We build data pipelines, warehouses, and real-time streaming systems that transform raw, siloed data into the reliable intelligence your business needs to move fast and win.
We design, build, and operate the full data stack — from ingestion to insight — so your team always has clean, fast, reliable data.
We design and build batch and streaming pipelines from any source — APIs, databases, SaaS tools, files — to any destination. Idempotent, fault-tolerant, monitored. Built on Airflow, dbt, Airbyte, or Fivetran depending on your stack. Your pipelines won't silently fail at 2 AM while you're sleeping.
Snowflake, BigQuery, and Redshift architecture — star/snowflake schemas, partitioning, clustering, and cost optimization so queries are fast and bills stay low.
Sub-second analytics with Kafka, Apache Flink, and Spark Streaming. Live dashboards, fraud detection, and real-time recommendations — all at scale.
Looker, Tableau, Power BI, and Metabase — we build semantic layers and dashboards that non-technical users actually use, with self-service drill-down built in.
Feature stores, training data pipelines, and model serving infrastructure — so your ML models always train on fresh, validated, production-quality data.
Great Expectations and Monte Carlo for automated data validation, lineage tracking, anomaly detection, and regulatory compliance frameworks built into every pipeline.
Inventory all data sources, assess quality and completeness, map relationships, and identify the highest-ROI analytics use cases to start with.
Choose the right stack (cloud warehouse, orchestrator, transformation layer), model the schema, define data contracts, and design monitoring strategy.
Build, test, and document all ingestion and transformation pipelines with full observability — alerts on failures, data drift, and SLA breaches.
Connect to BI tools, ML systems, and business workflows. Train your team, hand off documentation, and offer ongoing monitoring retainers.
That's exactly what we specialize in. Messy data is the norm, not the exception. We start with a data audit to understand what you have, identify gaps, and build pipelines that clean and validate data as it flows. We also handle legacy system migration and data reconciliation between siloed sources.
Yes. We design for both. Most businesses need a combination — batch for historical analytics, real-time streaming for operational dashboards and ML features. We'll help you decide what's worth the added complexity of real-time and what's better served by well-optimized batch jobs running every hour or day.
We work across AWS (S3, Glue, EMR, Redshift, Athena), Google Cloud (BigQuery, Dataflow, Pub/Sub), and Azure (Synapse, Data Factory, Event Hubs). We're cloud-agnostic and will recommend the right platform based on your existing infrastructure, team skills, and cost constraints.
We implement column-level encryption, role-based access controls, audit logging, and data masking for PII. We've built GDPR-compliant and HIPAA-ready pipelines. All pipelines include data lineage tracking so you can audit exactly where every record came from and what transformations it went through.
Free data audit — we'll review your current data stack, identify the biggest bottlenecks, and give you a clear roadmap for building a reliable data foundation. No obligations.