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Redshift-to-Snowflake migration modernizes Fintech data infrastructure with scalable orchestration

A leading European fintech company that provides a secure platform for investing in digital assets, including cryptocurrencies, stocks, ETFs, and precious metals. As a fast-growing innovator in the financial technology space, the client relies heavily on data to drive product development, user experience, and regulatory compliance.

We helped the client to modernize its cloud data infrastructure. The project involved migrating core analytics workloads from Amazon Redshift to Snowflake, and implementing a managed orchestration-as-a-service (MWaaS) solution using Astronomer and Apache Airflow to streamline and scale data pipeline automation.

Client: A leading blockchain investing platform
Year: 2024

As the client expanded its services and customer base, its existing Redshift-based data warehouse began to show limitations. Performance bottlenecks, scalability challenges, and increased maintenance overhead were affecting the data team’s ability to deliver timely, reliable insights.

The company needed a more flexible, high-performance data platform capable of supporting growing analytical demands, automation, and real-time data processing.

Challenge

A new comprehensive data modernization strategy was designed and implemented, focused on two key pillars: cloud data warehouse migration and modern orchestration. A cloud-native data architecture was designed and implemented, tailored to the demands of scaling shared mobility platforms. We optimized their data warehouse environment by ingesting data from their operational database and migrating key workloads to Snowflake, leveraging its scalable compute model to handle concurrent queries and complex joins efficiently.

First, a seamless migration from Amazon Redshift to Snowflake was executed, ensuring minimal disruption to existing workflows and analytics operations. Snowflake’s decoupled compute and storage architecture provided enhanced performance, elastic scalability, and a simplified cost model. This enabled the company to process large volumes of data more efficiently and support a broader range of analytical use cases.

At the same time, a managed orchestration environment was deployed using Astronomer’s MWaaS platform, powered by Apache Airflow. This new orchestration layer replaced fragmented, legacy workflows with a centralized, scalable system for scheduling, monitoring, and managing data pipelines. The implementation improved reliability, enabled better error handling and observability, and accelerated development cycles.

Solution

Partnership outcomes

With the new cloud-native architecture in place, the client experienced a significant uplift in performance and operational efficiency. Query response times improved by up to 80%, allowing analysts and data scientists to access insights faster and more consistently. The Snowflake platform enabled the organization to scale compute resources dynamically, supporting concurrent workloads without compromising performance.

The adoption of Astronomer Airflow for pipeline orchestration reduced manual intervention, enhanced visibility across data processes, and empowered the data engineering team to innovate and iterate more rapidly. Additionally, the move to a pay-as-you-go infrastructure model helped the client to optimize costs while maintaining enterprise-grade performance and security.

Overall, the solution equipped the client with a modern, reliable data foundation that supports real-time insights, regulatory agility, and data-driven product innovation, positioning the company for continued growth in the dynamic fintech landscape.

80%

Faster query performance after migrating from Redshift to Snowflake, enabling real-time analytics and faster decision-making.

65%

Less manual intervention through automated, centralized orchestration using Astronomer and Apache Airflow.

50%

Lower infrastructure and maintenance costs achieved via Snowflake’s elastic scaling and pay-as-you-go cloud model.

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