Security is built into every phase of our migration process. We start with a comprehensive assessment of your existing Greenplum security model—role-based access controls, row-level security policies, and encryption configurations. During migration, we remap these controls to cloud-native IAM frameworks (GCP IAM, AWS IAM, Azure AD), preserving the access policies that regulated industries depend on. For organizations in financial services or government, we design dual-run validation architectures that ensure no gaps in compliance coverage during the transition. Data governance metadata is migrated to modern platforms like Google Dataplex, Unity Catalog, or Collibra, so your audit trail remains intact.

The ROI from Greenplum modernization comes from multiple sources. The most immediate win is licensing cost reduction—customers facing Broadcom's restructured pricing see significant reductions in the total cost of ownership by moving to cloud-native platforms. Beyond cost savings, organizations gain elastic scaling that eliminates the complexity of gpexpand operations, improved query performance on modern columnar engines, and the ability to support real-time analytics, semi-structured data, and production AI capabilities that legacy Greenplum couldn't deliver. The PostgreSQL compatibility advantage also means higher automation rates during SQL conversion, which reduces migration timeline and cost compared to exits from proprietary platforms like Teradata or Netezza.

Greenplum's PostgreSQL foundation is a genuine migration advantage here. SQL and PL/pgSQL code conversion to PostgreSQL-compatible targets (AlloyDB, Aurora PostgreSQL) achieves the highest automation rates—significantly higher than migrations from proprietary platforms like Teradata or Netezza. For non-PostgreSQL targets like BigQuery or Snowflake, tools like Google's BigQuery Migration Service and Snowflake's SnowConvert AI handle a large portion of standard SQL translation automatically. However, MPP-specific constructs—distribution policy logic, GPORCA-tuned query patterns, external table definitions, and gpfdist pipeline orchestration—require manual refactoring by engineers who understand both the source and target architectures. MADlib in-database ML models have no automated conversion path and must be rebuilt on platform-native ML tools. This is exactly where Pythian's dual fluency in PostgreSQL heritage and MPP complexity makes the difference.

It depends on your workload characteristics, regulatory requirements, and strategic direction. Exiting to a cloud-native platform (BigQuery, Snowflake, Databricks) is the right choice for most organizations—you gain elastic scaling, modern analytics capabilities, transparent pricing, and freedom from vendor lock-in risk. However, staying within the Broadcom ecosystem makes sense for organizations with specific regulatory compliance or data sovereignty requirements that mandate on-premises control. In that case, we help you upgrade to Greenplum 7.x, deploy on Kubernetes via the Greenplum Operator, and optimize your licensing position. A third option—phased hybrid—lets you migrate high-value workloads to cloud-native platforms first while maintaining Greenplum for workloads with specific on-premises requirements. Pythian provides vendor-neutral assessment based on TCO analysis and business outcomes, not vendor pressure.