Snowflake Data Warehouse

Modernizing a Vertica Projection-Era Analytics into a Cloud-Native, AI-Ready Platform

3 min read

Mar 5, 2026 1:45:55 PM

How Pythian helped a global IT services provider exit Vertica Enterprise Mode and deliver production analytics and AI

A global IT services firm running mission-critical analytics on a legacy Vertica Enterprise Mode cluster faced mounting pressure from ownership instability, rising licensing costs, and an inability to scale for AI workloads. Pythian stabilized the environment, executed a zero-disruption migration to Snowflake on AWS, and delivered self-service analytics and production-ready AI.

60%

Reduction in query latency

$1.8M

Annual infrastructure savings

99.99%

Platform availability post-migration

Account profile

Industry: Information Technology and Services

Organization scale: Global enterprise, $750M+ revenue

Tech stack:

Vertica Enterprise Mode (20+ node on-premises cluster)
Apache Kafka
Snowflake on AWS
Apache Airflow, Fivetran, dbt
Tableau, Power BI
Snowpark ML, Python

When ownership uncertainty meets architectural limits

The firm had built its analytical environment on a 20+ node on-premises Vertica cluster over the previous decade, powering client SLA reporting, capacity planning, and fraud detection. Three converging pressures forced a reassessment.

Platform ownership instability

Vertica's fifth ownership change in 15 years — the pending Rocket Software acquisition — triggered a board-level review. Vendor support continuity and roadmap confidence dropped below acceptable thresholds for a firm whose revenue depends on uninterrupted data services.

200 Projections and tribal knowledge

The environment relied on over 200 hand-tuned projections, dozens of UDx functions written in C++ and Python, and tangled COPY-based batch and Kafka Scheduler streaming pipelines. Only a handful of senior DBAs could maintain it, and every new analytical workload required weeks of projection design.

DBA bottlenecks blocking growth

Client-facing dashboards ran on DBA-mediated query access with multi-hour turnaround. VerticaPy ML models for predictive capacity planning couldn't be operationalized — costing an estimated 20 percent in excess provisioning and limiting the firm's ability to compete for AI-driven contracts.

From projection-era complexity to cloud-native advantage

Pythian followed a clear principle: stabilize what's running, map every dependency, then move — without disrupting production analytics. The engagement spanned Foundation Layer stabilization through Platform modernization to Production AI enablement, and we delivered it as a single coordinated program.

Discovery phase

We audited the full Vertica estate: cluster configuration, projection designs, sort orders, encoding strategies, resource pools, and Tuple Mover health. We mapped every projection to the query pattern it served — the step most migration programs skip and most rollbacks trace back to. The audit uncovered 200+ projections, 35 UDx functions, 14 VerticaPy ML models, and a streaming layer that processed 800 million rows daily.

Strategic architecture

Based on workload analysis and ROI modeling, Pythian recommended a cloud-native exit to Snowflake on AWS. The firm's workloads were structured, SQL-heavy, and concurrency-sensitive — making Snowflake the closest architectural match to Vertica's columnar MPP model.

Projection replacement

We mapped projections to Snowflake clustering keys and materialized views.

Pipeline modernization

We swapped Kafka Scheduler streaming for Airflow, Fivetran, and dbt.

ML rebuild

We rebuilt the VerticaPy ML layer on Snowpark ML with a path to production AI.

Implementation roadmap

Phase 1: Stabilize and blueprint

We conducted a projection and workload audit across the full cluster, identified risks tied to the Rocket Software transition, and built a remediation plan to keep SLA reporting running. We then delivered a vendor-neutral platform recommendation with phased milestones.

Phase 2: Migrate with zero-disruption validation

We mapped 200+ projections to Snowflake clustering keys, refactored analytical SQL, rewrote 35 UDx functions, rebuilt 14 ML models on Snowpark ML, and extracted petabyte-scale data via EXPORT TO PARQUET. Dual-run validation ensured zero disruption to production workloads.

Phase 3: Enable analytics, AI, and managed services

We delivered self-service analytics on Snowflake, migrated Tableau and Power BI dashboards, and built AI-ready pipelines for production ML. Pythian provided 24/7 managed services during and after the migration.

Measurable outcomes that redefined the firm's data capability

The migration expanded what the organization could do with its data. A DBA-bottlenecked, projection-dependent environment became an elastic, self-service cloud platform — turning the data estate from a cost center into a revenue-enabling asset.

60% reduction in query latency

The firm's 50 most critical workloads now run on Snowflake's clustering and caching layer with no DBA intervention. New analytical workloads deploy in hours, not weeks.

$1.8M in annual infrastructure savings

The firm saved an estimated $1.8 million annually by shifting from Vertica's volume-based licensing and on-premises hardware to Snowflake's consumption model on AWS. Retiring the 20+ node cluster freed capital for AI investment.

99.99% platform availability with self-service access

Over 300 business users now have direct analytical access without waiting for a DBA to design a new projection.

Excess provisioning cut from 20% to under 5%

Predictive capacity planning models now run in production on Snowpark ML, enabling the firm to offer AI-driven managed services to its own customers.

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