From a Row-Store Bottleneck to an Elastic, Consolidated Analytics Platform

Our Client's Backstory

The client operates a multi-tenant ad-revenue analytics product used by digital publishers to understand where their money comes from. It ingests data from hundreds of ad-tech and analytics vendors, warehouses it, and lets each customer build datasets, custom reports, mappings, charts, dashboards, and alerts on top of it. The numbers on those screens are revenue, CPMs, and fill rates, the metrics publishers use to run their businesses.

The product is one of several under a larger organization that had already standardized on Snowflake as its central data platform. Consolidating every product's data onto that single platform was a priority, and this analytics product, still warehousing on PostgreSQL, was the last major data store sitting outside it.The initiative mattered on two fronts: the platform was both the wrong engine for a fast-growing analytical workload and the odd one out on the company's data strategy.

The Challenges

By mid-2025, a PostgreSQL warehouse that had served the platform well was buckling under a workload it was never built for, with real consequences for customers and cost.

• Reports running for hours: Heavy aggregations over ever-larger tables pushed report jobs past any interactive limit, and a revenue report that takes hours is stale before it lands on the customer's screen.

• Bulk loads getting slower: Tens of gigabytes of new connector data landed daily, and the row-store's write path slowed further with every increase in volume.

• The wrong engine for the job: PostgreSQL is a row-oriented transactional database; the reporting workload is the opposite (large analytical scans over huge, append-heavy tables), so no instance upgrade could fix the mismatch.

• Storage and cost climbing in lockstep: Volume had passed 2 TB on a managed instance filling toward its ceiling, at roughly $1,700/month for compute, storage, and IOPS provisioned around the clock.

• Off the company's standard platform: Staying on Postgres meant investing further in the exact technology the organization was deliberately consolidating away from.

• Accumulated workaround debt: To make Postgres cope, the team maintained hand-built machinery: a precomputed "totals table," request-time schema introspection, CSV-shuffling export paths, each a permanent maintenance burden.

The Result

The migration delivered a warehouse matched to the shape of the workload: faster, cheaper to scale, and simpler to maintain.

~85× storage compression

A several-hundred-gigabyte dataset collapsed to a few gigabytes on Snowflake after columnar compression, turning relentless storage growth into a negligible line item.

Reports from hours to minutes

Heavy report jobs that once ran for hours now complete in minutes, with nearly half the runtime of a representative 24-minute job, pure data-shuffling, engineered away entirely.

~50% of query time reclaimed

Eliminating request-time schema introspection removed roughly half of query time and restored a test suite that had regressed to ~300× slower back toward Postgres-era speed.

Net reduction in code

The core change touched about 100 files, yet removed several hundred more lines than it added. This retired the totals table, introspection, and CSV export paths in favor of native Snowflake capabilities.

One consolidated platform

The last major data store now lives on the organization's standard Snowflake platform, with elastic headroom for spiky ingestion and full isolation between interactive and background workloads.

The Result

The migration delivered a warehouse matched to the shape of the workload: faster, cheaper to scale, and simpler to maintain.

~85× storage compression

A several-hundred-gigabyte dataset collapsed to a few gigabytes on Snowflake after columnar compression, turning relentless storage growth into a negligible line item.

Reports from hours to minutes

Heavy report jobs that once ran for hours now complete in minutes, with nearly half the runtime of a representative 24-minute job, pure data-shuffling, engineered away entirely.

~50% of query time reclaimed

Eliminating request-time schema introspection removed roughly half of query time and restored a test suite that had regressed to ~300× slower back toward Postgres-era speed.

Net reduction in code

The core change touched about 100 files, yet removed several hundred more lines than it added. This retired the totals table, introspection, and CSV export paths in favor of native Snowflake capabilities.

One consolidated platform

The last major data store now lives on the organization's standard Snowflake platform, with elastic headroom for spiky ingestion and full isolation between interactive and background workloads.

The Result

The migration delivered a warehouse matched to the shape of the workload: faster, cheaper to scale, and simpler to maintain.

~85× storage compression

A several-hundred-gigabyte dataset collapsed to a few gigabytes on Snowflake after columnar compression, turning relentless storage growth into a negligible line item.

Reports from hours to minutes

Heavy report jobs that once ran for hours now complete in minutes, with nearly half the runtime of a representative 24-minute job, pure data-shuffling, engineered away entirely.

~50% of query time reclaimed

Eliminating request-time schema introspection removed roughly half of query time and restored a test suite that had regressed to ~300× slower back toward Postgres-era speed.

Net reduction in code

The core change touched about 100 files, yet removed several hundred more lines than it added. This retired the totals table, introspection, and CSV export paths in favor of native Snowflake capabilities.

One consolidated platform

The last major data store now lives on the organization's standard Snowflake platform, with elastic headroom for spiky ingestion and full isolation between interactive and background workloads.

The Result

The migration delivered a warehouse matched to the shape of the workload: faster, cheaper to scale, and simpler to maintain.

~85× storage compression

A several-hundred-gigabyte dataset collapsed to a few gigabytes on Snowflake after columnar compression, turning relentless storage growth into a negligible line item.

Reports from hours to minutes

Heavy report jobs that once ran for hours now complete in minutes, with nearly half the runtime of a representative 24-minute job, pure data-shuffling, engineered away entirely.

~50% of query time reclaimed

Eliminating request-time schema introspection removed roughly half of query time and restored a test suite that had regressed to ~300× slower back toward Postgres-era speed.

Net reduction in code

The core change touched about 100 files, yet removed several hundred more lines than it added. This retired the totals table, introspection, and CSV export paths in favor of native Snowflake capabilities.

One consolidated platform

The last major data store now lives on the organization's standard Snowflake platform, with elastic headroom for spiky ingestion and full isolation between interactive and background workloads.

The Result

The migration delivered a warehouse matched to the shape of the workload: faster, cheaper to scale, and simpler to maintain.

~85× storage compression

A several-hundred-gigabyte dataset collapsed to a few gigabytes on Snowflake after columnar compression, turning relentless storage growth into a negligible line item.

Reports from hours to minutes

Heavy report jobs that once ran for hours now complete in minutes, with nearly half the runtime of a representative 24-minute job, pure data-shuffling, engineered away entirely.

~50% of query time reclaimed

Eliminating request-time schema introspection removed roughly half of query time and restored a test suite that had regressed to ~300× slower back toward Postgres-era speed.

Net reduction in code

The core change touched about 100 files, yet removed several hundred more lines than it added. This retired the totals table, introspection, and CSV export paths in favor of native Snowflake capabilities.

One consolidated platform

The last major data store now lives on the organization's standard Snowflake platform, with elastic headroom for spiky ingestion and full isolation between interactive and background workloads.