DevOps Solutions
DevOps for Data Pipelines
Data pipelines are infrastructure too, and they deserve the same DevOps rigor as your application code. We implement DataOps for your ETL/ELT workflows — orchestration with Airflow or Dagster, data quality validation, warehouse provisioning, and CI/CD pipelines that test data transformations before they run in production.
Need this done for your project?
We implement, you ship. Async, documented, done in days.
DataOps Challenges
Data pipelines fail silently, produce incorrect results without obvious errors, and are notoriously difficult to test. Orchestration tools need reliable infrastructure. Data quality issues cascade downstream. Schema changes in source systems break transformations. We implement infrastructure and practices that make data pipelines reliable, observable, and maintainable.
Orchestration Infrastructure
We deploy Apache Airflow or Dagster on Kubernetes with proper scaling — separate scheduler, webserver, and worker pods. Workers auto-scale based on queue depth. DAGs are deployed via Git with CI validation. Task-level retries, SLA monitoring, and failure alerting are configured. For simpler needs, we use Prefect Cloud or temporal.io.
Data Quality & Testing
Great Expectations or dbt tests validate data at every stage of the pipeline. Schema validation catches upstream changes. Row count checks detect missing data. Distribution checks identify anomalies. Tests run in CI when transformation code changes and at runtime when data flows through. Failed quality checks halt the pipeline before bad data reaches your warehouse.
Data Warehouse Provisioning
We provision and configure BigQuery, Snowflake, Redshift, or PostgreSQL-based warehouses via Terraform. Access controls follow least-privilege principles. Cost controls prevent runaway query costs. Materialized views and incremental models optimize query performance. dbt manages the transformation layer with version-controlled SQL models.
CI/CD for Data
Transformation code (dbt models, Python transformations, SQL scripts) goes through the same CI/CD rigor as application code. Pull requests run dbt compile, schema tests, and data tests against a development warehouse. Production deployments are automated on merge. Rollback procedures restore previous transformation logic if issues are detected.
How It Works
Purchase the engagement, submit your async brief with your data sources, transformation requirements, and warehouse preferences, and receive a complete DataOps implementation within 7–14 business days.
Why Anubiz Engineering
100% async — no calls, no meetings
Delivered in days, not weeks
Full documentation included
Production-grade from day one
Security-first approach
Post-delivery support included
Ready to get started?
Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.