• Contents:

Intro to AWS Glue from SAA-C03 Notes

AWS Glue 101

AWS Glue

Ref: https://www.udemy.com/course/aws-certified-machine-learning-engineer-associate-mla-c01/learn/lecture/45285225

• 🔧 Two main functions:
  1. Serverless cataloging: discovery & definition of schemas & tables within data
  2. Serverless ETL: Feature engineering on raw data, then load to target
     • 💡 Runs Apache Spark under the hood

Glue Crawlers & Data Catalogs

• Crawlers scan data source (e.g. S3) & create schema → data catalog
  • ‼️ Catalogs only store schema/metadata**, original data stays in S3!**
  • Once cataloged, unstructured data can be treated like structured (can be read by Athena, EMR…)
  • 💡 AWS Glue acts as the “glue” between unstructured data in data store and services that analyze structured data
• ❗ Glue crawler extracts partitions based on how S3 data is organized!
  • ‼️ Think upfront how the data lake in S3 will be queried! Some structure in the unstructured data is desired!
  • e.g. if querying primarily by device, store data as device/yyyy/mm/dd instead of yyyy/mm/dd/device
• Apache Hive integration: run SQL-like queries from EMR
  • Glue Data Catalog ↔ Hive metastore
    • import & export
• 💰 Billing
  • Per-second billing for crawlers
  • Data Catalog free tier: First million objects stored & accesses are free

Glue ETL

• Feature engineering on raw data, then load to target (S3, JDBC, even Glue Data Catalog)
  • Raw data can be in S3, JDBC or come from streaming (Kinesis or Kafka, runs on Apache Spark Structured Streaming)
• Run on serverless Apache Spark platform → automatic Python or Scala code generation
  • Performance of underlying Spark jobs based on amount of DPUs (data processing units)
• Execution
  • Can be on-demand
  • Can be event-driven (Glue Scheduler or Glue Triggers)
  • Job bookmarks → persist state from job run, prevent reprocessing of old data
    • ❗ only handles new rows, not updated rows!
  • Failure/success can be sent to EventBridge/CW
• DynamicFrame: collection of DynamicRecords (self-describing, have a schema)
• Transformations
  • Bundled transformations (SQL-like): DropFields, DropNullFields, Filter, Join, Map…
  • ML transformations (e.g. FindMatches ML → identify duplicates/matching records)
  • Format conversions: CSV, JSON, Avro, Parquet, ORC, XML
  • Apache Spark transformations (e.g. K-Means)
  • ResolveChoice → deal with ambiguities
• ETL scripts can update Data Catalog schemas & partitions if necessary