Background
A prominent asset management firm, based in Los Angeles with over $3 trillion in assets under management, embarked on a strategic initiative to leverage Snowflake for advanced data accessibility. Their goal was to expose front office, back office, and middle office data through Snowflake with proper role-based access control, enhancing their product portfolio in customer service.
Challenge
The firm faced significant challenges nearly a year into the project. Despite Snowflake’s capabilities, they struggled with developer productivity, particularly in managing Snowflake assets such as models, transformation jobs, and extracts. The core issue was the lack of a robust software development process, including version control, which impacted their time to market. This not only resulted in financial costs but also led to frustration among clients due to delayed releases of new Snowflake models.
Solution
When faced with the complex challenge of optimizing Snowflake’s software release processes for a leading asset management firm, our expertise in platform engineering was sought after. Leveraging the core principles of automation and cutting-edge platform engineering strategies, AppZ engineered a bespoke solution that fundamentally transformed their approach to data management and deployment workflows in Snowflake.
Our innovative solution, rooted in the AppZ platform engineering framework, was meticulously designed to automate the entire software release pipeline. From integrating version control systems like GitLab for enhanced collaboration and code management to orchestrating deployment pipelines for seamless transitions across development, QA, and production environments, we crafted a robust, scalable infrastructure. This not only accelerated the development cycle but also ensured that the firm could manage and deploy their Snowflake models and schemas with unprecedented efficiency and precision.
By harnessing the power of AppZ’s platform engineering solutions, we empowered the asset management firm to unlock new levels of productivity, drastically reduce time-to-market, and foster a culture of innovation and continuous improvement within their data engineering teams.
- Integration with GitLab: We enabled data engineers across different divisions to commit their developments into GitLab, fostering a collaborative and version-controlled environment.
- Automated Deployment Pipeline: A custom pipeline was designed to facilitate the seamless push of changes across Snowflake environments (Dev, QA, Production), enhancing the CI/CD process.
- Utilization of Airflow for Orchestration: Airflow served as the dashboard for managing and visualizing the ETL processes, allowing for efficient scheduling and model triggering.
This architecture, integrating GitLab and Airflow, significantly accelerated the development cycle, allowing for rapid iterations and deployments.
Results
The implementation of our platform engineering solution led to transformative outcomes:
- Rapid Development Cycle: We successfully implemented the solution in the development environment within 45 days, followed by QA and production in 15 days each, markedly improving the time to market.
- Enhanced Developer Productivity: Developers can now commit code, test, iterate, and promote their assets through Airflow into Snowflake environments within minutes, drastically reducing deployment times.
- Improved Client Satisfaction: The streamlined process alleviated previous frustrations, resulting in faster delivery of new features and models to clients.
Conclusion
By embracing platform engineering principles and automation, the asset management firm was able to overcome significant operational hurdles, setting a new standard for data management and developer productivity within their organization. This case study exemplifies the transformative potential of platform engineering in addressing complex software development challenges in the cloud computing era.
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