Optimizing Data Archival in Salesforce with Snowflake Integration

Optimizing Data Archival In Salesforce With Snowflake Integration

TECHNOLOGIES USED

Salesforce

Source of data to be archived.

Snowflake DB

Destination for archiving, utilizing SQL queries for data management.

Boomi

Integration platform facilitating the update of Salesforce records based on archival data identified in Snowflake.

GOALS

The overarching goals for this data archival project were defined as follows:

Efficiently Archive Data: Archive over 40 million records from Salesforce to Snowflake, adhering to Involvement Criteria, Exclusions, and Master business rules.

Minimize Database Developer Workload: Mitigate the increased workload on DB developers due to the nature of the project, heavily dependent on database operations.

Establish Robust Testing Methodology: Design and implement a proper testing methodology to ensure the accuracy and reliability of the data archival process.

Streamline Offshore Support and Onsite Coordination: Overcome challenges related to offshore support and onsite coordination, ensuring seamless collaboration between teams.

CHALLENGE

The project faced several key challenges in its mission to archive over 40 million records from Salesforce to Snowflake. These challenges included the sudden overload of work, placing significant strain on DB developers, a lack of a proper testing methodology, and hurdles related to offshore support and onsite coordination.

SOLUTION

In addressing the challenges posed by the data archival project and striving to achieve its defined goals, a comprehensive set of solutions was strategically implemented. The process began with the provision of detailed design and development documents, offering a structured roadmap for the archival process. A pivotal step involved conducting an in-depth review of the database architecture, providing leadership to optimize and align it precisely with the goals of the archival initiative. Simultaneously, SQL query optimization enhanced the efficiency of data retrieval and updates within Snowflake, ensuring a streamlined and effective migration.

RESULT

The implementation phase included a series of measures such as rewriting Snowflake DB scripts to align them meticulously with project requirements, establishing staging tables to facilitate a seamless archival process, and running scripts for efficient data migration. Regular stand-up calls, reviews, and testing sessions were instituted to maintain project standards and promptly address identified issues. Additionally, the generation of comprehensive reports comparing the count of archival records for 3 and 5 years played a crucial role in data verification and validation. Thorough requirements gathering ensured that all criteria, exclusions, and master business rules were diligently considered in the archival process. The outcome of these solutions was highly commendable, as evidenced by the successful archival of over 40 million records, the reduction of DB developer workload through optimized scripts, the establishment of a structured testing methodology, improved offshore support and onsite coordination, and the receipt of appreciation for the project’s successful execution.