From siloed PostgreSQL to fresh customer context: How InfluxData modernized its data foundation

InfluxData did not set out to build a modern data stack for the sake of it.

They were trying to solve a much more practical problem: different teams were looking at the same business and seeing different numbers.

Marketing and sales pulled data from separate systems and ended up with conflicting metrics. Analysts spent the first ten minutes of meetings reconciling dashboards instead of discussing growth. Engineers were hand-coding pipelines to move data between PostgreSQL and downstream tools, then fixing them when they broke.

The problem was not a lack of data. It was a lack of a governed, unified system of record.

To fix it, InfluxData adopted Snowflake as their centralized data warehouse and implemented RudderStack as their customer data infrastructure to collect, transform, and deliver customer data with control and reliability.

Main takeaways

• A centralized warehouse is a prerequisite, but not enough. You also need reliable, governed pipelines feeding it.
• Custom, hand-coded integrations do not scale. Customer data infrastructure reduces engineering burden while increasing control.
• Identity-resolved event data unlocks deeper funnel visibility and personalization.
• Giving analytics teams end-to-end pipeline ownership accelerates time to value.
• A single source of truth eliminates recurring reporting friction and meeting-time debates.
• The same foundation that fixes reporting also enables AI-ready customer context.

The real issue: Centralization without consistency

Before the shift, InfluxData had:

• Product and backend data in PostgreSQL
• Marketing automation in Marketo
• CRM data in Salesforce
• BI in Domo
• Basic web insights in Google Analytics

Data was moving, but it wasn’t unified. Pipelines were custom-coded. Governance required manual work. There was no central warehouse acting as a shared source of truth.

This is a common pattern in growing SaaS organizations. Data exists everywhere, but context does not. Teams rely on point-to-point integrations and fragile scripts that were never designed to scale.

The result is drift. Definitions diverge. Metrics disagree. Engineering becomes a bottleneck for even simple routing changes.

What changed: Customer data infrastructure + Snowflake

InfluxData made two foundational decisions:

1. Establish Snowflake as the system of record.
2. Use RudderStack to collect, route, and standardize data across their stack.

Instead of writing and maintaining brittle ETL code for each integration, they used RudderStack pipelines to:

• Extract data from PostgreSQL, Salesforce, Marketo, and product events
• Land that data in Snowflake in a structured, consistent way
• Route modeled data back out to downstream tools

In one notable case, their team built a custom RudderStack connector to move PostgreSQL data cleanly into Snowflake and downstream destinations. Rather than continuing to maintain hand-coded integrations everywhere, they centralized the logic in one governed pipeline layer.

That shift gave the analytics team end-to-end control over ETL without needing to hire a dedicated senior engineer just to maintain pipelines.

More importantly, it created a single source of truth.

From warehouse unification to customer context

Once data was centralized in Snowflake and flowing reliably through RudderStack, the impact went beyond cleaner dashboards.

InfluxData began using event stream data to understand:

• What drove product signups
• How users navigated marketing pages
• Where users dropped off in the funnel
• How identity stitched across product and web interactions

Instead of anonymized, surface-level analytics, they gained visibility into real user journeys.

This is the shift from fragmented data to customer context.

For InfluxData, this meant moving from raw event counts to a unified view that their whole team could trust. Customer context is not just raw events. It is:

• Identity-resolved behavioral data
• Operational system data
• Modeled traits and attributes
• Governed, consistent definitions

When that context is assembled in the warehouse and delivered reliably to downstream tools, teams can optimize experiences, personalize journeys, and align around shared metrics.

For InfluxData, this meant:

• Eliminating 5–10 hours per week previously spent reconciling reports
• Empowering analysts to control pipelines
• Freeing engineers to focus on product work
• Supporting over 1,900 customers and 750,000 daily active instances with a more scalable foundation

Why this matters in the AI era

InfluxData’s transformation was not originally about AI. It was about consistency and scalability.

But the architecture they put in place is exactly what AI-era systems require.

AI-driven personalization, onboarding assistants, and automated decisioning rely on:

• Clean event data
• Stable identity resolution
• A warehouse system of record
• Governed delivery paths

Without those foundations, AI systems amplify inconsistencies instead of creating value.

By centralizing their data in Snowflake and using RudderStack to collect, transform, and deliver it with control, InfluxData built a foundation that supports analytics, activation, and future AI use cases without re-architecting from scratch.

This is the practical side of customer data infrastructure. It is not about replacing your warehouse. It is about making your warehouse usable across teams, systems, and experiences.

If your teams are spending more time reconciling numbers than acting on insights, the issue is rarely the BI tool. It is usually the data movement and governance layer underneath.

RudderStack gives data teams governed pipelines into their data cloud, so the warehouse becomes a reliable system of record rather than another source of drift. When your pipelines are stable and your warehouse becomes the shared source of truth, the rest of your stack starts to work the way it was intended.

If you want to see how InfluxData implemented this in practice, read the full case study.