AWS Glue Tutorial

Overview

AWS Glue is a serverless data integration service for discovering, preparing, and transforming data for analytics, machine learning, and application development.

  • Build ETL pipelines using Python or Spark
  • Centralize metadata with Glue Data Catalog
  • Automate schema discovery using Glue Crawlers
  • Orchestrate multi-step jobs with Glue Workflows
  • Design visually with Glue Studio
Hands-on: Build ETL pipelines, transform data, catalog schemas.

Core Components

  • Jobs: Serverless Spark or Python shell workloads
  • Data Catalog: Databases, tables, partitions, and schema versions
  • Crawlers: Scan data sources and infer schemas
  • Workflows: Coordinate jobs and triggers
  • Studio: Visual authoring and monitoring
  • Data Quality: Rules, profiles, and metrics for datasets
  • Schema Registry: Manage schemas for streaming platforms like Kafka

Glue ETL Jobs

Job Types

  • Spark: Distributed ETL with DynamicFrame and DataFrame APIs
  • Python Shell: Lightweight scripts for small tasks
  • Ray: Parallel compute for ML/feature engineering

Key Concepts

  • DynamicFrame: Schema-flexible abstraction with transforms
  • Bookmarks: Track processed data for incremental loads
  • Connections: VPC, JDBC, and marketplace connectors
  • Job Parameters: Runtime configuration and environment variables
  • Formats: Parquet, ORC, JSON, CSV, Avro, Iceberg

Sample Spark Job

from awsglue.context import GlueContext
from awsglue.job import Job
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext

args = getResolvedOptions(sys.argv, ["JOB_NAME"])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args["JOB_NAME"], args)

source = glueContext.create_dynamic_frame.from_options(
    connection_type="s3",
    connection_options={"paths": ["s3://my-bucket/raw/events/"]},
    format="json"
)
transformed = source.rename_field("userId", "user_id")
df = transformed.toDF()
df = df.filter(df["event_type"] != "test")

glueContext.write_dynamic_frame.from_options(
    frame=glueContext.create_dynamic_frame.from_df(df, glueContext, "df"),
    connection_type="s3",
    connection_options={"path": "s3://my-bucket/curated/events/"},
    format="parquet"
)
job.commit()

Glue Data Catalog

  • Persistent metadata store for databases, tables, and partitions
  • Backed by Hive Metastore compatible APIs
  • Integrates with Athena, EMR, Redshift Spectrum, and Lake Formation
  • Supports schema versions and column-level metadata
Entity Description Common Use
Database Logical grouping of tables Organize by domain or environment
Table Schema definition for datasets Query via Athena and ETL jobs
Partition Subsets of data by keys Improve query performance and cost

Glue Crawlers

  • Automatically discover datasets in sources like S3, JDBC, DynamoDB
  • Infer schema, data types, partitions, and update the Data Catalog
  • Schedule for continuous updates; supports classifiers for custom formats

Glue Workflows

  • Define orchestrated pipelines of crawlers, triggers, and jobs
  • Support conditional branching and dependencies
  • Track runs, status, and lineage

Glue Studio

  • Visual job authoring with drag-and-drop nodes
  • Monitors runs, logs, and metrics
  • Supports data previews and autogenerated code

Streaming ETL

  • Process streaming data from Kinesis Data Streams and Kafka
  • Stateful aggregations and windowing with Spark Structured Streaming
  • Write outputs to S3, Redshift, or other sinks

Data Quality

  • Define rules to validate datasets
  • Generate profiles to understand data distributions
  • Produce metrics and alerts for pipeline health

Schema Registry

  • Central registry for streaming schemas (Kafka producers/consumers)
  • Supports schema evolution and compatibility checks
  • Integrates with Glue Data Catalog and ETL jobs

Best Practices

  • Use Parquet with partitioning for query efficiency
  • Optimize Spark with pushdown, coalesce, and repartition strategies
  • Prefer Bookmarks for incremental loads
  • Separate raw, staged, and curated S3 zones
  • Store scripts in S3 and version with Git

Security & Governance

  • Use Lake Formation for fine-grained table and column permissions
  • IAM roles for jobs, crawlers, and Catalog access
  • KMS encryption for data at rest; TLS for data in transit
  • VPC endpoints and private subnets for secure connectivity
  • Enable logging to CloudWatch and S3

Hands-On Workflow

  1. Plan
    • Define source and target formats
    • Identify partitioning keys
    • Choose job type (Spark, Python Shell, Ray)
  2. Create Catalog
    • Create database and run Crawler on S3
    • Verify table schema and partitions
  3. Develop ETL
    • Author in Glue Studio or script in S3
    • Use DynamicFrame transforms
    • Write to curated S3 in Parquet
  4. Orchestrate
    • Create Workflow with crawler → job → quality checks
    • Schedule with triggers
  5. Validate
    • Run Data Quality rules
    • Query with Athena or load to Redshift
  6. Monitor
    • CloudWatch metrics and logs
    • Optimize job parameters and parallelism

Pricing & Limits

  • Jobs billed per Data Processing Unit hour
  • Data Catalog charges per request and storage beyond free tier
  • Crawler runtime billed per DPU hour
  • Data Quality and Schema Registry incur additional usage-based costs
Optimization: Use Parquet, partitioning, and bookmarks to reduce runtime and cost.

Summary

  1. Glue ETL Jobs with Python/Spark for scalable transformations
  2. Glue Data Catalog centralizes metadata for analytics
  3. Glue Crawlers automate schema discovery
  4. Glue Workflows orchestrate pipelines
  5. Glue Studio simplifies visual authoring
  6. Data Quality and Schema Registry improve reliability

Last Updated: January 2025