AWS Glue is a powerful data engineering platform when designed, tuned, and governed correctly.But We are treating it as a simple ETL utility often leads to cost, performance, and reliability issues.
As a Data Friend we need to have solid understanding on the AWS Glue
Myth 1: AWS Glue is only for simple ETL
Reality:
AWS Glue supports complex transformations including joins, aggregations, schema evolution, incremental processing, and large-scale distributed processing using Apache Spark. It is suitable for enterprise-grade data engineering workloads.
Myth 2: AWS Glue is serverless, so performance tuning is not required
Reality:
While infrastructure management is serverless, Glue jobs still require tuning
- Worker types (G.1X, G.2X, G.4X)
- Number of DPUs
- Spark configurations
- Partitioning and data layout
- Poor tuning leads to high cost and slow execution.
Myth 3: AWS Glue works only with Amazon S3
Reality:
AWS Glue integrates with multiple data sources
- Amazon RDS and Aurora
- Amazon Redshift
- DynamoDB
- JDBC sources (Oracle, SQL Server, MySQL, PostgreSQL)
- Streaming sources such as Kafka and Kinesis
Myth 4: AWS Glue is very expensive
Reality:
Glue becomes expensive mainly due to design issues
- Over-provisioned DPUs
- Full data reloads instead of incremental loads
- Missing job bookmarks
With optimized design, Glue is often more cost-effective than always-on Spark clusters.
Myth 5: Glue Crawlers automatically handle schema management
Reality:
Crawlers may
- Create excessive tables
- Misinterpret schema changes
- Perform poorly with nested or semi-structured data
Production systems typically require controlled schema management and governance.
Myth 6: AWS Glue replaces data warehouses
Reality:
AWS Glue is a data integration and transformation service. It complements data warehouses by preparing and transforming data before loading into analytics platforms.
Myth 7: Glue jobs are difficult to debug
Reality:
Glue supports debugging through
- Amazon CloudWatch logs
- Spark UI
- Job bookmarks
- Glue Studio monitoring
Most challenges arise from limited Spark expertise rather than Glue itself.
Myth 8: AWS Glue supports only batch processing
Reality:
AWS Glue also supports
- Streaming ETL
- Near real-time pipelines
- Event-driven processing
It is not limited to scheduled batch workloads.
Myth 9: AWS Glue is a set-and-forget service
Reality:
Production Glue pipelines require
- Cost and performance monitoring
- Schema change handling
- Failure alerts and retries
- Version control and CI/CD
Glue jobs should be treated as production-grade software.
Myth 10: AWS Glue is only for data engineers
Reality:
With Glue Studio, SQL-based transformations, and visual workflows, Glue can be effectively used by analytics teams, architects, and platform teams.
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