ETL Pipelines with Apache Airflow & dbt
Automated ETL and ELT pipelines, real-time streaming and orchestration with Dagster and Airflow.
Pipelines That Do Not Wake You Up At Night
Most pipeline work we are called in for is not a greenfield build. It is a set of jobs that run too long, fail quietly and have no owner. We rebuild those as metadata-driven pipelines with testing, observability and alerting so failures surface before the business notices.
We work in both batch and streaming, orchestrated with Dagster or Airflow depending on what your team can support. The right answer is usually the one your engineers can operate without us on the call.
How We Build Pipelines
Metadata-Driven ELT
One framework that adds sources through configuration rather than new code.
- Config-driven source onboarding
- Reusable extract and load patterns
- Incremental and full refresh modes
- Schema drift handling
Real-Time Streaming
Event pipelines for the cases where nightly batch is genuinely not enough.
- Kafka, Event Hubs and Kinesis ingestion
- Structured Streaming and Flink
- Change data capture
- Late and out-of-order event handling
Apache Airflow & dbt Orchestration
Apache Airflow, dbt and Dagster set up so dependencies, retries and model lineage stay explicit.
- Dagster asset and job design
- Airflow DAG development
- dbt model development and testing
- Backfill and catch-up strategy
- SLA and alerting configuration
Data Quality
Tests that run with the pipeline rather than a dashboard nobody opens.
- Expectation and assertion frameworks
- Freshness and volume checks
- Reconciliation against source
- Quarantine and replay patterns
Ingestion Tooling
Managed connectors where they save money and custom code where they do not.
- Fivetran and managed connector setup
- Custom API and file ingestion
- Database replication
- Vendor versus build assessment
Observability
Knowing what ran, what it cost and what broke.
- Pipeline run metrics and logging
- Lineage capture
- Cost per pipeline attribution
- On-call runbooks
What You Get
- A pipeline framework that onboards new sources through configuration
- Batch and streaming paths chosen deliberately rather than by habit
- Data quality tests running as part of every pipeline execution
- Orchestration with explicit dependencies, retries and alerting
- Lineage and run history you can query when something looks wrong
- Runbooks so your team owns the platform after handover
Pipelines That Feed AI Systems
A RAG system is a data pipeline with a language model on the end of it. The failure modes are the ones we already handle: stale sources, silent schema drift and no way to tell whether today's output is worse than yesterday's. We build ingestion and embedding pipelines with the same testing and observability as the rest of the platform.
- Document ingestion from SharePoint, Confluence, S3 and ticketing systems
- Chunking strategy, metadata enrichment and deduplication
- Scheduled embedding generation, refresh and backfill
- Writes into OpenSearch, pgvector, Databricks Vector Search or Cortex Search
- PII detection and redaction before indexing
- Retrieval evaluation and regression tests on every run
Ready to Get Started?
Book a free consultation and we will map out the right approach for your platform, your timeline and your budget.
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