Data Warehouse Consulting

Brittle and slow-to-scale legacy ETL/ELT pipelines cause constant rework, operational overhead, and inconsistent data outputs.

How We Solve It:

• We offer a layered pipeline design with reusable transformation components.
• Automated testing and data quality checks are built into every step.
• Pipelines that evolve with the schema.

Inconsistent data, fragmented security controls, and outdated governance practices diminish trust and reduce data value while increasing compliance risk as you scale.

How We Solve It:

• We build scalable governance frameworks with metadata catalogs and federated stewardship.
• Security-first processes with lineage visibility and risk assessment.
• Role-based access and compliance mapping (GDPR, HIPAA, etc.).

A rigid legacy warehouse is costly to maintain while being highly vulnerable to rising security threats. It often also struggles with handling modern data velocity and compliance demands, which can be solved by adopting a data warehouse as a service (DWaaS).

How We Solve It:

• Flexible, modular architectures to support new data sources.
• Zero-trust security with access controls, continuous threat monitoring, and encryption.
• Automated optimization and provisioning.

Your current traditional warehouses are incapable of supporting AI workloads, resulting in fragmented ML workflows and slow model deployment. It also leads to the pipelines being rigid.

How We Solve It:

• Native integration with suitable ML frameworks, such as TensorFlow, PyTorch, and H2O.ai.
• Complete MLOps automation with CI/CD and drift detection.
• AI-driven ingestion, automated transformation logic, and schema detection.

Most legacy warehouses face performance blockages, rigid schemas, complex data warehouse migration, and costly scaling. These give rise to downtime, unpredictable costs, and data loss, negatively affecting your valuable data.

How We Solve It:

• Early TCO modeling and cost control frameworks.
• Our phased migration approach offers quick-win pilots and dependency mapping.
• We employ structured cutover planning.

Every new data initiative needs a strategically planned foundation. Improper or rushed implementation can lead to fragmented architectures and expensive reworks.

How We Solve It:

• We use top hybrid or cloud-native services, such as BigQuery, Snowflake, or Azure Synapse, for end-to-end implementation.
• Set up optimal data infrastructure and security details.
• A clear roadmap ensures the architecture aligns with your long-term data strategy.

An unoptimized data warehouse often leads to excessive compute consumption and slow dashboards, resulting in high operational costs.

How We Solve It:

• We implement cost governance frameworks and automated resource scaling for predictable spending.
• Continuous query tuning and data pipelines reduce cloud cost.
• Optimize indexing and storage strategies to enhance read times for important analytical workloads.

Your outdated legacy DWH may be limiting your ability to adopt advanced analytics. Data modernization services enable your architecture to handle diverse modern data formats.

How We Solve It:

• Migration to scalable cloud platforms.
• We design flexible data models for easy integration with new data sources.