Introduction
Modern data teams face a common challenge: how to manage growing volumes of data while ensuring it is reliable, timely, and ready for analytics. Traditional pipelines often break under the pressure of multiple data sources, complex dependencies, and the need for continuous validation. Without the right tools, analysts are left chasing broken SQL scripts, business users lose trust in dashboards, and engineering teams waste hours manually fixing failed jobs instead of building value.
At MetaFactor, our Calgary Data Engineers Experts understand these challenges firsthand and have seen how the right tooling can transform the way organizations work with data. To overcome these issues, two technologies have become essential in the modern data stack: Apache Airflow for workflow orchestration and dbt (data build tool) for SQL-based transformations. Together, they bring structure, automation, and transparency to the entire ELT (Extract, Load, Transform) process.
What is Apache Airflow?
Apache Airflow is an open-source platform that allows teams to programmatically author, schedule, and monitor workflows. Using Directed Acyclic Graphs (DAGs), Airflow lets you define dependencies between tasks, manage retries, and track progress through a user-friendly interface. It is language-agnostic for execution and designed for scalable, repeatable, and auditable workflows. Airflow is preferred over many other orchestration platforms because it has become a widely adopted industry standard, backed by a large open-source community and strong ecosystem of integrations. It gives teams full flexibility through Python-based DAGs rather than relying on rigid, GUI-only schedulers. Unlike proprietary tools, Airflow is cloud-agnostic, meaning it can run on-premises, in AWS, Azure, or GCP, or within managed services such as Astronomer and Google Cloud Composer. This combination of openness, flexibility, and community support makes Airflow a safer long-term choice compared to niche or vendor-locked alternatives. Key strengths:- Flexible Python-based DAG definitions
- Robust scheduling and retry logic
- Rich UI for monitoring pipelines
- Broad ecosystem and strong integration options across both cloud and on-premises environments
What is dbt?
dbt (data build tool) is an open-source command-line tool designed for transformations inside the data warehouse. It enables analysts and engineers to write modular SQL queries that are version-controlled, tested, and documented. dbt focuses on the “T” in ELT, encouraging teams to keep transformations inside the warehouse rather than moving data into separate ETL servers or proprietary platforms. dbt is preferred compared to other transformation platforms because it democratizes data modeling by letting teams use plain SQL rather than specialized coding languages. It introduces software engineering best practices, such as version control, modularity, automated testing, and documentation, into the analytics workflow, something that traditional ETL tools often lack. In addition, dbt is cloud-agnostic, with native integrations for Snowflake, BigQuery, Databricks, and Redshift, giving organizations flexibility without vendor lock-in. Its large and active community also means rapid innovation, shared best practices, and a wide ecosystem of packages and plugins. Key strengths:- SQL-based transformation logic that is accessible to analysts as well as engineers
- Built-in testing and documentation for higher data quality and trust
- Modular and reusable models that speed up development
- Broad integration with modern cloud warehouses such as Snowflake, BigQuery, Databricks, and Redshift
Why Airflow and dbt Work Well Together
While dbt excels at transforming data once it is in the warehouse, it does not handle scheduling, dependency management, or external integrations. Airflow fills this gap by orchestrating dbt runs alongside other tasks, such as:- Loading data from APIs or files into the warehouse
- Triggering machine learning pipelines after transformations
- Running monitoring and validation checks
- Maintain a single orchestration layer for your entire pipeline
- Use dbt’s transformation logic together with Airflow’s scheduling and alerting capabilities
- Build fully automated end-to-end ELT workflows
Example: Building an ELT Workflow
In modern data platforms, the path from raw data to business-ready insights involves multiple coordinated steps. By combining Apache Airflow for orchestration, Databricks for scalable processing and storage, and dbt for modular transformations, organizations can create pipelines that are both powerful and maintainable. Airflow acts as the central conductor, scheduling and triggering each stage of the process, while Databricks handles the heavy lifting of data ingestion, transformation, and storage in Delta Lake format. dbt then takes over within the Databricks environment to apply structured SQL transformations, run automated data tests, and generate documentation. This combination not only streamlines the ELT process but also ensures that data teams can collaborate effectively, maintain quality standards, and adapt quickly to evolving business needs. Extract and Load: Airflow runs tasks to pull data from APIs, transactional systems, or flat files, and loads it into Databricks. Within Databricks, ingestion jobs store this raw data in Delta Lake tables, ready for downstream processing. Transform: Airflow triggers a dbt job that runs inside Databricks using the dbt-databricks adapter. dbt transforms raw Delta tables into curated, analytics-ready datasets, applying cleaning, joins, aggregations, and business logic while maintaining full version control. Validate: Airflow executes data quality checks, leveraging dbt’s built-in tests directly in Databricks or using additional validation scripts. This ensures that only accurate, reliable data moves into the analytics layer. Deliver: Airflow orchestrates the delivery of validated Delta tables from Databricks to BI tools such as Power BI, Tableau, or Looker, enabling stakeholders to access trusted, up-to-date insights. This architecture ensures that the entire process—from raw ingestion to actionable analytics—is fully automated, monitored, and reproducible within a robust Databricks-powered ecosystem.