AWS Glue: The Complete ETL and Data Integration Guide
AWS Glue has gotten complicated with all the job types, pricing models, and competing ETL approaches flying around. As someone who has built data pipelines on Glue for multiple production workloads — processing everything from small daily CSV imports to multi-terabyte data lake transformations — I learned everything there is to know about what works, what’s overrated, and where Glue genuinely shines. Today, I will share it all with you.
Writing Your First Glue Job
Let me walk you through a production-ready Glue job. This reads from S3 via the Data Catalog, applies transformations, and writes partitioned Parquet output. This pattern covers about 80% of real-world Glue use cases:
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Developer Professional (2025)
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import sys from awsglue.transforms import * from awsglue.utils import getResolvedOptions from pyspark.context import SparkContext from awsglue.context import GlueContext from awsglue.job import Job from awsglue.dynamicframe import DynamicFrame # Initialize Glue context args = getResolvedOptions(sys.argv, ['JOB_NAME']) sc = SparkContext() glueContext = GlueContext(sc) spark = glueContext.spark_session job = Job(glueContext) job.init(args['JOB_NAME'], args) # Read from Data Catalog source_dyf = glueContext.create_dynamic_frame.from_catalog( database="sales_db", table_name="raw_orders", transformation_ctx="source_dyf" ) # Apply transformations # 1. Drop null values cleaned_dyf = DropNullFields.apply( frame=source_dyf, transformation_ctx="cleaned_dyf" ) # 2. Map columns to new schema mapped_dyf = ApplyMapping.apply( frame=cleaned_dyf, mappings=[ ("order_id", "string", "order_id", "string"), ("customer_id", "string", "customer_id", "string"), ("amount", "double", "order_amount", "decimal"), ("order_date", "string", "order_date", "date") ], transformation_ctx="mapped_dyf" ) # 3. Convert to Spark DataFrame for complex transformations df = mapped_dyf.toDF() df = df.filter(df.order_amount > 0) df = df.withColumn("year", year(df.order_date)) df = df.withColumn("month", month(df.order_date)) # Convert back to DynamicFrame output_dyf = DynamicFrame.fromDF(df, glueContext, "output_dyf") # Write to S3 as partitioned Parquet glueContext.write_dynamic_frame.from_options( frame=output_dyf, connection_type="s3", connection_options={ "path": "s3://my-bucket/processed/orders/", "partitionKeys": ["year", "month"] }, format="parquet", transformation_ctx="output" ) job.commit()A few things to note about this code. The transformation_ctx parameter is crucial — it enables job bookmarks (incremental processing), which we’ll cover next. The DynamicFrame is Glue’s extension of Spark DataFrames that handles schema inconsistencies more gracefully. And partitioning the output by year and month dramatically improves query performance when downstream consumers (Athena, Redshift Spectrum) filter by date.
Glue Crawlers: Automated Schema Discovery
Crawlers scan your data sources and automatically infer table schemas. They’re incredibly useful when you’re ingesting data from sources where the schema might change over time or when you’re cataloging existing data in S3 that doesn’t have explicit schema definitions.
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Developer Professional (2025)
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resource "aws_glue_crawler" "orders_crawler" { database_name = aws_glue_catalog_database.sales_db.name name = "orders-crawler" role = aws_iam_role.glue_role.arn s3_target { path = "s3://my-bucket/raw/orders/" } schema_change_policy { delete_behavior = "LOG" update_behavior = "UPDATE_IN_DATABASE" } schedule = "cron(0 6 * * ? *)" # Run daily at 6 AM configuration = jsonencode({ Version = 1.0 Grouping = { TableGroupingPolicy = "CombineCompatibleSchemas" } }) }I should mention some crawler gotchas I’ve hit in production. Crawlers can sometimes create too many tables if your S3 key structure is irregular — the CombineCompatibleSchemas grouping policy helps with this. Schedule crawlers to run before your ETL jobs, not after. And be careful with the delete_behavior setting: LOG is safest for production because it doesn’t remove table definitions when underlying data is temporarily unavailable.
Glue Data Quality
Glue Data Quality (DQDL) is a relatively new feature that lets you define and enforce data quality rules directly in your Glue workflows. Before DQDL, I was writing custom validation logic in every ETL job, which was tedious and inconsistent across pipelines.
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Developer Professional (2025)
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# DQDL Ruleset Example Rules = [ ColumnExists "order_id", ColumnExists "customer_id", IsComplete "order_id", IsUnique "order_id", ColumnValues "order_amount" > 0, ColumnValues "order_date" between "2020-01-01" and "2025-12-31", Completeness "customer_id" >= 0.95 # 95% must have values ]The completeness check is particularly useful — requiring 95% of customer_id values to be non-null catches data quality degradation before it affects downstream systems. I integrate DQDL rules into my Glue workflows so that jobs fail fast when data quality drops below acceptable thresholds, preventing bad data from propagating through the pipeline.
Performance Optimization
1. Choose the Right Worker Type
Glue worker selection has a significant impact on both performance and cost. Bigger isn’t always better — I’ve seen teams default to G.2X workers for every job when Standard would have been perfectly adequate and half the price.
Worker Type vCPU Memory Use Case Standard 4 16 GB General workloads G.1X 4 16 GB Memory-intensive, 1 executor/worker G.2X 8 32 GB Large datasets, complex joins G.4X 16 64 GB ML workloads, huge datasets G.8X 32 128 GB Extreme memory requirements 2. Enable Job Bookmarks
Job bookmarks track what data has already been processed, so subsequent job runs only handle new data. This is essential for incremental ETL patterns and dramatically reduces processing time and cost for recurring jobs.
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Developer Professional (2025)
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# Enable bookmarks in job config job.init(args['JOB_NAME'], args) # Read with bookmark support source_dyf = glueContext.create_dynamic_frame.from_catalog( database="mydb", table_name="orders", transformation_ctx="source", # Required for bookmarks! additional_options={ "jobBookmarkKeys": ["order_date"], "jobBookmarkKeysSortOrder": "asc" } )3. Pushdown Predicates
Pushdown predicates filter data at the source level, reducing the amount of data Glue needs to read into memory. If your data is partitioned in S3, pushdown predicates can reduce data scanned by orders of magnitude.
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AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. Unlike traditional ETL tools that require infrastructure management, Glue handles the heavy lifting—you focus on your data transformations.
Developer Professional (2025)
-
source_dyf = glueContext.create_dynamic_frame.from_catalog( database="mydb", table_name="orders", push_down_predicate="year=2024 and month=12" # Only read this partition )The performance difference is dramatic. Without pushdown predicates, a job reading a year’s worth of partitioned data might scan 365 partitions. With the predicate above, it reads only one. I’ve seen job runtimes drop from 45 minutes to under 3 minutes just by adding appropriate pushdown predicates.
Glue Workflows: Orchestrating ETL Pipelines
For pipelines with multiple dependent jobs, Glue Workflows provide built-in orchestration. A workflow chains crawlers, jobs, and triggers together with dependency management.
That said, for complex orchestration requirements, I often recommend Step Functions over Glue Workflows. Step Functions provides more flexible error handling, branching logic, and integration with non-Glue services. Glue Workflows are simpler but less powerful — they work well for linear pipelines but struggle with complex DAG patterns.
Cost Optimization
- Use Auto Scaling: Let Glue automatically adjust the number of workers based on workload characteristics rather than over-provisioning for peak
- Optimize DPU Hours: More workers don’t always mean faster jobs due to diminishing returns and shuffle overhead
- Enable Flex Execution: Lower-cost option for non-urgent workloads that can tolerate potential delays in execution start
- Monitor with CloudWatch: Track DPU utilization metrics to right-size workers and detect waste
Glue vs. Alternatives
Choosing between Glue, EMR, and Athena depends on your specific use case. Here’s how I think about the decision:
Feature AWS Glue EMR Athena Serverless ✅ Yes ❌ No (Serverless EMR exists) ✅ Yes ETL Focus ✅ Primary use case ⚡ Flexible ❌ Query only Data Catalog ✅ Built-in ⚡ Uses Glue Catalog ⚡ Uses Glue Catalog Best For Batch ETL, Data prep Complex Spark/Hadoop Ad-hoc queries My rule of thumb: use Glue for standard batch ETL workloads, EMR when you need full control over your Spark/Hadoop cluster, and Athena for ad-hoc queries and light transformations. Most organizations end up using all three in combination — Glue for data preparation, Athena for exploration, and EMR for specialized processing.
Further Reading
Pro Tip: Start with Glue Studio for visual pipeline building, then export the generated code to customize. This gives you the best of both worlds — quick prototyping with full code control when you need to optimize performance or add complex logic.
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AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. Unlike traditional ETL tools that require infrastructure management, Glue handles the heavy lifting—you focus on your data transformations.
Developer Professional (2025)
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