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What is a Data Warehouse? Definition, Benefits, Architecture Explained

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What is a Data Warehouse? Definition, Benefits, Architecture Explained

What is Data Warehouse?

A data warehouse is essential for businesses that wish to drive engagement and make critical data-driven decisions. This blog presents all the basic concepts of data warehouse, from what a data warehouse is to how many components it has. 

This data warehouse is then combined with a business intelligence tool, allowing users to uncover trends and implement sophisticated analyses. Your data warehouse makes sure the information can simply be queried by your selection of Business Intelligence (BI) solutions. Decision makers access and analyze the information from a data warehouse to extract valuable business insights. 

To explain the data warehouse meaning, you just need to picture the data warehouse as a repository where all the data from various sources and systems comes in. The data can be structured, semi-structured and unstructured, but once it flows into the data warehouse, it will be transformed and processed ready for Business Intelligence and analytics tools. As time goes by, historical data is built up within the data warehouse, making it a significant “single source of truth” for your business. Besides its support for BI and analytics tools, data warehouse is also utterly important need for data mining which looks for patterns in your data.  Some of you may have heard of databases, data lakes or data marts. Those are the terms that can be confusing or cause misunderstanding. Don’t worry, we’re here to clarify them. 

Confusing Data Warehouse Terms Explained

1. Data warehouse vs Database

A data warehouse is different from a database. While a database’s sole purpose is to store data from a sole source (such as transactional data), data warehouse processes data from multiple sources to not only store but also read big data to uncover the data relationships or trends.   ETL (Extract, Transform, Load) tools gather information from various locations, covering databases and other sources like websites and files, and put it in a single centralized data warehouse. The ETL process can occur automatically or manually under scheduled pre-defined conditions. When data is in the warehouse, it can then be configured and manipulated.  

2. Data warehouse vs Data lake

A data lake centralizes all data including both unstructured and structured data while it’s a requirement for data to be in table format to enter a data warehouse. Since data warehouse needs to query data using SQL, that’s why tabular format is required. 

Therefore, data lake only requires raw data, but data warehouse needs curated data. Users of data lake are business analysts, data scientists, data developers, data engineers and data architects, while users of data warehouse are business analysts, data scientists, data developers.

3. Data warehouse vs Data mart

Data mart is a part of data warehouse, so it also stores curated data, but it is designed to serve a specific team such as marketing, sales or finance teams. To be specific, data warehouse collects large amount of data from many sources (around 100’s of gigabytes to petabytes), while data mart includes a sole source or a portion of data in the data warehouse (can be only 10’s of gigabytes). The data from data warehouse is complete and detailed for the whole organization to use while there might only be summarized data in data mart for a single department to use. 

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