Overview

1. Json Schema Cheat Sheet Download
2. Create Json Schema

• Serverless ETL (extract, transform, load) service
• Uses Spark underneath

Glue Crawler

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• This is not crawler in the sense that would pull data from data sources
• A crawler reads data from data sources ONLY TO determine its data structure / schema
  • Crawls databases using a connection (actually a connection profile)
  • Crawls files on S3 without needing a connection
• After each crawl, virtual tables are created in Data Catalog, these tables stores meta data like the columns, data types, location, etc. of the data, but not the data itself
• Crawler is serverless, you pay by Data Processing Unit (time consumed for crawling), but there is a 10 minute minimum duration for each crawl
• You can create tables directly without using crawlers

Crawling Behaviors

• For database tables, it is easy to determine data types for columns, but crawler mostly shines on file based data (i.e. data lake)
• The crawler determines column and data type by reading data from the files and “guess” the format based on patterns and similarity of the data format for different files
• It creates tables based on the prefix / folder name of similarly-formatted files, and create partitions of tables as needed
• It uses “classifiers” to parse and understand data, classifiers are sets of pattern matchers
  • User may create custom classifiers
  • Classifiers are categorized based on file types, e.g. the Grok classifier is for text based files
    • There are JSON and CSV classifiers, they are for respected file types
  • Classifier will only classify file types into their primitive data types, for example, even if a JSON contains ISO 8601 formatted timestamp, the crawler will still see it as a string

Glue Data Catalog

• An index to the location, schema and runtime metrics of your data
• Connections
  • This is actually connection configuration for Glue to connect to databases
  • If you access S3 via a VPCE, then you also need a NETWORK type connection
    • Creating NETWORK type connection without a VPCE will cause “Cannot create enum from NETWORK value!” error which is very confusing
  • If you access S3 via its public endpoints then no connection is required

Glue ETL

• Glue ETL is EMR made serverless
• It runs Spark underneath
• User can create Spark jobs and run it directly without provisioning servers
  • Glue ETL provisions EMR clusters on-the-fly, so expect 5-10 minutes cold start time even for the simplest jobs
• Glue has built-in helpers to perform common tasks like casting string to timestamp (you will need this for crawled JSONs)
• ETL is useful for
  • Type conversion for columns
  • Compress data from plain format (text, log, CSV, JSON) to columnar format (parquet)
  • Data joining, so data can be scanned more easily
  • Other data transforming and manipulation
• ETL Jobs can be connected to form a Workflow, every Job in the workflow can be Triggered manually or automatically

Json Schema Cheat Sheet Download

Dev Endpoint

Create Json Schema

• Glue ETL provisions EMR for every Job run, so it is too slow for development purposes
• If you are developing your Spark script and want to have an environment that is in the cloud and has access to the resources, use Dev Endpoint
• Basically Glue provisions an EMR cluster that is long-running (and long-charging) for you as a dev environment, remember to delete the endpoint after you are done to prevent bill overflow