Comprehensive Data Engineering Services
We build scalable, reliable data infrastructure that transforms raw data into strategic assets. Our solutions combine cutting-edge technologies with proven architectural patterns.
Modern Data Pipeline Architecture
Design and implement batch and streaming pipelines using frameworks like Apache Beam, Spark, and Flink with optimizations for cost and performance. Implement CDC (Change Data Capture) patterns for real-time database synchronization.
Implementation Example:
Built a petabyte-scale retail data pipeline processing 2M+ events/sec with 99.99% uptime using Spark Structured Streaming and Delta Lake.
Cloud Data Platform Engineering
Architect Microsoft Fabric solutions with Synapse, Azure Data Lake, and Cosmos DB. Implement data mesh architectures with domain-oriented ownership and self-service capabilities.
Implementation Example:
Migrated legacy EDW to Azure Fabric with 70% cost reduction while improving query performance 5x.
Data Warehouse Modernization
Transform traditional warehouses into cloud-native analytics platforms using Snowflake, BigQuery, or Redshift with dimensional modeling, data vault 2.0, and star schema optimizations.
Implementation Example:
Modernized healthcare payer's data warehouse handling 50TB+ of claims data with sub-second query response for analysts.
Real-time Data Processing
Build event-driven architectures with Kafka, Event Hubs, and Kinesis. Implement complex event processing with Flink SQL and stateful stream processing.
Implementation Example:
Developed real-time fraud detection system processing 500K TPS with <100ms latency using Flink and Redis.
Data Governance & Quality
Implement data contracts, lineage tracking (OpenLineage), and quality monitoring with Great Expectations. Automate metadata management with DataHub or Purview.
Implementation Example:
Established enterprise data governance framework reducing data incidents by 80% through automated quality checks.
ML Data Infrastructure
Build feature stores (Feast, Hopsworks) and vector databases for AI applications. Implement data versioning with DVC and experiment tracking.
Implementation Example:
Created feature platform serving 1M+ features/sec to production ML models with 99.9% availability SLA.
Our Data Engineering Methodology
We follow a disciplined approach to deliver reliable, scalable data systems:
Requirements Analysis
Conduct thorough assessment of data volumes, velocity, variety and business SLAs to determine optimal architecture patterns.
Architecture Design
Create blueprint addressing ingestion, storage, processing and serving layers with failure modes and scaling considerations.
Technology Selection
Choose appropriate stack balancing performance, cost and maintainability based on workload characteristics.
Implementation
Develop with infrastructure-as-code (Terraform), CI/CD pipelines, and automated testing frameworks.
Performance Tuning
Optimize partitioning, indexing, caching and query patterns through iterative benchmarking.
Operationalization
Implement monitoring (Prometheus/Grafana), alerting, and automated recovery procedures.
Our Data Engineering Technology Stack
We leverage the most powerful tools in modern data infrastructure:
Reference Architecture
Our typical enterprise data platform blueprint:
End-to-end data platform handling batch and streaming workloads with governance and monitoring
Ready to Build Your Data Foundation?
Our certified data engineers will design and implement infrastructure that scales with your business needs.
Discuss Your Data Strategy