How to Build a Data Pipeline
Create an automated data pipeline that extracts, transforms, and loads data for analytics and reporting.
What You'll Learn
This intermediate-level guide walks you through how to build a data pipeline step by step. Estimated time: 16 min.
Step 1: Define your data sources
Inventory all data sources — databases, APIs, SaaS tools, event streams — and document their schemas and update frequencies.
Step 2: Choose your pipeline architecture
Select batch processing with Airflow, streaming with Kafka, or ELT with Fivetran based on your latency and complexity needs.
Step 3: Implement extraction
Build connectors to pull data from each source with proper error handling, incremental loading, and schema change detection.
Step 4: Add transformation logic
Write SQL or Python transformations using dbt or custom code to clean, join, and model data for your analytics needs.
Step 5: Monitor pipeline health
Set up alerts for pipeline failures, data quality issues, freshness SLA violations, and row count anomalies.
Frequently Asked Questions
ETL or ELT?▾
ELT is the modern standard — load raw data into your warehouse first, then transform with dbt. ETL is better when you need to filter sensitive data before loading.
Which orchestration tool should I use?▾
Airflow for complex DAGs and custom operators. Prefect for modern Python-native orchestration. Dagster for data-asset-centric pipelines.
How do I handle schema changes?▾
Implement schema evolution detection, version your transformations, and use flexible column types. Alert on unexpected schema changes so you can update pipelines.