📚 What is a Data Warehouse?
A Data Warehouse (DW) is the process of collecting and managing data from various sources to provide meaningful insights about a business. A Data Warehouse is typically used to connect and analyze heterogeneous business data sources. It is a critical component of a Business Intelligence (BI) system, designed for data analysis and reporting.
🗄️ Data Storage
It involves a mix of technologies and components that help strategically utilize data. Instead of transactional processing, it’s the automated process of collecting a vast amount of information from a company, structured for retrieval and review, to transform data into information and make it available for users to create a difference in a timely manner.
🏢 Types of Data Warehouses
- Enterprise Data Warehouse (EDW): Positioned to meet the enterprise’s overall information needs, fed from multiple sources within the enterprise IT system. It includes different layers like ODS and Data Marts, providing flexibility to return to Data Marts or ODS layers as needed.
- Data Mart: Smaller in scope and focus (e.g., dedicated to Marketing), Data Marts are a subset of a DW, fed from one or a few applications. Data Marts have a specific functional and scope-oriented structure.
- Operational Data Store (ODS): Directly fed from operational data, ODS is known for holding more current information compared to traditional DWs. It can sometimes be updated multiple times a day and serves the purpose of reducing the load on operational data when queried. It’s sometimes used as an initial stage before the DW.
💡 How Does a Data Warehouse Work?
A Data Warehouse is used to consolidate integrated data from multiple heterogeneous sources to provide greater visibility into a company’s performance. A data center is designed to run searches and analyses on historical data derived from transactions.
Once integrated, the data remains unchanged. Stored data should be secure, accurate, easily accessible, and manageable.
To build a data warehouse, the first step is data extraction, where a large amount of data is collected from various source points. After processing the data, the next step is data cleansing, which involves scanning for errors and correcting or excluding them.
Cleaned data is then transformed from one form to another for the computer. While transforming, data goes through processing, merging, aggregation, and other operations to make it more organized and user-friendly. Over time, as multiple data points change, additional data is added to the data warehouse.
🌟 Reasons to Invest in a Data Warehouse
• Centralize all data in one place. • Obtain detailed industry insights and gain a comprehensive understanding of the global Data Warehouse sector and business environment. • Evaluate approaches to minimize production processes, significant issues, and production losses. • In addition to standard framework work, it also provides tailored analyses based on specific requirements to assess future perspectives and opportunities for the data warehouse.
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