Compare data processing and streaming services across AWS, Azure, and GCP including ETL pipelines, real-time streaming, pricing, performance, and use cases.
Recommendation: AWS is recommended for enterprises needing a comprehensive data processing ecosystem with centralized metadata management via Glue Data Catalog and the broadest service integration. Azure excels for hybrid data integration scenarios with Data Factory's 90+ connectors and organizations migrating existing Kafka workloads to Event Hubs. GCP is ideal for teams wanting a unified batch and stream processing model through Apache Beam on Dataflow, with the strongest exactly-once processing guarantees and tight BigQuery integration.
| Category | AWS | Azure | GCP |
|---|---|---|---|
| ETL/ELT Engine Core engine for extract, transform, and load operations | AWS Glue (serverless Spark, Python Shell, Ray) | Azure Data Factory (Mapping Data Flows on managed Spark) | Cloud Dataflow (Apache Beam, unified batch and stream) |
| Streaming Platform Real-time event and data streaming capabilities | Kinesis Data Streams, Kinesis Data Firehose | Azure Event Hubs (up to millions of events/sec) | Cloud Pub/Sub (global, serverless messaging) |
| Unified Batch & Stream Single programming model for both batch and stream processing | Separate services for batch (Glue) and stream (Kinesis) | Synapse Analytics provides unified workspace; separate engines | Dataflow provides true unified model via Apache Beam SDK |
| Visual Pipeline Authoring Code-free visual interface for building data pipelines | Glue Studio visual editor | Data Factory visual pipeline designer with 90+ connectors | Cloud Data Fusion (CDAP-based visual interface) |
| Built-in Connectors Pre-built connectors to data sources and sinks | 30+ native connectors via Glue, custom connectors supported | 90+ built-in connectors including on-premises sources | 40+ connectors via Data Fusion; Beam I/O connectors |
| Kafka Compatibility Native support for Apache Kafka protocol | Amazon MSK (Managed Streaming for Apache Kafka) | Event Hubs native Kafka endpoint (no code changes needed) | Managed Service for Apache Kafka, Pub/Sub Kafka bridge |
| Managed Spark Managed Apache Spark for large-scale data processing | Amazon EMR, Glue Spark jobs | Azure Databricks, Synapse Spark pools | Cloud Dataproc (managed Hadoop/Spark with autoscaling) |
| Workflow Orchestration Pipeline scheduling and dependency management | AWS Step Functions, Amazon MWAA (Managed Airflow) | Data Factory pipelines, Azure Logic Apps | Cloud Composer (managed Apache Airflow) |
| Autoscaling Automatic scaling of processing resources based on workload | Glue auto-scales DPUs; Kinesis requires manual shard splitting | Data Factory auto-scales Mapping Data Flows; Event Hubs auto-inflate | Dataflow autoscales workers and rebalances work dynamically |
| Exactly-Once Processing Guarantee that each record is processed exactly once | Kinesis supports deduplication; Glue job bookmarks track state | Event Hubs supports checkpointing; Stream Analytics guarantees at-least-once | Dataflow guarantees exactly-once processing for streaming pipelines |
Pricing: Glue: $0.44/DPU-hour. Kinesis Data Streams: $0.015/shard-hour plus $0.014 per million PUT payloads. Kinesis Firehose: $0.029 per GB ingested. EMR: EC2 cost plus $0.015-$0.270/hr per instance depending on type.
Performance: Glue Spark jobs process petabytes of data with serverless scaling. Kinesis Data Streams handles up to 1 MB/sec per shard with sub-second latency. Firehose buffers and delivers data with configurable intervals from 60 seconds.
Pricing: Data Factory: $0.25 per activity run, $0.25/DIU-hour for data movement, $0.84/vCore-hour for Data Flows. Event Hubs: $0.028/throughput unit/hr (Standard), $0.03 per million events. Stream Analytics: $0.11/streaming unit/hr.
Performance: Data Factory Mapping Data Flows process data on auto-scaled Spark clusters. Event Hubs ingests millions of events per second with sub-second latency. Stream Analytics processes complex event patterns with SQL-based queries in real time.
Pricing: Dataflow: $0.056/vCPU-hour, $0.003557/GB-hour (batch); $0.069/vCPU-hour, $0.004394/GB-hour (streaming). Pub/Sub: $40/TiB ingested and delivered. Data Fusion: $1.80/hr (Basic), $4.20/hr (Enterprise).
Performance: Dataflow dynamically rebalances work across workers for optimal throughput. Pub/Sub delivers messages globally with median latency under 100ms. Dataflow streaming pipelines achieve exactly-once semantics with sub-second processing latency.
AWS provides a comprehensive data processing ecosystem with Glue for serverless ETL and Kinesis for real-time streaming at any scale.
AWS Glue is a fully managed, serverless ETL service that discovers, prepares, and transforms data for analytics. The Glue Data Catalog provides a centralized metadata repository, and Glue Studio offers a visual interface for authoring ETL jobs in Python or Spark.
Amazon Kinesis enables real-time data streaming with four capabilities: Kinesis Data Streams for custom stream processing, Kinesis Data Firehose for loading streams into data stores, Kinesis Data Analytics for SQL and Apache Flink processing, and Kinesis Video Streams for video ingestion.
AWS Step Functions orchestrates complex data pipelines. Amazon EMR provides managed Hadoop and Spark clusters. AWS Lake Formation simplifies building and managing data lakes with fine-grained access control.