AWS Glue

From my perspective as a Principal Cloud Infrastructure Analyst, AWS Glue is a strong and exceptional fit for managed ETL in our Data Mesh context, but it requires disciplined engineering around governance, cost control, and operational runbooks. Reliability has been good when we enforce network connection standards, IAM role standards, and repeatable aggregation/deployment patterns. Where complexity shows up is not in basic ETL, but in scale behavior: schema drift, crawler edge cases, cross-account catalog sync, and differences between small dev datasets and real production distributions. Reliability & Uptime: Glue jobs and notebooks have been stable for batch pipelines (Landing Parquet to Raw Iceberg), especially after standardizing session/job settings, VPC connections, and naming conventions. I also found that explicitly using Glue 5.0 for Lake Formation fine-grained access reduced friction compared to Glue 4.0-era workarounds. Support Quality: Support has been effective when issues are concrete (permissions, crawler configurations, Iceberg parameters, job retries), but root-cause analysis still depends heavily on in-house expertise and structured troubleshooting. The turn-around in these scenarios, or especially when the solution requires a feature enhancement, can be a blocker and something to consider. Integration Success: I have seen Glue integrations work well with Lake Formation, Athena, Data Catalog/Data Mesh patterns, and Snowflake interoperability designs. In practice, data integration quality improves when ownership boundaries are clear (single writer principle, explicit account/environment segregation, and defined IAM trust patterns) Business Impact: Glue has allowed us to move faster on governed data ingestion, data modeling and data transformation, while maintaining security guardrails and metadata consistency as first-class concerns. Scenario 1: I worked through Glue 4.0/earlier limitations where column-level filtering was problematic for some Lake Formation-tagged datasets, and I used Athena-based workarounds (direct queries/JDBC) where needed. Moving to Glue 5.0 with proper session configurations and Lake Formation fine-grained settings materially simplified operations and reduced workaround overhead. Scenario 2: I dealt with crawler quality issues in Data Mesh onboarding (table over-consolidation, schema migration, partition/schema inconsistencies, and KMS-key-related read problems). We have improved reliability by tightening the S3 layout conventions, exclusion patterns, crawl scope, and schema update controls, plus targeted re-crawls and manual DDL fallback where necessary.