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Service Comparison: ETL and Orchestration

Two jobs get blurred together here. Moving and transforming data (ETL or ELT) and running steps in order, on a schedule, with retries (orchestration). Some products do both, some do one.

This page compares AWS Glue, Azure Data Factory, Google Dataflow, managed Airflow (Amazon MWAA and Google's managed Airflow), and two open-source tools, dbt and Dagster. No prices; check each vendor's pricing page. Background: ETL vs ELT and Data pipelines and orchestration.

The short version

Product Mainly does Style You manage
AWS Glue Data integration: catalog, crawlers, ETL jobs Serverless, Spark-based jobs, visual or code Job code and triggers
Azure Data Factory Pipelines that copy and orchestrate, plus mapping data flows Managed, visual authoring Pipelines, integration runtimes
Dataflow Batch and streaming processing with Apache Beam Fully managed, code or prebuilt templates Beam pipeline code
Amazon MWAA Run Apache Airflow DAGs Managed Airflow, DAGs in S3 DAGs, environment settings
Google managed Airflow (Composer) Run Apache Airflow DAGs Managed Airflow, DAGs in Cloud Storage DAGs, environment settings
dbt SQL transformations inside a warehouse Open-source engine (dbt OSS) plus the hosted dbt platform Models, tests
Dagster Orchestrate data assets Orchestrator (the docs link a public GitHub repo; the docs page does not use the words open source) Asset code

AWS Glue

  • A serverless data integration service to discover, prepare, move and integrate data.
  • Parts: the Data Catalog (central metadata), crawlers that infer schemas, ETL jobs on a Spark-based engine, Glue Studio for visual job building, notebooks and interactive sessions.
  • Triggers start jobs and crawlers on a schedule, on demand or on an event. Workflows chain crawlers, jobs and triggers.
  • Handles ETL, ELT and streaming. Cataloged data can be queried from Athena, EMR and Redshift Spectrum.
  • Note from the docs: Glue for Ray has an end-of-support notice. Check it if you saw Ray in older material.

📖 What is AWS Glue? - features, Studio, related services

Azure Data Factory (and Fabric Data Factory)

  • A managed cloud service for hybrid ETL, ELT and data integration projects.
  • Building blocks: pipelines (groups of activities), activities (copy, transform, control), datasets, linked services (connection definitions), mapping data flows (Spark-backed visual transforms), integration runtimes and triggers.
  • Can hand transformation to Databricks, HDInsight or SQL. CI/CD through Azure DevOps and GitHub.
  • The docs tip says Data Factory in Microsoft Fabric is the next generation, and that new data-integration work should start there. Existing ADF workloads can be upgraded.

📖 Introduction to Azure Data Factory - concepts, Fabric note

Google Dataflow

  • Runs Apache Beam pipelines (Java, Python or Go SDKs). One programming model covers batch and streaming.
  • Fully managed: Dataflow allocates worker VMs per job and removes them when the job ends.
  • Prebuilt templates let you run common pipelines without writing Beam.
  • Streaming pipelines process data exactly-once by default; at-least-once mode can cut cost and latency if duplicates are acceptable.
  • Because Beam is portable, the same pipeline can run on another runner such as Flink or Spark.

📖 Dataflow overview - Beam, batch and streaming, templates

Managed Airflow: MWAA and Google's managed Airflow

Apache Airflow is an open-source tool to create, schedule and monitor workflows written in Python. Both clouds run it for you.

  • Amazon MWAA sets up Airflow when you pick a version, scales workers between limits you set, integrates with IAM for access and sends logs and metrics to CloudWatch. DAGs and supporting files live in an S3 bucket.
  • Google's managed Airflow (the Composer 3 docs now say "Managed Airflow (Gen 3)") is a fully managed Airflow service. Google runs a GKE cluster per environment for the scheduler, triggerers and workers, runs the Airflow web server UI and manages the metadata database. DAGs live in the environment's Cloud Storage bucket. Gen 3 supports Airflow 2 and 3.
  • This guide did not read a managed Airflow page for Azure. Data Factory pipelines cover much of the same ground; check Microsoft's docs for any Airflow offering.

📖 What is Amazon MWAA? - features, architecture, versions 📖 Managed Airflow (Gen 3) overview - what Google manages, DAG storage

dbt and Dagster

  • dbt turns SQL select statements into tables and views in your warehouse, handles run order, and adds tests and generated docs. It transforms data already in the warehouse; it does not extract or load. See Analytics engineering and dbt and the dbt project build.
  • Dagster is a data orchestrator. Its core unit is the asset; assets declare dependencies on each other, which gives lineage and ordering. It stresses built-in observability and testability.

📖 What is dbt? - models, tests, docs 📖 Dagster docs - assets, orchestration

How to choose

flowchart TB
    A[What is the job?] --> B{Move or reshape data at scale?}
    B -->|Yes| C{Which cloud?}
    C -->|AWS| D[Glue]
    C -->|Azure| E[Data Factory or Fabric Data Factory]
    C -->|Google, streaming heavy| F[Dataflow]
    B -->|SQL inside the warehouse| G[dbt]
    A --> H{Many dependent steps across tools?}
    H -->|Airflow skills, want managed| I[MWAA or Google managed Airflow]
    H -->|Asset-first, new build| J[Dagster]

The diagram splits "do the work" tools from "run the steps in order" tools. Most real stacks use one of each: for example Glue or Data Factory to land data, dbt to model it, and Airflow or Dagster to schedule both.

Rules of thumb:

  • A cloud-native service wins when you want few moving parts and your data is already in that cloud.
  • Airflow wins on talent and plugins; asset-based tools win on lineage. Both are fine.
  • Orchestrate things that already work. Do not rewrite a working job to fit the scheduler.