Azure Data Engineering Services

We leverage the full Azure data stack including ADLS Gen2 for storage, Azure Data Factory and Synapse Pipelines for ETL, Synapse Analytics for warehousing, Databricks for advanced processing, Event Hubs for streaming, and Purview for governance. Service selection is based on your specific workload requirements and existing Microsoft ecosystem investments.

Typical Azure data platform implementations take 8–12 weeks depending on complexity and scope. Simple use cases can go live in 6–8 weeks, while enterprise-scale platforms with multiple sources, hybrid connectivity, and complex transformations may require 12–16 weeks.

We apply FinOps best practices including right-sizing, reserved capacity analysis, pause/resume scheduling for Synapse pools, intelligent tiering for storage, and comprehensive tagging with Azure Cost Management. Most clients achieve 30–60% cost reduction.

Yes. We implement real-time architectures using Event Hubs (including Kafka-compatible endpoints), Stream Analytics for transformations, and Azure Functions for event processing. We design for sub-second latency with proper dead-letter handling and exactly-once semantics where needed.

We implement comprehensive governance using Microsoft Purview for data catalog, lineage, and classification. Combined with Azure AD, RBAC, and Private Endpoints, we ensure enterprise-grade security and compliance. All implementations support audit logging and regulatory requirements.

Yes. We specialize in migrating legacy SSIS, Informatica, and custom ETL systems to modern Azure-native architectures. Our approach ensures zero data loss, validates transformations, and typically reduces ongoing costs by 30–60% while improving performance and maintainability.