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Improving data accessibility for an asset management firm with Snowflake optimization project

A leading asset management firm in Los Angeles, managing over $3 trillion in assets, launched a strategic initiative to improve its data access capabilities using the Snowflake data warehouse.
A leading asset management firm in Los Angeles, managing over $3 trillion in assets, launched a strategic initiative to improve its data access capabilities using the Snowflake data warehouse.

Goals and challenges

The company aimed to:

  • provide seamless access to front, back, and middle office data through Snowflake
  • introduce appropriate role-based access controls

Nearly a year into the project, the organization encountered significant obstacles:

  • Managing Snowflake architecture, including assets like models, transformation jobs, and extracts, was challenging for the development team and harmed their productivity.
  • The absence of a robust software development process and lack of version control hindered their time to market.
  • Delayed releases of new Snowflake models resulted in additional financial costs and client frustration.

Our approach

In order to optimize Snowflake and the entire software release process, the organization approached us to leverage Maxima Consulting's platform engineering expertise.

We based our architecture solution on the Cloud Orbit platform engineering framework and used it to automate the entire software release pipeline. The solution was successfully implemented in the development environment within 45 days, followed by quality assurance and production in 15 days each.

Key elements of the project included:

  1. Integration with GitLab: Integrated a version control system for enhanced collaboration and code management. This enabled data scientists and engineers across various business units to commit their developments to GitLab, fostering a collaborative and version-controlled environment.
  2. Automated deployment pipelines: The automation of deployment pipelines ensured seamless data sharing across development, quality assurance, and production environments. Designing a tailored pipeline to facilitate seamless changes across Snowflake environments (Dev, QA, Production) enhanced the entire CI/CD process.
  3. Orchestration with Airflow: Utilizing Airflow to manage and visualize ETL processes enabled efficient scheduling and model triggering across teams.

Integrating GitLab and Airflow tools resulted in a robust, scalable infrastructure that accelerated the development lifecycle and enabled the company to manage and deploy its Snowflake data models and schemas with exceptional efficiency and precision.

Client's benefits

The implementation of our platform engineering solution led to transformative outcomes:

  • Rapid development cycle: We managed to significantly improve the time to market for new releases.
  • Top-notch performance: Developers can now commit code, test, iterate, and promote their assets into Snowflake through Airflow 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.

Leveraging our services enabled the asset management firm to overcome significant operational limitations. By embracing platform engineering principles and automation tools, the firm set a new standard for data management and developer productivity.

Frequently asked questions about Snowflake

What is Snowflake?

Snowflake is a popular data warehouse used to store, process, manage, and analyze data. Snowflake's biggest advantage is its scalability - it is capable of efficiently running multiple data workloads at the same time. Additionally, analysts and engineers praise the data warehousing platform for being fast, flexible, and easy to use.

Should your company use a cloud data warehouse like Snowflake?

Data warehouses are used in many industries, including banking, insurance, commerce, healthcare, education, and manufacturing, to facilitate data engineering and business intelligence tasks. Snowflake platform and other data warehousing solutions use massively parallel processing to analyze, process, and integrate data in order to improve performance and data accessibility.

That said, the benefits of data warehouses are significant only if an organization utilizes large volumes of data. For other organizations, implementing such platforms can result in limited advantages. However, if your company expects to utilize more data in the future, embracing cloud data warehouses ahead of time is worth considering.

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