AWS Glue: The Complete ETL and Data Integration Guide
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.
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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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The Data Catalog is Glue’s central metadata repository. Think of it as a Hive metastore on steroids—it stores table definitions, schemas, and partition information for all your data sources.
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Component Description Use Case Databases Logical containers for tables Organize tables by domain (sales_db, marketing_db) Tables Schema definitions pointing to data locations Define structure for S3 data, JDBC sources Crawlers Automated schema discovery Scan S3/databases and infer schemas Connections Network and credential configs Connect to RDS, Redshift, on-prem databases 2. Glue ETL Jobs
ETL jobs are where the transformation magic happens. Glue supports three job types:
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ETL jobs are where the transformation magic happens. Glue supports three job types:
Developer Professional (2025)
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A visual interface for building ETL pipelines without writing code. Drag-and-drop transformations that generate PySpark code behind the scenes.
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Here’s a production-ready Glue job that reads from S3, transforms data, and writes to Parquet:
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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()Glue Crawlers: Automated Schema Discovery
Crawlers scan your data sources and automatically infer schemas. Here’s how to set one up with Terraform:
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Crawlers scan your data sources and automatically infer schemas. Here’s how to set one up with Terraform:
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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" } }) }Glue Data Quality
Glue Data Quality (DQDL) lets you define and enforce data quality rules:
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Glue Data Quality (DQDL) lets you define and enforce data quality rules:
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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 ]Performance Optimization
1. Choose the Right Worker Type
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 processed data to avoid reprocessing:
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Job bookmarks track processed data to avoid reprocessing:
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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
Filter data at the source level to reduce data scanned:
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