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
Redshift services that create an architecture-aware advantage
Stabilize
Redshift health assessment
We assess your cluster environment to identify bottlenecks, cost waste, and performance risks. You get a benchmarked baseline and a clear remediation plan to build from.
Optimize
Performance optimization
We tune distribution keys, sort keys, compression, and workload management to cut compute spend and accelerate queries. For elastic workloads, we transition provisioned clusters to Redshift Serverless.
Migrate and modernize
Cloud-native platform migration
Pythian migrates workloads from legacy platforms (Teradata, Oracle, Netezza) to Redshift, and from Redshift to modern alternatives like Snowflake, BigQuery, or Databricks. We handle proprietary SQL refactoring, UDF conversion, and data validation end to end.
How we work with you
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.
Modernizing a global IT service provider's Redshift environment for production analytics
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.
Pythian: The enterprise migration leader
Legacy platform and on-premise migrations to Redshift
Teradata
Eliminate expensive proprietary hardware for a scalable, pay-as-you-go cloud architecture.
Oracle
Trade complex licensing and manual indexing for automated scaling and seamless AWS integration.
Netezza
Move from rigid, end-of-life appliances to high-performance, petabyte-scale analytics.
Vertica
Replace complex node management with Redshift’s automated concurrency scaling and effortless patching.
SQL Server
Break free from restrictive compute bottlenecks with a cloud-native engine.
Db2
Modernize legacy mainframe data into a high-concurrency environment optimized for modern BI tools.
Greenplum
Transition from self-managed Postgres clusters to a zero-maintenance, fully managed MPP (Massively Parallel Processing) service.
Modern platform migrations to Redshift
BigQuery
Shift from a purely serverless to predictable, high-performance reserved instance.
Databricks
Move from complex Spark-based management to a SQL-first environment that empowers analysts without notebook overhead.
Snowflake
Consolidate data silos within the AWS perimeter to simplify security governance.
Database and NoSQL migrations to Redshift
MySQL
Upgrade from a transactional RDS setup to a dedicated OLAP engine designed for complex analytical queries.
DynamoDB
Bridge the gap between NoSQL operational data and long-term analytical insights using Redshift’s seamless ETL integration.
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.
Deliver real-time insights
Data-driven analytics
From batch Redshift reports to real-time insights on Looker, Power BI, and Tableau. Business users get answers in seconds, with self-service access that removes the data team as a bottleneck.
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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