Enterprise data warehousing
An enterprise data warehouse (EDW) centralizes different types of an organization’s data from various sources, storing it in a cleansed, standardized, and consistent format, breaking down data silos, and making corporate information accessible for further querying, analysis, and reporting.
Eight components of an enterprise data warehouse
An enterprise data warehouse is more than a repository connected to data sources (CRM, IoT devices, SaaS apps, etc.) on one end and to BI or analytics software on the other. It is a comprehensive data processing and storage environment that consists of the following key components:
ETL/ELT
Extract, transform, load (ETL) or extract, load, and transform (ELT) tools ingest information from the source systems and process it until it’s suitable for permanent storage. Since companies typically have numerous data sources with different data types, models, and information generation speeds, ETL/ELT is one of the core elements for enterprise-grade analytics.
Staging area
A staging area is a temporary raw data repository between data sources and its permanent storage that hosts the data during the transformation stage. This element is typical for solutions built with the ETL approach but can be omitted if the transformations are performed in the data warehouse database.
Data warehouse database
Traditionally, an enterprise data warehouse database is a relational database where integrated and subject-oriented business information is loaded into data models for analytical querying. This component also includes a metadata repository where an enterprise stores a map of its data for easy access and handling, as well as a management system to organize and update metadata.
Data marts
Dimensional data marts are built to meet the analytics needs of specific user groups and decision-makers from sales and marketing, production, supply chain management, finance, and other departments. Data marts facilitate easier and quicker data access and analysis as they handle smaller datasets.
OLAP cubes
Deploying multidimensional online analytical processing (OLAP) cubes that store data in the pre-aggregated form helps overcome the limitation of relational databases and streamline data analysis. The data in OLAP cubes can be sliced and diced, drilled down, rolled up, and pivoted to handle various analytics requests of business users.
Data governance
The data governance component defines processes and policies for managing data quality and security, data modeling, metadata, data retention and backup, data usage, and user activity.
Analytics & query layer
The analytics and query layer represents a user-friendly frontend to allow authorized users to query, analyze, and visualize data in the warehouse and share reports. These tools include SQL clients, business intelligence (BI) systems, reporting tools, dashboards, and a wide range of data visualization solutions. They make the data accessible and actionable, enabling data analysts and business users to discover strategic insights.
Performance optimization
For data warehouses to deliver fast query performance regardless of the data volume size, they should come with performance optimization capabilities. This entails in-memory processing for more rapid data query execution and analytics, caching to store frequently accessed data and reduce query time, and parallel processing that revolves around utilizing distributed systems to process large datasets.