Amazon Redshift Consulting Services
From optimizing Redshift performance to architecting what comes next.
25+
Years of data expertise
100K+
Workloads migrated or managed
45+
Technology specializations
Pythian's experts know Redshift inside and out
Our solutions turn Redshift complexity into competitive advantage.
Optimize performance and cut costs
Redshift health assessment and optimization
We audit your Redshift clusters for bottlenecks and cost waste—distribution keys, sort keys, VACUUM cadence, and workload management. You get a remediation plan that delivers measurable performance and cost improvements.
Modernize to Redshift Serverless
Redshift Serverless transition
We move you from provisioned clusters to Redshift Serverless—elastic scaling and usage-based pricing without disrupting production. Your existing SQL, security models, and AWS integrations stay intact.
Migrate to cloud-native platforms
Cloud-native platform migration
We handle low-risk migrations from Redshift to Snowflake, BigQuery, or Databricks—proprietary SQL refactoring, UDF conversion, and petabyte-scale data movement through S3 staging. Every path targets performance parity on the new platform.
Refactor proprietary SQL and UDFs
SQL and UDF migration engineering
Redshift's SQL diverges from standard PostgreSQL with proprietary extensions, data types, and UDFs that automated tools can't fully convert. We handle the manual refactoring those tools miss, with full validation on the target platform.
Modernize pipelines and orchestration
Data engineering and automation
We modernize ETL/ELT pipelines to Airflow, Glue, or cloud-native orchestration and remap security to target frameworks. The migration becomes an opportunity to eliminate years of technical debt.
Unlock analytics and AI
Production analytics and AI enablement
Transform batch reports into real-time dashboards on Looker, Power BI, or Tableau, then integrate ML workflows using Redshift ML, SageMaker, or target-platform AI services. Data locked in provisioned clusters becomes fuel for self-service analytics and production AI.
Modernizing a global IT service provider's Redshift environment for production analytics and AI
Pythian cut an aging Redshift estate query latency by 60 percent, saving $1.8M annually.
A multinational IT services provider with $750M+ in revenue had outgrown its legacy Redshift architecture. Surging data volumes, brittle ETL pipelines, and deprecated proprietary code drove up costs while blocking the real-time analytics its enterprise customers demanded. Pythian stabilized the foundation, modernized every layer of the stack, and delivered production-ready dashboards and ML capabilities—without disrupting production.
A phased, architecture-aware approach to Redshift
Stabilizing operations
We assess cluster health, key effectiveness, compression, and workload management. For clusters with rising costs or degrading performance, we deliver an optimization plan that stabilizes operations while we plan the path forward.
Cataloguing needs
We catalog every query, UDF, view, and scheduled job, then map the dependencies between them—including downstream BI and ML integrations. This complete picture is where most programs fail without deep Redshift expertise.
A roadmap toward ROI
We recommend the right path—stay on Redshift, go Serverless, or exit to a new platform—based on workloads, cloud strategy, and ROI modeling. Phased milestones deliver quick wins first.
Securing data integrity without disruption
We migrate data, convert proprietary SQL, refactor UDFs, rebuild pipelines, and remap security to target frameworks. Dual-run validation ensures zero disruption so production keeps running until everything is verified on the new platform.
Modern business intelligence
We deliver modern BI, self-service analytics, and AI-ready pipelines on your new platform, plus 24/7 managed support. Ongoing monitoring and cost control ensure lasting returns.
Ready to optimize, modernize, or migrate your Amazon Redshift deployment?
Pythian's related Amazon Redshift services
Integrated expertise across every layer of the data stack.
Build resilient, modern pipelines
Data engineering consulting
We replace fragmented ETL with resilient, observable data flows built on Airflow, AWS Glue, dbt, and cloud-native orchestration—designed to scale with your analytics demands.
Govern and control your data estate
Data strategy and governance
We ensure your new environment is secure, compliant, and cost-controlled—migrating row-and-column-level security policies, defining ownership frameworks, and aligning data investments with business priorities.
Operationalize AI at scale
Production AI
From data locked in Redshift clusters to production-ready AI. We integrate ML workflows using SageMaker, BigQuery ML, Vertex AI, or MLflow—deploying models that deliver measurable business outcomes.
Amazon Redshift consulting services frequently asked questions (FAQ)
Security is built into every phase of our migration process. We start with a comprehensive audit of your existing Redshift security posture—VPC configuration, row-level and column-level access policies, encryption settings, and IAM roles. During migration, we remap Redshift's fine-grained security controls to the target platform's native frameworks (Snowflake RBAC, BigQuery IAM, or Databricks Unity Catalog), preserving the access policies that regulated industries depend on. We also migrate data governance metadata to modern platforms like Collibra, Dataplex, or Unity Catalog. Dual-run validation ensures no gaps in security coverage during the transition, and we maintain full audit trails throughout.
ROI comes from multiple sources. Cost optimization is often the most immediate win—tuning distribution keys, compression encodings, and workload management can reduce compute spend significantly before any migration begins. For organizations migrating to serverless or cloud-native platforms, the shift from provisioned capacity to usage-based pricing typically delivers additional savings, especially for bursty or high-concurrency workloads. Beyond cost, organizations see significant query performance improvements, reduced operational overhead (no more VACUUM management or cluster resizing), and the ability to enable self-service analytics and production AI that weren't feasible on the legacy architecture. Our phased approach means you start seeing returns on high-value workloads early—not just at the end.
Harder than most organizations expect. While Redshift SQL shares PostgreSQL roots, it diverges significantly with proprietary extensions—data types like SUPER and GEOMETRY, features like PIVOT/UNPIVOT and macros, and the upcoming deprecation of Python UDFs in mid-2026. Automated conversion tools can handle a portion of standard queries, but the proprietary features, custom UDFs, and distribution-key-dependent query patterns require manual refactoring by engineers who understand both Redshift's internals and the target platform. Lambda UDFs, materialized views with Redshift-specific optimizations, and workloads tuned around the leader node architecture all need careful redesign. This is where Pythian's dual fluency—deep Redshift knowledge combined with target-platform expertise—makes the difference.
It depends on your workload profile, cloud strategy, and budget. Redshift Serverless is a strong choice for organizations committed to AWS that want elastic scaling and usage-based pricing without leaving the Redshift ecosystem—it preserves your existing SQL, security models, and AWS integrations. Cloud-native platforms like Snowflake, BigQuery, or Databricks are better suited for organizations pursuing multi-cloud strategies, needing higher concurrency, or looking for serverless architectures decoupled from AWS infrastructure management. A phased hybrid approach is also viable—optimize and stabilize your current Redshift environment first, then migrate high-value workloads to the target platform while maintaining Redshift for lower-priority jobs during the transition. Pythian provides vendor-neutral assessment based on workload analysis and ROI modeling, not vendor loyalty.
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