Skill Guide

ETL/ELT pipeline design and orchestration (Airflow, dbt, Prefect)

ETL/ELT pipeline design and orchestration involves architecting, implementing, and managing automated data workflows that extract data from sources, transform it into usable formats, and load it into target systems, using tools like Airflow, dbt, and Prefect.

This skill is critical for enabling reliable, scalable data infrastructure that drives analytics, machine learning, and business intelligence. It directly impacts business outcomes by ensuring data freshness, quality, and accessibility for decision-making.

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How to Learn ETL/ELT pipeline design and orchestration (Airflow, dbt, Prefect)

Focus on understanding core concepts: ETL vs. ELT paradigms, data transformation logic, and basic scheduling. Learn SQL fundamentals, Python scripting, and the role of orchestration in data workflows.

Practice designing pipelines for specific use cases, such as incremental loads or error handling. Avoid common mistakes like hardcoding credentials or neglecting idempotency. Use scenario-based exercises to integrate tools like Airflow DAGs with dbt models.

Master complex system design, including multi-cloud orchestration, dynamic pipeline generation, and performance optimization. Align pipeline architecture with business KPIs, mentor teams on best practices, and implement observability frameworks for proactive monitoring.

Practice Projects

Beginner

Project

Build a Simple ETL Pipeline with Airflow

Scenario

Extract daily sales data from a CSV file, transform it by calculating totals, and load it into a PostgreSQL database.

How to Execute

1. Set up a local Airflow instance using Docker. 2. Create a DAG with tasks for extraction, transformation (using PythonOperator), and loading. 3. Implement basic logging and error handling. 4. Schedule the DAG to run daily and verify outputs in the database.

Intermediate

Project

Implement an ELT Pipeline with dbt and Airflow

Scenario

Design an ELT pipeline to ingest raw user activity data into a data warehouse, then use dbt to transform it into a analytics-ready star schema.

How to Execute

1. Use Airflow to orchestrate data loading from an API into Snowflake. 2. Write dbt models for staging, intermediate, and final transformations. 3. Integrate dbt runs as Airflow tasks with dependency management. 4. Add data quality tests in dbt and monitor pipeline runs via Airflow's UI.

Advanced

Project

Architect a Scalable Multi-Source Pipeline with Prefect

Scenario

Build a dynamic, fault-tolerant pipeline that ingests data from multiple APIs, handles schema evolution, and orchestrates downstream ML model training.

How to Execute

1. Design a Prefect flow with tasks for each API, implementing parallel execution and retries. 2. Use Prefect's parameters for dynamic configuration. 3. Integrate schema validation and evolution handling. 4. Connect to a Kubernetes cluster for scaling and add observability with logging and alerting. 5. Document architecture for team adoption.

Tools & Frameworks

Software & Platforms

Apache Airflowdbt (data build tool)Prefect

Use Airflow for complex DAG-based scheduling and monitoring, dbt for SQL-based transformation and testing, and Prefect for Python-native workflow orchestration with dynamic capabilities. Apply based on team expertise and use case complexity.

Data Infrastructure

Apache SparkSnowflakeAWS GlueGoogle Cloud Dataflow

Leverage these for scalable data processing and storage. Spark for heavy transformations, Snowflake as a cloud data warehouse, and Glue/Dataflow for serverless ETL in cloud environments.

Monitoring & Observability

PrometheusGrafanaDatadog

Implement these to track pipeline health, performance metrics, and failures. Use for alerting on SLA breaches or data quality issues in production systems.

Interview Questions

Answer Strategy

Focus on idempotency, incremental processing, and watermarking. Sample answer: 'I would design the pipeline with idempotent tasks to allow safe re-runs, use watermarks to track processing timestamps, and implement a late-data handling strategy like a separate correction pipeline that merges late records without duplicating existing data.'

Answer Strategy

Test problem-solving and communication skills. Sample answer: 'A DAG failed due to a schema change in a source API. I first isolated the issue using Airflow logs, then rolled back the pipeline to a stable version. I communicated with stakeholders via Slack to set expectations, implemented schema validation tests in dbt, and documented the incident for future prevention.'