DACPAC Pros & Cons

The DACPAC (Data-tier Application Component Package) is a Microsoft SQL Server technology used for packaging and deploying SQL Server database schema and objects. It is specifically designed for SQL Server environments and is not applicable to other database platforms.

Here's a list of its pros and cons:

 Pros: 

•  Version Control: Using GITHUB we can be able to control version management for database schema and objects, which helps in tracking changes over time and collaborating with team members effectively.

• Version control allows multiple team members to work on the same codebase simultaneously.
• Single source of truth: It ensures all the team members is working with the latest version of scripts and database objects.
• The version control enables developers to revert to a previous version if needed.
• The developers creating the same procedure for testing purposes in different names. While manually merging the procedure will affect the functionality which is already working in production. 
• Version control systems maintain a detailed history of changes, including who made that and when and the purpose of the commit against JIRA.
• We can trace back to specific commits, making it easier to identify issues.
• New team members can quickly understand the history and context of database changes by seeing version control logs.
• Code reviews become more effective when using version control. Earlier we can’t be able to compare the scripts with previous version of scripts.
• The entire project will be validated for errors while compiling the project and populate the warnings to improve the code quality.

•  Simplified Deployment: It simplifies the deployment process by packaging all database objects and schema into a single unit, making it easier to deploy changes across different environments.
• Automated deployment pipelines can pull the correct version of scripts, reducing human error.
• Manual deployments are complicated even though experienced team members may make mistakes while executing scripts in sequence.
• Here I have mentioned few incidents for human errors.
• Typos, incorrect order of execution, or missing steps can lead to production deployment failure.
• Coordinating manual deployments among team members is challenging and its heavily depends on developers’ availability.
• Manually executing scripts, verifying changes, and handling rollbacks can be time-taken process and it require more downtime. 
• Manual deployments lack the benefits of branching, merging, and tagging.
• For greenfield deployment, new database can be created by using any version.
• Consistency: With DACPAC, we can ensure consistency across various database instances like QA, Cert and Production environment. since we are deploying the same package each time, reducing the chances of errors due to manual interventions.
•       By managing database scripts in version control, we can ensure consistent deployments across different environments (development, testing, production).
• Schema Comparison and Synchronization: DACPAC provides tools for comparing database schemas between different environments and synchronizing them, which helps in maintaining consistency across development, testing, and production environments.
•       To compare the database objects, earlier we are depending on third party tools and cannot be performed in client environment.
• Automated Deployment: DACPAC supports automated deployment through scripting or integration with deployment pipelines, enabling continuous integration and continuous deployment (CI/CD) practices.
• Integration with Visual Studio: DACPAC integrates with Visual Studio and its familiar for development team. We can easily review the script changes and it’s avoided developer dependency for review.
• while deploying in production environment, if any error occurs due to invalid objects it will automatically rollback to the previous state.
• Post deployment script to insert or update data. It will support both Greenfield and Brownfield deployment.

 

Cons:

•  Limited to Microsoft SQL Server: DACPAC is primarily designed for Microsoft SQL Server environments, so it may not be suitable for other database platforms. 
• Lack of Support for Some Features: DACPAC may not fully support certain advanced database features or configurations, leading to limitations in certain scenarios. 
• Some examples of features or configurations that may not be fully supported by DACPAC include:
• SQLCLR Objects: DACPAC may have limitations when it comes to managing SQL Common Language Runtime (SQLCLR) objects such as stored procedures, user-defined functions, and triggers written in .NET languages like C# or VB.NET.  
• Partitioning: DACPAC may have limitations in managing partitioned tables or indexes, which are used for improving query performance and data management in large databases. 
• Full-text Search: DACPAC may not fully support managing Full-text Search configurations or objects, which are used for efficient text search capabilities within SQL Server databases. 
• Potential for Data Loss: Incorrect usage of DACPAC or improper deployment scripts can potentially lead to data loss or corruption if not handled with caution. 
• Dependency Management: Dependency management can be complex, especially when dealing with dependencies outside the database schema, such as linked servers, external services, etc. Currently we are not using linked server concept. 
• Error Handling and Rollback: DACPAC deployments can encounter errors during deployment, such as schema conflicts or data migration failures. We should have proper rollback mechanism to resolve the impact of deployment failures and maintain data consistency. 
• Data Preservation during Deployment: DACPAC primarily focuses on schema deployment, especially in scenarios involving schema changes or table restructuring, requires manual scripting. 
• Handling Seed Data and Reference Data: DACPAC doesn't handling seed data, it requires additional manual steps or scripting.  
• Large Data Volume: Deploying DACPACs with large volumes of data can lead to performance issues during deployment, especially in scenarios where data needs to be synchronized across environments.