Amazon Redshift Consulting Services
Case study
Global IT service provider modernized Redshift operations to unlock advanced analytics
Optimized Redshift infrastructure saved $1.8M annually.
Pythian deployed cloud-native architecture to rebuild brittle pipelines and resolve cluster concurrency bottlenecks halting enterprise reporting.
Faced with escalating operational bottlenecks, enterprise IT solutions provider struggled with surging data volumes and brittle ETL pipelines that stalled critical customer-facing reporting. Turning to Pythian, the company sought to stabilize its warehouse and establish an infrastructure ready for AI and analytics. Pythian migrated the legacy estate to RA3 managed storage and integrated modern streaming pipelines to accelerate data delivery. Engineers now focus on deploying predictive models and tracking live dashboards instead of manually fixing table bloat and data extraction risks.
Pythian's deep technical expertise turned our data warehouse from a performance bottleneck into a competitive differentiator."
Chief Information Officer
Enterprise IT Solutions Provider
$1.8M
Infrastructure savings
60%
Reduction in query latency
99.9%
Platform availability
Take control of your Amazon Redshift architecture and performance.
The enterprise IT service provider, generating over $750M in revenue, hit an operational wall when its legacy data warehouse failed to support modern analytics.
The company's IT leaders partnered with Pythian to shift engineering focus away from database maintenance and toward real-time business intelligence.
We reached a point where data maintenance was consuming our entire engineering capacity and holding back business growth. Pythian broke that bottleneck, transforming our data operations so our teams can focus entirely on delivering predictive intelligence for our clients."
Chief Information Officer
Enterprise IT Solutions Provider
Ballooning infrastructure costs eroded operational margins
The IT service provider saw its AWS spend climb 40 percent year over year while performance continued to degrade under heavy concurrent workloads.
Neglected VACUUM maintenance caused severe table bloat
Omission of routine VACUUM operations and misaligned distribution keys across multi-terabyte tables triggered massive broadcast joins, causing analytical queries to stall under heavy disk utilization.
Brittle batch jobs delayed customer reporting
The enterprise IT solutions provider faced severe business impacts when legacy batch integration jobs routinely failed to complete overnight, delaying customer-facing service level reports.
Rigid extraction workflows blocked advanced analytics
Internal data science teams faced week-long extraction delays and potential security risks when attempting to access production data for model training.
Decoupling legacy database nodes resolved chronic resource contention.
Pythian database specialists migrated the over-provisioned Amazon Redshift DC2 clusters to RA3 instances with managed storage. Table and key realignments achieved a 10x performance boost on scan-heavy reporting workloads. Transitioning to Auto WLM with concurrency scaling resolved asset contention and eliminated query backlogs.
Refactoring batch architectures established real-time data streaming.
Engineers replaced legacy Informatica batch ETL with an Apache Airflow and AWS Glue framework. The team deployed Zero-ETL pipelines to stream Amazon Aurora data directly into the warehouse while leveraging Redshift Spectrum for high-performance S3 queries. Refactoring deprecated Python UDFs into optimized SQL removed critical dependencies from daily operations.
Centralized access controls secured sensitive data assets.
Pythian consultants established a centralized data ownership framework to manage row- and column-level security policies. The team deployed AWS Lake Formation to automate lineage tracking and enforce fine-grained access controls over sensitive enterprise records. Standardized security controls provided the verified auditing capabilities required by financial services and healthcare customers.
Deploying native analytical integrations unlocked advanced production AI capabilities.
Cloud engineers connected Looker, Power BI, and Amazon SageMaker directly to optimized Amazon Redshift materialized views via Redshift ML. This native integration allowed analysts to run predictive models using standard SQL without executing external data extractions. The deployed environment now automatically generates live customer dashboards and automated decision support for ticket scaling and capacity forecasting.
Modernize your data warehouse and accelerate business analytics.
Maximizing enterprise value through Pythian’s AWS partnership
As an Advanced AWS Partner, Pythian integrates modern cloud architecture to solve complex enterprise scale challenges.
Pythian maintains credentialed validation across core AWS data disciplines, deploying verified frameworks that lower total cost of ownership across multi-terabyte nodes. Discover how our strategic collaboration with AWS accelerates infrastructure modernization, reduces total cost of ownership, and unlocks production-ready artificial intelligence capabilities.
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