed on different nodes of the Hadoop cluster. The "Map" phase processes data in parallel across the nodes, and the "Reduce" phase combines the results to produce the final output. MapReduce allows for the efficient processing of large-scale data by utilizing the computational power of multiple machines in parallel.

In addition to its fundamental components, Hadoop features a robust ecosystem of tools and technologies that expand its capabilities. The data warehousing and SQL-like query language Apache Hive, the high-level scripting language Apache Pig, the quick data processing engine Apache Spark, the distributed NoSQL database Apache HBase, and numerous additional tools are among them. Within the Hadoop ecosystem, these products offer extra capabilities for data storage, processing, querying, and analysis.

Hadoop is extensively utilized in a variety of fields and applications, including data analytics, machine learning, log processing, and others, where managing massive amounts of data is essential. It is an excellent tool for handling and analyzing massive data because of its distributed nature, fault tolerance, and scalability.

What is a Data warehouse?

A data warehouse is a central location where a company can store vast amounts of data gathered from numerous sources. It is intended to help business intelligence (BI) activities by enabling users to examine the data and come to wise conclusions.

A data warehouse's main objective is to offer a unified picture of data from many systems and databases. Data is organized, cleaned up, and turned into a format that is best for reporting and analysis in this unified and structured storage area. The extract, transform, and load (ETL) procedure is used for achieving this.

Data warehouses are often designed using a combination of hardware, software, and database systems capable of handling huge amounts of data and complicated queries. They use strategies including indexing, partitioning, and data compression to boost storage effectiveness and performance.

The capability of a data warehouse to allow the storing of historical data is one of its important features. It collects and saves information over time, allowing users to analyse trends, measure performance, and compare historical patterns. Making decisions and developing a strategy are made much easier with this.

Features of Data warehouses

1. Subject-Oriented: Data warehouses are arranged according to particular business-related topics or domains, such sales, clients, goods, or finances. Each subject area is represented by a unique data mart or collection of tables in the data warehouse.
2. Non-volatile: Usually, data is not changed or updated frequently once it has been stored into a data warehouse. The read-only status of the data ensures that it can continue to offer accurate historical data for analysis.
3. Integrated Data: Data warehouses combine information from several systems, including transactional databases, spreadsheets, and external ones. In order to create a uniform view, it makes sure that data from various systems and departments is standardised and integrated.
4. Optimized for Analytics: Data warehouses are built to support complex analytical queries and reporting. They use methods like indexing, segmentation, and aggregation to provide effective retrieval and analysis of massive amounts of data.

Difference between Hadoop and Data warehouse