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Our client, a litigation support-services firm, was grappling with a significant challenge related to their court reporting system.
An initial data migration from an on-premises system to a Snowflake environment was deemed inefficient, costly, and riddled with performance issues. Realizing the need for expert intervention, the firm sought out Launch for our technical expertise and experience in data migration.
Our client’s Snowflake-related costs increased significantly due to issues with data quality and inefficiencies related to the prior data migration from on-premises to Snowflake. Additionally, integrating multiple data sources while maintaining data consistency proved complex and cumbersome. The firm urgently needed a reliable, cost-effective, and efficient data migration process.
Launch conducted a thorough assessment of the existing solution, identifying critical pain points to formulate a strategic plan to address each issue systematically.
Refactoring the Solution
Launch quickly identified that, in order to streamline the data ingestion process, the existing code needed to be refactored. Our lead engineer for the project conducted this refactoring, which included transitioning the data ingestion process from JSON to a more efficient tabular format, simplifying data processing, and drastically reducing processing time.
Streamlining Data Pipelines
Launch focused on improving data pipelines by identifying bottlenecks in the existing data architecture. We determined a need to transition our client from their existing Airflow tool to Azure Data Factory, which offered better performance and cost efficiency. Furthermore, we consolidated multiple data sources into a master pipeline, eliminating redundancies and improving data consistency.
Implementing Parallel Processing
To enhance efficiency, Launch introduced parallelism in data processing. This change allowed multiple tables to be refreshed simultaneously, significantly reducing the time required for data updates.
Improving Reporting
Our team optimized Sigma reports to prevent unnecessary data refreshes, reducing resource usage and speeding up report generation times, making the reporting process more efficient.
Results
Launch helped bring about a remarkable transformation in our client’s data migration process:
- The shift to a tabular format for data ingestion and the transition to Azure Data Factory led to a 90% cost reduction and improved overall performance.
- Data update processing time was slashed from approximately 2 hours down to 5 minutes.
- Data quality and consistency was improved across the board, increasing reporting accuracy and reducing issues.
Our client now has a reliable Snowflake environment that quickly and efficiently parses data from multiple sources. The seamless integration of Launch's resources with the client’s team ensured smooth project execution, fostering a productive and collaborative environment.
Experience And Expertise in Data Migration
Our experience and technical expertise in these types of projects enabled our client to overcome their data migration challenges effectively. By refactoring inefficient code, streamlining data pipelines, and leveraging modern data processing tools, Launch delivered a solution that was both cost-effective and highly efficient.
Launch played a pivotal role in helping navigate the complexities that come with a data migration, going beyond the immediate task to identify cost-saving opportunities and improve overall efficiency. Contact us today to learn more about how Launch can integrate with your team to fully understand and address your specific needs with tailored solutions.