How DataOps expertise turned fragile data pipelines into a high-reliability backbone

Modernize the data stack to support advanced AI workloads while improving reliability and lowering cost—without disrupting live products.

• Migrate legacy systems to cloud-native architecture
• Implement DataOps practices and end-to-end observability
• Stand up scalable feature-engineering pipelines for ML
• Reduce incidents and accelerate model development

Fragile pipelines, manual operations, and a failed cloud migration left teams firefighting instead of building.

• Legacy data pipelines failed 34% of the time (training delays)
• Data quality issues affected 58% of ML models (customer complaints)
• Prior cloud migration failed after $4.1M spend
• Manual processing capped throughput at 100GB/hour
• 47% duplicate datasets from absent governance
• Infra costs 63% over budget (inefficient usage)

CleverX assembled cloud, streaming, and DataOps specialists to rebuild the platform around event-driven patterns, product-oriented datasets, and automated quality controls.

Expert recruitment:

• 19 consultants: 8 cloud architects, 6 DataOps specialists, 5 streaming experts
• Avg 7 years at petabyte scale; Kubernetes, Spark, real-time processing
• Multi-cloud and cost-optimization experience

Technical framework:

• Event-driven architecture processing 10B events/day
• Data mesh with 50 domain data products
• Feature platform serving 1M features/sec
• Data quality monitoring for 500 critical datasets

Quality protocols:

• SLA monitoring with 99.9% uptime targets
• Automated testing for all pipelines (unit + contract + e2e)
• Full lineage for compliance/debugging
• Disaster recovery with 30-minute RTO

Delivered in four sprints to minimize disruption while lifting stability, speed, and efficiency.

Weeks 1–2: Assessment & architecture

• Reviewed 300 pipelines for optimization/migration paths
• Designed cloud-native architecture cutting costs 40%
• Staged migration plan to avoid downtime

Weeks 3–6: Platform implementation & migration

• Deployed Kubernetes-based data platform with auto-scaling
• Migrated 10TB/day flows with zero downtime
• Built streaming pipelines (latency: hours → seconds)

Weeks 7–8: Automation & enablement

• Automated 80% of pipeline deployment tasks
• Trained 100 engineers on DataOps practices
• Launched self-service data platform for 500 users

Week 9: Performance & cost

• Optimized utilization (-$180K/month)
• Caching reduced query times 75%
• Cost allocation model for 50 business units

Efficiency gains:

Automation and self-service compressed cycle times and sped delivery.

• Pipeline build time cut 3 weeks → 3 days
• Data processing costs down 42%
• Feature engineering accelerated 65%
• Data freshness improved 24h → 15 minutes

Quality improvements:

Reliability and data trust increased across ML and analytics.

• 46% improvement in pipeline reliability
• Data quality scores 61% → 89%
• Incidents down 45 → 8 per month
• ML training success 67% → 92%

Business impact:

Stability and speed translated into revenue and retention.

• 5 new AI features launched, adding $3.2M revenue
• Annual infra spend reduced $2.4M
• Churn down 21% with higher reliability
• Time-to-market for AI products faster by 8 weeks

Strategic advantages:

A platform designed to scale without linear cost growth.

• Supports 10× growth at non-linear cost curve
• 200 reusable pipelines accelerate future builds
• Data marketplace enabling cross-team discovery/usage
• DataOps framework adopted by 3 portfolio companies

Recognized by a major cloud partner for excellence in large-scale DataOps.