Data Pipeline Orchestration
Build, schedule, and monitor ETL workflows with Apache Airflow. Process and analyze data at scale with ClickHouse and PostgreSQL.
What You Get
Pipeline Architecture
Apache Airflow for Workflow Orchestration
Airflow is the industry standard for programmatically authoring, scheduling, and monitoring workflows. Define your data pipelines as Python code with DAGs (Directed Acyclic Graphs).
Built-in operators for databases, cloud services, and custom transformations. Monitor pipeline health, retry failed tasks, and set up alerting - all from a beautiful web UI.
Common Use Cases
ETL Pipelines
Extract data from multiple sources, transform and clean it, then load into your data warehouse on a schedule.
Scheduled Reports
Generate daily, weekly, or monthly reports from your data and deliver them via email, Slack, or S3.
ML Pipelines
Orchestrate machine learning workflows: data prep, training, validation, and model deployment.
Data Sync
Keep databases in sync, replicate data across systems, and maintain data consistency.
Data Stack
Apache Airflow
Workflow orchestration platform
ClickHouse
Real-time analytics database
PostgreSQL
Relational database for metadata
n8n
Visual workflow automation
MinIO
S3-compatible object storage
ClickHouse for Analytics
ClickHouse is a blazing-fast columnar database designed for real-time analytics. Query billions of rows in milliseconds using familiar SQL syntax.
Perfect as the destination for your Airflow pipelines. Store processed data, run ad-hoc queries, and power dashboards with sub-second response times.
Ready to build your data platform?
Start with a free account and deploy Airflow with ClickHouse today.
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