comparison of AWS Glue vs Running Spark jobs on EMR using spark-submit from a data engineering perspective:
1️⃣ AWS Glue
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Type: Serverless ETL service (managed Spark)
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When to Use: Lightweight to medium ETL/ELT workloads, event-driven or scheduled jobs.
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Pros:
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Fully managed, no cluster management.
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Auto-scaling and pay-per-use.
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Built-in crawler, schema inference, and Data Catalog integration.
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Native connectors to S3, RDS, Redshift, DynamoDB.
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Cons:
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Limited cluster customization.
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Startup latency (1–2 min warmup).
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Less control over Spark version tuning.
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Example:
ETL pipelines that transform S3 data to Redshift or Iceberg tables on S3.
2️⃣ Amazon EMR with spark-submit
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Type: Managed Hadoop/Spark cluster service (full control).
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When to Use: Heavy processing, streaming jobs, or when you need fine-grained control.
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Pros:
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Full control over cluster configuration (memory, cores, Spark version).
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Supports complex, long-running, streaming, and ML pipelines.
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Can integrate with S3 (EMRFS), HDFS, Iceberg, Delta Lake.
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Cons:
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You manage cluster lifecycle (start/stop or auto-terminate).
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Higher ops overhead and cost if not managed well.
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Example:
Petabyte-scale ETL, Spark Streaming with Kafka, or ML pipelines needing custom Spark configs.
💡 Rule of Thumb:
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Glue → Simpler, serverless ETL on S3/Redshift/Iceberg.
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EMR → Complex, large-scale, or streaming workloads needing control and custom tuning.
