AWS Glue ETL
When to Use
- Building serverless ETL pipelines on AWS without managing infrastructure
- Deciding between AWS Glue and Amazon EMR for a transformation workload
- Designing Glue Data Catalog schema and crawler strategies
- Using Glue Studio (visual ETL) or Glue Notebooks for data preparation
- Implementing incremental data processing with job bookmarks
- Preparing for AWS Certified Data Engineer Associate (DEA-C01) exam
Core Jobs
1. Glue vs EMR Decision
| Factor | Choose AWS Glue | Choose Amazon EMR |
|---|
| Management | Fully serverless, no cluster to manage | Cluster management required (or EMR Serverless) |
| Runtime | Apache Spark (managed), Python shell | Spark, Hadoop, Hive, Presto, Flink — full ecosystem |
| Existing code | Greenfield ETL | Migrating existing Spark/Hadoop jobs |
| Data Catalog | Built-in (Hive metastore compatible) | Uses Glue Data Catalog or own Hive metastore |
| Streaming | Glue Streaming (micro-batch) | Spark Streaming, Flink (true streaming) |
| Cost model | DPU-hours billed per job run | Cluster uptime (can be costly for short jobs) |
| Best for | Simple-to-moderate ETL, scheduled batch | Complex Spark workloads, custom libraries, ML |
Decision rule: Default to Glue for new ETL workloads. Choose EMR when you need custom Spark configurations, non-Spark runtimes, or are migrating existing cluster-based jobs.
2. DynamicFrame vs DataFrame
| Aspect | DynamicFrame | DataFrame |
|---|
| Schema | Flexible — handles inconsistent/nested schemas automatically | Requires clean, defined schema |
| Origin | Glue-native abstraction | Apache Spark native |
| Operations | Glue-specific transforms (ApplyMapping, ResolveChoice, DropNullFields) | Full Spark SQL API |
| Convert | df = dynamic_frame.toDF() | dyf = DynamicFrame.fromDF(df, glue_ctx, "name") |
| Best for | Reading messy source data with mixed types | Complex aggregations, window functions, joins |
Rule: Use DynamicFrame for reading from sources with schema inconsistencies. Convert to DataFrame for complex Spark operations, then convert back to DynamicFrame for writing.
3. Glue Data Catalog
- Central metadata repository — Hive metastore compatible
- Shared by Athena, Redshift Spectrum, EMR, Lake Formation
- Structure: Data Catalog → Databases → Tables → Partitions
- Tables can point to S3, RDS, DynamoDB, JDBC sources
- Schema versions tracked automatically; schema evolution supported
- Used as the metadata layer for AWS Lake Formation governance
4. Job Bookmarks (Incremental Processing)
- Tracks which data has already been processed in previous job runs
- Prevents reprocessing of old data in incremental loads from S3, JDBC, DynamoDB
- Configuration:
--job-bookmark-option job-bookmark-enable — enable bookmarks--job-bookmark-option job-bookmark-disable — full refresh (no bookmark)--job-bookmark-option job-bookmark-pause — pause without resetting
- Works by tracking S3 object metadata (ETags, last modified) and file offsets
- Reset bookmark via Glue console or
reset_job_bookmark API call
5. Glue Crawlers
- Auto-discover schema from S3, JDBC, DynamoDB, and other sources
- Creates/updates tables in the Glue Data Catalog
- Detect new partitions and schema changes on each run
- Classifier hierarchy: built-in (JSON, CSV, Parquet, ORC) → custom classifiers
- Schedule: on-demand, cron-based, or triggered via EventBridge
- Best practice: schedule crawlers AFTER data arrives, not continuously
6. Glue Workflows and Orchestration
- Orchestrate multi-step ETL: crawlers → triggers → jobs → triggers → jobs
- Trigger types:
- Scheduled — cron expression
- On-demand — manual or API
- Conditional — fires when specified jobs/crawlers succeed, fail, or complete
- Use EventBridge for more complex event-driven orchestration (S3 upload → Glue job)
- For complex ML/data pipelines: Step Functions + Glue > Glue Workflows alone
7. Glue Streaming (Continuous ETL)
- Reads from Kinesis Data Streams or Apache Kafka (MSK) in micro-batches
- Window sizes: 100 seconds minimum; not true real-time like Kinesis Analytics
- Use when: near-real-time ETL to S3/Redshift, applying transformations to streaming data
- Glue Streaming vs Kinesis Data Analytics: Glue = batch-oriented ETL; KDA = real-time SQL/Flink analytics
Key Concepts
- DPU (Data Processing Unit) — unit of compute for Glue; 1 DPU = 4 vCPU + 16GB RAM; billed per second
- Glue Studio — visual drag-and-drop ETL builder; generates PySpark code; supports custom transforms
- pushDownPredicate — filter applied at the S3 partition level before loading data into Glue job; reduces DPU cost
- ResolveChoice — handles ambiguous type conflicts in DynamicFrame (cast, project, make_struct, make_array)
- ApplyMapping — rename columns and change types in DynamicFrame
- Connection — Glue object storing JDBC/network configuration for databases (RDS, Redshift, on-prem)
- Development endpoint — deprecated; use Glue Notebooks (Jupyter-based) instead
- Glue Data Quality — rule-based quality checks on datasets; integrates with Glue ETL jobs
Checklist
Output Format
- 🔴 Critical — no job bookmark on incremental load (full table reprocessed every run)
- 🟡 Warning — crawler running continuously (wasteful); DynamicFrame used for complex aggregations (convert to DataFrame)
- 🟢 Suggestion — pushDownPredicate not applied (unnecessary data scanned); Glue Studio for visual debugging
Exam Tips
- DynamicFrame resolves schema inconsistencies automatically; DataFrame requires a clean schema — use DynamicFrame for ingestion from heterogeneous sources
- Job bookmarks = incremental processing (only new/changed data processed); disable bookmarks for full refresh runs
- Glue Data Catalog = Hive metastore compatible — shared by Athena, EMR, and Redshift Spectrum automatically
- Glue vs EMR: Glue for serverless simple-to-moderate ETL; EMR for complex Spark/Hadoop jobs needing full cluster control or custom runtimes
- Glue Streaming = micro-batch from Kinesis/MSK — NOT true streaming like Kinesis Data Analytics (Flink)
- pushDownPredicate = filter at S3 partition level BEFORE loading into Glue job — major cost and performance optimization
- ResolveChoice handles type ambiguity (e.g., a field that is sometimes String, sometimes Int) — know the four options: cast, project, make_struct, make_array
- Glue workflows triggers: Scheduled / On-demand / Conditional (success/failure/completion of other jobs)