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 want orchestration in one tool, not Glue plus EventBridge plus Step Functions
  • 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
  • Event-driven triggers and scheduled jobs
  • 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)$300Pay per DPU-hour (~$0.44/DPU-hr)
Pricing modelFixed per tierConsumption (DPU-hours)
Integration scope
Sources260+AWS-centric + JDBC
ETL capabilitiesETL, ELT, Reverse ETL, wildcard processingSpark-based ETL
API managementFull
On-prem deployment
Transformations
Visual mappingdrag-and-drop designer with live previewGlue Studio visual editor
Scripting languagesSQL, JavaScript, Python, XSLT, shellPySpark, Scala, Python
Nested and hierarchical dataJSON, XML, Avro, Parquet — read, write, normalize, flatten by draggingPartial — passes JSON through, unpacking happens downstream
Warehouse pushdown (ELT)transform before load or in the warehouse, same enginePartial — Spark, or push into Redshift
Reusable logicmacros, templates, and 3,900+ prebuilt flow templatesjobs and shared libraries
Lookups and enrichmentLookup Builder for cross-source lookups
Data validationvalidation rules with per-step error handling
Orchestration & workflow
SchedulingCron expressions and fixed intervals, with per-schedule parametersCron via Glue triggers or EventBridge
Event-driven triggersHTTP listeners and webhooks, message queues, file and email eventsEventBridge and S3 events
Continuous executionlooping schedules for CDC and queue consumersPartial — streaming ETL jobs
Visual workflow builderComposer canvas, 200+ flow typesGlue Studio and workflows
Nested workflowsnested flows with conditional and looped stepsPartial — workflows and triggers, Step Functions for real branching
Run external toolsshell and SSH scripts, CLI, JavaScript, Python, SQL, HTTP callsPartial — Python shell jobs; other AWS services via Step Functions
Parallel executionoverlapping schedules run as independent, separately cancellable instancesparallel jobs and DPUs
Retries and error handlingper-step exception handling with notificationsjob retries, DLQ patterns
Run monitoringper-schedule status, run history, automatic Flow Findings reportsCloudWatch metrics and logs
CDC & Streaming
CDC engineDebezium-compatible, built-in (no Kafka required)AWS DMS (separate service, often paired)
Database CDC sourcesMySQL, Postgres, SQL Server, Oracle, MongoDB, DB2, othersVia DMS — broad coverage
Streaming queuesKafka, EventHubs, Kinesis, SQS, PubSub, ActiveMQ, RabbitMQKinesis, MSK (Kafka)
IoT brokersMQTT brokers
Real-time replicationLog-based CDC, full, incrementalStreaming jobs (Spark Streaming)
Change tracking modesLog-based, trigger-based, timestamp/high-watermarkLog-based via DMS
Developer experience
REST APIfull API for flows, connections, schedules, and runscloud API
CLIfull CLI with built-in SQLcloud CLI
MCP serverbuilt-in — connect Cursor, Claude, or ChatGPT to your instance
Client librariesPython, Bash, and PowerShell clientscloud SDKs in many languages
Version controlbuilt-in — automatic history, diff, and revert on every artifactPartial — infrastructure-as-code, not artifact history
Embeddable / white-label
Compliance & security
SOC 2 Type 2audited; report under NDA, SOC 3 publicplus ISO 27001, PCI, FedRAMP
HIPAAsupported with a BAAcovered by the cloud BAA
GDPR / DPAcompliant, DPA available
SSO and MFASAML SSO, optional 2FA, JWT stateless authcloud IAM and directory integration
Role and artifact-level accesssix roles plus tag-based scoping of flows, connections, and schedulesfine-grained IAM policies
Encryption and data handlingTLS in transit, encrypted at rest, customer-managed PGP, SSH tunnels, IP allowlisting; rows are not persisted by defaultplatform KMS, customer-managed keys
Audit loggingadmin actions logged, access logs monitoredcloud-native audit trail
Security testingmonthly vulnerability and penetration scans, static analysis blocking every build, periodic third-party auditscontinuous, platform-wide
Gen AI
AI agentBuilt-in agent (Simba) — builds and edits flows from chatuse Bedrock externally
Agent capabilitiesReads metadata, reads/samples data, writes JS & SQL, schedules, deploys, monitorsCode generation suggestions in Glue Studio
Natural-language flow building‘Vibe-build’ — create flows by describing what you wantPartial — Q in Glue (preview, AWS-context only)
AI-driven mappingAuto-suggests source-to-destination mappingsPartial — schema discovery via crawlers
Built-in analyticsAgent runs analysis on flow data and pipeline behavior
Chat across productSame agent context on every screen
CLI for agentFull CLI access for run/deploy/monitor/manage
Trains on customer dataNeverNot by default