Flock Safety Scales AI and Data Analytics with Snowflake

Rather than outsourcing the migration to a consulting partner, Flock Safety chose to execute the project internally.

The decision was driven by one advantage: experience.

Rains had implemented Snowflake previously and understood both the platform and the team’s existing architecture well enough to recognize that the migration could move much faster than traditional implementation timelines suggested.

“We had enough experience that we weren’t starting from square one, so we decided to deliver the migration ourselves,” says Woytasik.

Instead of treating the proof of concept as disposable work, the team designed it as though it was a mini-migration.

Terraform patterns, infrastructure design, migration scripts and validation processes built during the proof of concept became reusable assets throughout the production rollout.

Snowflake’s technical teams provided guidance on sizing and best practices, while Flock Safety retained ownership of architecture and execution from start to finish.

One of the principles Rains emphasized throughout the migration was avoiding unnecessary processes.

Rather than spending weeks building a rigid implementation plan, the team focused on understanding technical dependencies while allowing ingestion pipelines, transformations, infrastructure and BI integrations to progress in parallel.

“Don’t have a strict plan,” Rains advises. “Have a very rough plan. Most things can be done in parallel.”

That philosophy allowed a four-person project team to accomplish work typically associated with much larger implementation efforts while remaining flexible as new requirements emerged throughout the project.

The result was a migration completed in approximately four months, with the final month devoted primarily to partner integrations and cleanup rather than core platform work.

Artificial intelligence became a force multiplier throughout the migration.

Under Rains’ technical leadership, the team combined AI-assisted development with disciplined engineering practices to automate much of the repetitive work involved in migrating warehouses.

AI generated migration scripts, converted SQL syntax between Redshift and Snowflake, and helped engineers rapidly build Python-based validation tools capable of verifying hundreds of terabytes of migrated data.

Without these tools, creating a comprehensive validation framework would have required substantially more engineering effort.

“We’re not Python experts,” says Woytasik. “In a matter of days we had validation tooling that we were able to use across almost every source to prove our data quality after migrating.”

The emphasis on automated validation gave leadership confidence that data quality remained intact throughout the migration while allowing the engineering team to maintain an aggressive delivery schedule.