Projections are Vertica's defining architectural concept—and the single biggest migration risk. They're not just indexes or materialized views; they're physically sorted, compressed, and distributed copies of table column subsets optimized for specific query patterns. We start with a deep projection audit that maps every projection to the query pattern it serves, the sort order it relies on, and the encoding strategy it uses. Then, we translate that performance intent into the target platform's optimization model: Clustering keys and materialized views in Snowflake, sort keys and distribution styles in Redshift, clustered and partitioned tables in BigQuery, or Z-order optimization in Databricks. This projection-to-platform-native mapping is the critical expertise gap that separates successful Vertica migrations from costly rollbacks.

ROI comes from multiple sources. The most immediate win is typically shifting from Vertica's data-volume-based licensing plus on-premise hardware costs to a cloud consumption model—which fundamentally changes the economics in your favor. Beyond cost savings, organizations gain elastic scaling for peak workloads without hardware procurement, self-service analytics that eliminate the DBA bottleneck for every new analytical workload, and AI-ready infrastructure that Vertica's centralized model makes difficult. Our phased approach delivers quick wins on high-value workloads early in the engagement, so you start seeing returns before the full migration is complete.

The Rocket Software acquisition (expected to close mid-2026) is Vertica's fifth ownership change in 15 years. Rocket Software has announced intent to invest in Vertica as part of its modernization platform strategy, but its track record is primarily in mainframe and legacy infrastructure—not competing head-to-head with Snowflake or Databricks. We provide honest, vendor-neutral guidance based on your specific workloads and priorities. For some organizations, staying on Vertica under Rocket Software and modernizing to Eon Mode is the right call. For others, the ownership uncertainty is the trigger to exit to a cloud-native platform. We help you make that decision based on workload analysis and ROI, and we support whichever path you choose—including stabilizing your current environment while you evaluate options.

Standard Vertica SQL that aligns with PostgreSQL syntax can often be converted with automated tools. However, Vertica's proprietary analytical extensions—TIMESERIES gap filling and interpolation, MATCH clause pattern recognition, EVENT_NAME() sessionization, and event series joins—require manual refactoring by engineers who understand both the source semantics and the target platform's equivalent patterns. UDx functions written in C++, Java, Python, or R against the Vertica SDK must be completely rewritten for the target platform. VerticaPy in-database ML models need to be rebuilt on BigQuery ML, Snowpark ML, Spark ML, or MLflow. This is precisely where Pythian's dual fluency—in both Vertica's projection era and cloud-native platforms—makes the difference. We've refactored these workloads across complex, petabyte-scale environments and know where the hidden performance dependencies live.