ELT Architecture
Modern ELT pattern with warehouse-centric transformation. Stack scales with your maturity.
Click any node for details.
Decision Log
Pipeline Decisions
- Batch vs. streaming, 15-60 min batch covers most reporting needs; add streaming only for real-time use cases.
- Incremental vs. full refresh, Incremental for large tables (>1M rows); full refresh simpler for smaller datasets.
- Orchestration choice, Airflow for complex dependencies; Dagster/Prefect for better developer experience.
- Quality gates, Block downstream models on critical test failures; alert-only for non-critical.
Integration & Ops
- Extraction layer, Fivetran for speed; Airbyte for cost savings and custom connectors.
- Transformation, dbt for SQL-first teams; Spark for complex joins or ML feature engineering.
- SLA monitoring, Freshness checks in dbt or Monte Carlo; PagerDuty for on-call alerting.
- Cost controls, Warehouse resource monitors and query tags for attribution.
Overview
Manual data work is eating your team's time. We build automated pipelines that extract, transform, and deliver data where you need it; reliably, on schedule.
Stack by Stage
| Component | Enterprise | Mid-Market | Startup |
|---|---|---|---|
| Warehouse | Snowflake | BigQuery | PostgreSQL |
| Extraction | Fivetran | Airbyte | Custom scripts |
| Transform | dbt Cloud | dbt Core | dbt Core |
| Orchestration | Dagster Cloud | Airflow | Prefect |
| Quality | Monte Carlo | Great Expectations | dbt tests |
| BI | Looker | Metabase | Metabase |
Automation wins
Time savings
Eliminate 20+ hours/week of manual reporting work.
Data freshness
Dashboards update automatically, on your schedule.
Data quality
Automated tests catch issues before they reach stakeholders.
Auditability
Full lineage and logs for every data transformation.
Pipeline services
- •ELT pipelines with dbt and incremental models
- •Orchestration with Airflow, Dagster, or Prefect
- •CDC (Change Data Capture) with Debezium or Fivetran
- •Data quality checks with Great Expectations or dbt tests
- •Idempotent, resumable pipeline design
- •SLA monitoring and alerting for data freshness
- •Self-serve analytics layer with semantic modeling
Pipelines we automate
Pipeline FAQs
Do we need to move to a cloud data warehouse?
Not necessarily; we can work with PostgreSQL, MySQL, or other databases. But cloud warehouses (Snowflake, BigQuery) make scaling and collaboration easier.
How do you handle real-time requirements?
Most reporting needs are actually "near real-time" (every 15-60 minutes). For true streaming, we implement Kafka or Flink-based solutions.
What about our legacy systems?
We connect to anything with an API, database, or file export. Legacy systems often require custom extraction logic, which we handle.