AWS Glue vs Azure Data Factory vs Google Cloud Dataflow
Modern data-driven applications require robust ETL (Extract, Transform, Load) and data integration pipelines. AWS, Azure, and Google Cloud each provide powerful serverless platforms for building, scheduling, and running data workflows:
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AWS Glue
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Azure Data Factory (ADF)
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Google Cloud Dataflow
This article offers an expert Level 500 comparison of architecture, capabilities, performance, use cases, and pricing.
Core Capabilities
| Feature | AWS Glue | Azure Data Factory | Google Cloud Dataflow |
|---|---|---|---|
| Primary Use Case | Serverless ETL, data prep, cataloging | Data movement & orchestration, ETL | Stream & batch data processing |
| Execution Model | Serverless Spark or Python jobs | Orchestrated pipelines with Data Flows | Apache Beam pipelines (batch & stream) |
| Visual Designer | Yes (AWS Glue Studio) | Yes (ADF Mapping Data Flows) | Yes (Dataflow SQL, Beam code) |
| Data Catalog | AWS Glue Data Catalog | Azure Data Catalog / Purview | BigQuery / Data Catalog |
Architecture & Scalability
| Feature | AWS Glue | Azure Data Factory | Google Cloud Dataflow |
|---|---|---|---|
| Serverless Compute Scaling | Automatic scaling of Spark/Python | Integration Runtime scales | Dynamic autoscaling (stream & batch) |
| Multi-region Support | Yes | Yes | Yes |
| Real-time Streaming Support | Limited (Glue Streaming ETL) | Via Synapse Pipelines or Spark | Native real-time processing |
| Integration with Data Lake | Deep S3 integration | Azure Data Lake Storage Gen2 | Google Cloud Storage |
Advanced Capabilities
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AWS Glue:
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Managed Spark ETL engine.
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Integrated Data Catalog with schema discovery.
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Serverless Python Shell Jobs.
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Glue Studio provides low-code ETL authoring.
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Tight integration with Athena, Redshift, and Lake Formation.
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Azure Data Factory:
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Extensive connector library (over 100+ sources).
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Mapping Data Flows for visual Spark transformations.
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Built-in pipeline orchestration and scheduling.
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SSIS integration runtime for legacy SQL Server ETL.
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Google Cloud Dataflow:
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Based on Apache Beam — unified batch & stream processing.
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Tight integration with BigQuery, Pub/Sub, Cloud Storage.
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Real-time data transformation and windowed aggregations.
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Auto-scaling, dynamic work rebalancing, highly optimized for cloud-native architectures.
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Real-world Scenario: Cross-platform Data Pipeline
A financial services firm needs to consolidate transactional data across multiple systems and load it into a data warehouse for analytics:
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AWS Glue: Extracts data from S3 and RDS, transforms it using Glue Spark Jobs, stores the results in Redshift and Athena.
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Azure Data Factory: Ingests data from on-prem SQL Server and SaaS APIs, applies transformations via Mapping Data Flows, loads into Synapse Analytics.
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Google Cloud Dataflow: Processes streaming transactions from Pub/Sub, applies complex windowed calculations, and stores results in BigQuery in near real-time.
Performance Metrics
| Metric | AWS Glue | Azure Data Factory | Google Cloud Dataflow |
|---|---|---|---|
| Batch Processing Latency | High (Spark startup overhead) | Low-medium (Integration Runtime) | Low (highly optimized Beam pipelines) |
| Streaming Processing Latency | ~1–2 mins (Glue Streaming ETL) | ~Low via Synapse + ADF pipelines | Sub-second in streaming mode |
| Scaling Behavior | Elastic scaling (Spark clusters) | Integration Runtime scales out | Fully elastic, autoscaling |
| Pipeline Orchestration | Basic (Jobs, Triggers) | Advanced (Control flow, Data Flows) | Advanced (Beam programming model) |
Costing Models
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AWS Glue:
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Pay per DPU-hour (Data Processing Unit).
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Separate charges for Glue Catalog storage and API calls.
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Azure Data Factory:
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Pay per pipeline activity run + Integration Runtime compute.
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Mapping Data Flows incur Spark cluster charges.
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Google Cloud Dataflow:
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Pay per vCPU-second, memory-second, and data processed.
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Pricing optimized for both batch and streaming workloads.
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Disclaimer
This article is independently developed and not affiliated with or endorsed by Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). All service names, prices, and descriptions are based on publicly available sources as of June 2025 and may change.
