Ingestion, transformation, and analytics at petabyte scale. What used to take weeks now takes hours. AWS-native, horizontally scaled.
We build production-grade data pipelines on AWS that handle ingestion, transformation, and analytics at any scale. From real-time streaming to batch processing, our pipelines are built to be reliable, cost-efficient, and fully automated.
We leverage the full AWS data stack: Lambda for serverless processing, SQS for message queuing, Glue for ETL, and NiFi for complex data routing.
We've built pipelines that process petabytes of data for Fortune 500 companies. That same expertise is now available to growing businesses.
Architectures designed to scale horizontally from day one. Process 10 records or 10 billion. The pipeline adapts automatically.
All pipeline code in Python, all infrastructure as code in Terraform. Reproducible, version-controlled, and fully auditable deployments.
We audit your current data landscape: sources, volumes, latency requirements, and downstream consumers.
Design the pipeline architecture with AWS-native services, defining ingestion, transformation, and delivery stages.
Implement the pipeline with comprehensive testing, data quality checks, and performance benchmarking at scale.
Production deployment with monitoring, alerting, and cost optimization. Ongoing tuning to maintain peak performance.
Every data problem has a different shape. Here are the most common pipeline architectures we design and build, ranging from consolidating scattered data sources to processing millions of events per second.
Stop pulling reports from six different SaaS tools. We consolidate your CRM, ERP, marketing platforms, operational databases, and third-party APIs into a single S3-based data lake, queryable with Athena, Redshift Spectrum, or your preferred BI tool. One source of truth, finally.
Data engineering requirements vary significantly by industry. The volume, latency, compliance constraints, and downstream consumers are all different. Here is where we have built production systems.
Clinical trial data processing, EHR system integration, patient analytics platforms, and research data pipelines, with HIPAA compliance, strict data governance, and audit logging that satisfies both internal and regulatory requirements.
Fivetran, Airbyte, and dbt are excellent tools for the right use case. Here is an honest comparison to help you decide when a custom AWS-native pipeline is the better investment.
Excellent for ingesting standard SaaS sources into a warehouse. The right answer when your data sources are all supported connectors.
Powerful platforms, but often overbuilt for growing businesses and expensive to run at low utilization.
Right-sized for your actual data volume. You own everything, pay for what you use, and have no per-connector pricing or platform minimums.
