Comparison

Etlworks vs AWS Glue

AWS Glue is the natural choice if you live entirely inside AWS. Etlworks gives you the same data integration capabilities across multi-cloud, on-prem, and hybrid — with visual flows instead of PySpark.

The verdict

When each tool fits.

When Etlworks fits better

You operate in multi-cloud or hybrid environments
You want predictable monthly pricing, not pay-per-DPU
You need on-prem data integration alongside cloud
Your team prefers visual configuration over PySpark code
You want a Gen AI agent built into the platform, not bolted on via separate cloud services

Where they’re equal

AWS-native data sources (S3, RDS, Redshift, Aurora)
Schema discovery and crawling
Serverless execution
Compliance with AWS-aligned standards
Pay-as-you-use pricing model (different shape, similar structure)

When AWS Glue fits better

You're 100% on AWS with no plans to move
You have a large team comfortable writing PySpark
You need deep integration with other AWS services (Lake Formation, Athena)
You want serverless billing for sporadic workloads
You prefer Apache Spark as your compute engine

Feature breakdown

Side by side.

Capability	Etlworks	AWS Glue
Pricing & commercial
Starting price (monthly)	$300	Pay per DPU-hour (~$0.44/DPU-hr)
Pricing model	Fixed per tier	Consumption (DPU-hours)
Integration scope
Sources	260+	AWS-centric + JDBC
ETL capabilities	ETL, ELT, Reverse ETL, wildcard processing	Spark-based ETL
API management	Full
On-prem deployment
CDC & Streaming
CDC engine	Debezium-compatible, built-in (no Kafka required)	AWS DMS (separate service, often paired)
Database CDC sources	MySQL, Postgres, SQL Server, Oracle, MongoDB, DB2, others	Via DMS — broad coverage
Streaming queues	Kafka, EventHubs, Kinesis, SQS, PubSub, ActiveMQ, RabbitMQ	Kinesis, MSK (Kafka)
IoT brokers	MQTT brokers
Real-time replication	Log-based CDC, full, incremental	Streaming jobs (Spark Streaming)
Change tracking modes	Log-based, trigger-based, timestamp/high-watermark	Log-based via DMS
Gen AI
AI agent	Built-in agent (Simba) — builds and edits flows from chat	use Bedrock externally
Agent capabilities	Reads metadata, reads/samples data, writes JS & SQL, schedules, deploys, monitors	Code generation suggestions in Glue Studio
Natural-language flow building	‘Vibe-build’ — create flows by describing what you want	Partial — Q in Glue (preview, AWS-context only)
AI-driven mapping	Auto-suggests source-to-destination mappings	Partial — schema discovery via crawlers
Built-in analytics	Agent runs analysis on flow data and pipeline behavior
Chat across product	Same agent context on every screen
CLI for agent	Full CLI access for run/deploy/monitor/manage
Trains on customer data	Never	Not by default