Data Warehouse
Discover the benefit of an Enterprise Data Warehouse
Data Warehouse
Data is constantly available for analyzing and planning purposes
Data Warehouse
Get full access to all the data in your organization without compromising the security or integrity

Data Warehouse

Discover how to create operational efficiency

Analyse plan and act

Analyze data from sales, finance, inventory and other software in a consolidated and aggregated view. 

Data Warehouses are capable of tracking and modifying marketing campaigns, for faster, more accurate evaluation of campaign effectiveness.

Data mining tools can help you find hidden patterns using automatic methodologies against data stored in your warehouse.

Data warehouses are optimized for read access, resulting in faster report generation compared to running reports against the source transaction system.

Share and collaborate

You can use business analytics tools to deliver insights throughout your organization.

Quickly go from data to insight to action. Connect to hundreds of sources: on-premises data sources, big data, streaming data, and cloud services, prepare your data with ease and create beautiful reports.

View dashboard and scorecards on the web or on your phone, get alerts when data changes, and drill into details.

Your datais at your fingertips.

Collaborate with coworkers, share dashboards and reports, with annotations and publish your results to the web, pdf, etc.


Data security

All historical data from multiple sources can be stored and accessed from a data warehouse as the single source of truth.

You can improve data quality by cleaning up data as it is imported into the data warehouse, providing more accurate data as well as providing consistent codes and descriptions.

Data warehouses make it easier to provide secure access to authorized users while restricting access to others. There is no need to grant business users access to the source data, thereby removing a potential attack vector against one or more production transaction systems.

Data warehouses make it easier to create business intelligence solutions on top of the data, such as OLAP cubes.

Data Warehouse Architecture

azure sql data warehouse transparent

The standards provide guidance and tools for companies who want to ensure that their products and services consistently meet customer’s requirements, and that quality is consistently improved.

Choose a data warehouse when you need to turn massive amounts of data from operational systems into a format that is easy to understand, current, and accurate.

Consider using a data warehouse when you need to keep historical data separate from the source transaction systems for performance reasons. Data warehouses make it easy to access historical data from multiple locations, by providing a centralized location using common formats, common keys, common data models, and common access methods.

Business intelligence solutions are built on top of the EDW by creating Online Analytical Processing (OLAP) cubes.

OLAP is especially useful for applying aggregate calculations over large amounts of data. OLAP systems are optimized for read-heavy scenarios, such as analytics and business intelligence. OLAP allows users to segment multi-dimensional data into slices that can be viewed in two dimensions (such as a pivot table) or filter the data by specific values. This process is sometimes called “slicing and dicing” the data, and can be done regardless of whether the data is partitioned across several data sources. This helps users to find trends, spot patterns, and explore the data without having to know the details of the traditional data analysis.

Semantic models can help business users abstract relationship complexities and make it easier to analyze data quickly. A semantic data model is a conceptual model that describes the meaning of the data elements it contains. Semantic modeling provides a level of abstraction over the data warehouse database schema, so that users don’t need to know the underlying data structures. 

Business Intelligence and analytics solutions coupled with advanced reporting capabilities are key to succeed in getting ISO certification.

Online analytical processing (olap)


The tabular semantic model uses relational modeling constructs (model, tables, columns). Internally, metadata is inherited from OLAP modeling constructs (cubes, dimensions, measures). Code and script use OLAP metadata.

Tabular offers a relational modeling approach that many developers find more intuitive.

For new projects, we generally recommend tabular models. Tabular models are faster to design, test, and deploy; and will work better with the latest self-service BI applications and cloud services from Microsoft.

Tabular model databases can use row-level security, using role-based permissions.


The multidimensional semantic model uses traditional OLAP modeling constructs (cubes, dimensions, measures).

Multidimensional is a mature and scalable technology built on open standards, embraced by numerous vendors of BI software, but can be hard to master.

Multidimensional model databases can use dimension and cell-level security, using role-based permissions.

Please note that no major innovation is to be expected in this product in the future from Microsoft.

Multidimensional cubes are higher complexity than tabular models.