Pipelines Your Business Bets On
Production-grade data engineering — streaming and batch pipelines, lakehouse architectures, and cost-governed platforms on Snowflake, Databricks, and the major clouds.
Why Data Engineering Programs Stall
Analytics and AI inherit whatever the pipelines deliver — late, brittle, and expensive is the default. Symptoms: dashboards that break on schema changes nobody communicated, cloud bills growing faster than data volume, and a backlog where every new source is a six-week project because nothing was built for reuse.
The Apptad Approach to Data Engineering
We engineer data platforms like production software: infrastructure-as-code, testing and observability in the pipeline, streaming where freshness pays and batch where it doesn't, and FinOps discipline so the platform's cost curve stays under its value curve.
Lakehouse done right
Medallion architectures on Databricks or Snowflake with governed contracts between layers — consumers build on interfaces, not on table internals.
Streaming where it matters
Kafka/CDC ingestion proven at 50M+ events/day — and the judgment to use cheap batch everywhere freshness doesn’t pay.
Pipelines with tests and SLAs
Data contracts, quality gates, and observability built in — schema drift gets caught in CI, not in the CFO’s dashboard.
Cost as a design constraint
FinOps governance from day one. Speed that doubles your cloud bill isn’t performance, it’s deferred pain.
Data Engineering in Production
Power Generator Lifts Fleet Availability 6 Points with Unified Asset Performance Analytics
A multi-fuel power generator consolidated historian, SCADA, and work-order data into a Databricks lakehouse on AWS — turning reactive maintenance into condition-based asset strategy across 9 GW of capacity.
Read the case study
Tier-1 Carrier Cuts Churn 14% with GenAI Customer Intelligence
Vertex AI + BigQuery customer intelligence platform combines churn prediction with GenAI-powered next-best-action for 38M subscribers.
Read the case study
Top-10 Bank Saves $28M/Year with Real-Time Fraud Detection on Databricks
Replaced a brittle rules-based fraud system with a real-time ML platform on Databricks + Snowpark, scoring 50M+ transactions daily.
Read the case studyData Engineering, Answered Straight
Snowflake or Databricks?
Often both, with clear roles: Snowflake excelling as the governed warehouse and sharing layer, Databricks as the engineering and ML platform. If you must pick one, the deciding factors are workload mix (SQL-analyst-heavy vs. engineering/ML-heavy) and where your team's skills are. We build production platforms on both — the architecture matters more than the logo.
When is streaming worth the complexity?
When the decision the data feeds is time-sensitive enough to pay for it — fraud scoring, replenishment, condition monitoring. Most reporting isn't, and batch remains dramatically cheaper to build and operate. We design for the decision, not the fashion.
How do you keep platform costs under control?
Cost is architecture: right-sized compute, auto-suspend policies, storage tiering, and query governance designed in — then FinOps monitoring with owner-level accountability. On one program that discipline funded the entire managed-services contract out of savings.
Part of our Business Analytics practice.
Snowflake gave us the platform; Apptad gave us the discipline to use it. They migrated our legacy warehouse, retired three duplicate marts, and stood up the customer-360 layer that now drives every personalization decision.
Have a Data Engineering Question? Ask the People Who Ship It
Our Business Analytics practice is led hands-on. Connect on LinkedIn or send a note through the form — a practitioner (not a sales rep) replies within one business day.
