An enterprise data warehouse (EDW) is a relational data warehouse containing a company’s business data, including information about its customers.

In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is considered a core component of business intelligence. DWs are central repositories of integrated data from one or more disparate sources.

Big Data allows unrefined data from any source, but Data Warehouse allows only processed data, as it has to maintain the reliability and consistency of the data. The unprocessed data in Big Data systems can be of any size depending on the type their formats.

A data warehouse stores current and historical data for the entire business and feeds BI and analytics. Data warehouses use a database server to pull in data from an organization’s databases and have additional functionalities for data modeling, data lifecycle management, data source integration, and more.

Both the above look similar but there is a clear difference. Big data is a repository to hold lots of data but it is not sure what we want to do with it, whereas data warehouse is designed with the clear intention to make informed decisions. Further, a big data can be used for data warehousing purposes.

The future of data warehousing “Despite declarations by pundits, data warehousing is not dead. Recent surveys show that more than 60% of companies are operating between two and five data warehouses today. Fewer than 10% have only one data warehouse or none at all.

Traditional database system deals with structured data. Big data system deals with structured, semi structured and unstructured data.

Also known as enterprise data warehousing, data warehousing is an electronic method of organizing, analyzing, and reporting information. For example, data warehousing makes data mining possible, which assists businesses in looking for data patterns that can lead to higher sales and profits.

Real World Big Data Examples

• Discovering consumer shopping habits.
• Personalized marketing.
• Finding new customer leads.
• Fuel optimization tools for the transportation industry.
• User demand prediction for ridesharing companies.
• Monitoring health conditions through data from wearables.
• Live road mapping for autonomous vehicles.

An EDW enables data analytics, which can inform actionable insights. Like all data warehouses, EDWs collect and aggregate data from multiple sources, acting as a repository for most or all organizational data to facilitate broad access and analysis.

It’s not part of the Enterprise Data Warehouse, but the whole purpose of the EDW is to feed this layer. The lower layers – processing, integration and data – is what we used to call the EDW. These are technology layers that need to store, bring together and process the data needed for analytics.

Data marts deployed in specific departments containing a subset of organizational data. While data warehouse theory suggested either central EDW or individual data marts, in reality many organizations combine models and have both. Online Analytical Processing (OLAP) server that processes complex queries on the data.

It was the central data store that holds historical data for sales, finance, ERP and other business functions, and enables reporting, dashboards and BI analysis. Today, the definition of the EDW is expanding. It is becoming a “stack”, not a monolithic system you build and maintain.

Here is our view of the big data stack. The top layer – analytics – is the most important one. Analysts and data scientists use it. It’s not part of the Enterprise Data Warehouse, but the whole purpose of the EDW is to feed this layer. The lower layers – processing, integration and data – is what we used to call the EDW.