MSDN Documentation

1. Planning and Design Review

Before any physical deployment begins, a thorough review of the data warehouse design is essential. This includes:

• Validating dimensional models and schemas.
• Confirming ETL/ELT processes and dependencies.
• Defining security roles and access policies.
• Establishing performance benchmarks and monitoring strategies.

2. Infrastructure Setup

This involves provisioning and configuring the necessary hardware and software components:

• Database Servers: Selecting and setting up the appropriate database management system (e.g., SQL Server, Azure Synapse Analytics).
• Storage: Ensuring sufficient and performant storage for data and indexes.
• ETL/ELT Tools: Installing and configuring data integration tools (e.g., SQL Server Integration Services (SSIS), Azure Data Factory).
• Networking: Configuring network connectivity and firewalls for secure access.

3. Database and Schema Deployment

Deploying the core database structures:

• Creating databases, schemas, tables, views, and stored procedures.
• Applying any necessary database patches or updates.
• Implementing indexing strategies for optimal query performance.

Example SQL script for creating a dimension table:

CREATE TABLE DimDate (
    DateKey INT PRIMARY KEY,
    FullDate DATE NOT NULL,
    DayOfMonth INT NOT NULL,
    Month INT NOT NULL,
    MonthName VARCHAR(20) NOT NULL,
    Quarter INT NOT NULL,
    Year INT NOT NULL
);
                

4. ETL/ELT Process Deployment

Deploying the logic that populates the data warehouse:

• Deploying SSIS packages or Azure Data Factory pipelines.
• Configuring connection managers and sensitive data handling.
• Setting up job scheduling for automated data loads.

5. Security Implementation

Configuring access controls and permissions:

• Creating logins, users, and roles.
• Granting or denying permissions at the schema, table, and column levels.
• Implementing row-level security (RLS) where applicable.

6. Testing and Validation

Rigorous testing is crucial to ensure data accuracy and functionality:

• Unit Testing: Testing individual ETL components and stored procedures.
• Integration Testing: Verifying end-to-end data flow.
• Data Validation: Comparing source data with target data for accuracy and completeness.
• Performance Testing: Stress testing queries and load processes.

7. User Acceptance Testing (UAT)

Involving business users to validate the data warehouse meets their analytical needs.

8. Go-Live and Post-Deployment

The final stage of the deployment process:

• Scheduling the initial full data load.
• Monitoring system performance and resource utilization.
• Establishing ongoing maintenance and support procedures.