AWS Glue is always Powerful as Data Engineer Trust ?

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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