Our Approach

Context

Organizations with legacy on-premise data warehouses often face performance bottlenecks, high maintenance costs, and limited scalability.

Our Approach

• •Assess current schema, query patterns, and data lineage
• •Design cloud-native lakehouse architecture with clear migration paths
• •Implement incremental migration with parallel validation at each stage
• •Build automated data quality monitoring from the start
• •Migrate reports and dashboards with user acceptance testing

Observability Focus

• Migration progress tracking with row-level reconciliation
• Query performance comparison between old and new systems
• Data freshness and quality metrics throughout
• Cost tracking and optimization recommendations

Tech Stack

Context

Teams spending significant time on manual report generation face errors from copy-paste workflows and delayed delivery to stakeholders.

Our Approach

• •Map all report dependencies and data sources
• •Build AI agent to orchestrate data collection and validation
• •Implement validation rules with anomaly detection
• •Create approval workflows for sensitive reports
• •Set up automated distribution and archiving

Observability Focus

• Agent run logs with step-by-step visibility
• Report generation timing and status tracking
• Data validation pass/fail metrics
• Escalation tracking and resolution workflows

Tech Stack

Context

Organizations often discover data quality issues too late, after downstream systems and reports are already affected.

Our Approach

• •Deploy data quality rules at ingestion points
• •Build AI agent for anomaly detection and triage
• •Implement automated root cause analysis
• •Create self-service quality dashboards
• •Set up alerting with context-rich notifications

Observability Focus

• Real-time quality scoring per data source
• Anomaly detection accuracy tracking
• Time to detection and resolution metrics
• Alert fatigue monitoring and tuning

Tech Stack

Context

Legacy ETL tools reaching end of life create risk, especially when jobs have undocumented dependencies.

Our Approach

• •Automated discovery and documentation of existing jobs
• •Map dependencies and execution patterns
• •Build parallel pipelines with output comparison
• •Implement gradual cutover with instant rollback capability
• •Create comprehensive runbooks and team training

Observability Focus

• Job execution comparison dashboards
• Data diff reports between old and new pipelines
• Resource utilization and cost comparison
• Migration progress and risk indicators

Tech Stack

Context

Multiple BI tools across departments create conflicting metrics and force analysts to spend time reconciling numbers.

Our Approach

• •Inventory all reports and metric definitions
• •Create unified semantic layer with agreed metrics
• •Migrate high-value reports to single platform
• •Build self-service analytics with governance
• •Deprecate redundant tools and reports

Observability Focus

• Report usage and adoption tracking
• Metric consistency validation
• Query performance monitoring
• User feedback collection

Tech Stack