Companies are looking to use their data to make faster decisions and improve internal processes, becoming increasingly data-driven. Sales, spending, and consumer behavior data can be derived from corporate data. It is now expected to use an enterprise data warehouse (EDW) to integrate data from various sources and use it for business intelligence (BI) and analytics.

What Is an Enterprise Data Warehouse?

A centralized data warehouse containing data from various sources is called an EDW. It collects corporate data and provides access to it for business intelligence and data-driven decision-making. The data stored in the EDW is accessible and can be used by users (with appropriate permissions) across the organization.

The EDW contains historical and current data, including real-time data and snapshots of source systems. Because the EDW routinely stores definitive, unaltered business information in one place, it is the most easily accessible "version of the truth" for business data. Real-time analytics is based on archive platforms, also known as EDW.s

Types of EDWs

The two main types of enterprise data warehouses can be divided into on-premises or "traditional" data warehouses and cloud-based data warehouses. Some companies use a third type: virtual data warehouses.

Traditional Or Local Data Warehouses

Local data warehouses are mainly used within a company's firewall. Teradata, Netezza, and Exadata are just a few examples. Local data warehouses give you complete control, but with that power comes much responsibility. Managing a typical data warehouse requires system administrators, network engineers, and database administrators; a complete technical package is needed.

Storing Information in The Cloud

Cloud data warehousing is becoming increasingly expensive for organizations. Cloud data storage needs to be reliable, scalable, and cost-effective. Snowflake, Google BigQuery, and Amazon Redshift are examples of cloud data warehouses. Organizations can use cloud data warehouses to increase data capacity and storage space when needed. In addition, cloud data warehouses can process data without additional staff or technical resources.

Online Data Storage

Data visualization is a third option used by some companies. In this case, a virtual layer is created to analyze data and create reports while the data remains in the original systems. It seems like a quicker and easier way to get started. However, data visualization relies on the source systems to retrieve the data, which causes performance issues when scaling.

Enterprise Data Warehouse Components

There are several tools for building an enterprise-scale data warehouse infrastructure. Let's take a look at the function and purpose of each component.

Data Sources are all sources from which raw data is extracted and stored. This can be simple spreadsheets, files, SQL relational databases, IoT systems, etc.

Load Manager - The load manager extracts information from the data sources and loads it into the data warehouse.

Data Warehouse Manager - The Data Warehouse Manager creates visualizations and indexes, merges data, analyses, and performs other data management tasks.

Query Handler - The query handler is responsible for processing user queries. It ensures the queries are executed and passes them to the appropriate tables in the data warehouse.

End User Tools - The tools used by end users to interact with the enterprise data warehouse are called end-user tools. These include tools for application development, query presentation, data reporting, OLAP, data mining, and ESI.

Data Sets - choices. Sometimes, a data warehouse may contain several smaller pieces, called data maps, explicitly designed for a particular domain, user group, or topic. For example, you might create multiple data warehouses for the marketing and finance departments.

Performance Level - The final element of the EDW is the tools that allow end users to access data. This layer, known as the BI interface, serves as a dashboard for enterprise reporting, data visualization, and individual information extraction for activities such as machine learning.

Summary

To summarize, a data warehouse is significant and essential for any company that wants to manage large amounts of data. When decisions are made based on stored data, it gives a competitive advantage. In today's practical world, it is easier for an organization to collect and communicate all this data using an EDW.

Seamless integration of an enterprise data warehouse is critical to streamline the enterprise software development process.