In this session we will introduce key ETL features of AWS Glue and cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We will also discuss how to build scalable, efficient, and serverless ETL pipelines.

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Ben Thurgood
Principal Solutions Architect
Building Serverless ETL Pipelines
With AWS Glue

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Can I get you
to go ahead
and…

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…prepare
our data for
analysis

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Generate Collect Store
Extract
Transform
Load
Analyse
Visualise/
Report

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Collect Store
Extract
Transform
Load
Analyse
Visualise/
Report
Generate
ERP
Connected
devices
Transactions
Social
media
Web logs /
cookies

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Generate Store
Extract
Transform
Load
Analyse
Visualise/
Report
Collect
Polling Application
Amazon
Kinesis Stream
Amazon
Kinesis Firehose

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Generate Store
Extract
Transform
Load
Analyse
Visualise/
Report
Collect
AWSSnowball
Amazon S3
AWSDMS

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Generate Collect
Extract
Transform
Load
Analyse
Visualise/
Report
Store
Amazon
RDS
Amazon S3
Database
on EC2

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Generate Collect Store
Extract
Transform
Load
Visualise/
Report
Analyse
Amazon Redshift &
Redshift Spectrum
Amazon EMR
Amazon Athena
Amazon Kinesis
Analytics

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Generate Collect Store
Extract
Transform
Load
Analyse
Visualise/
Report
Data
scientists
Business
users
Engagement
platforms
Automation/
events
Data
analysts

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Generate Collect Store Analyse
Visualise/
Report
Extract
Transform
Load
AWS
Lambda
Amazon
Kinesis Enabled
Amazon EMR

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No Problem…?
Deal with these Terabytes and Petabytes of data
Simplify querying disparate data sets
Combine existing / legacy data with modern data sets
Prepare data for machine learning

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Some extra challenges..
Volumes will grow (the new oil)
Adding data sources
Large proportion of ETL is hand coding
Data formats change over time
• Within data you already have
• Changes will be coming soon
Target schemas change

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Extract Transform
Load
Analyse
Visualise/
Report
Generate
Collect
Store

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And.. ETL Is Not The Rewarding Part
Time
Value
ETL
Analyse and Consume
Generate
Collect
Store

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Generate Collect Store Analyse
Visualise/
Report
Extract
Transform
Load
AWS Glue

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Why AWS Glue?

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Automate your ETL
Automatically discover and categorise your data
• Connect to your data sources
• Generate your Data Catalogue
Make it immediately searchable and queryable
• Athena
• Redshift
• EMR

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Automate your ETL
Generates your ETL code
• Clean
• Enrich
• Move
Adaptable code
Extension to Spark in Python or Scala

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Automate your ETL
Runs your ETL jobs serverless
• Managed
• Control the amount of resources used
• Scales out automatically
Schedule or trigger jobs

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Glue Customer Examples

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How do I ETL my data?

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Four Steps
Crawl Map Edit and
Explore
Schedule

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How Do I Discover My Data?

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Glue Data Catalogue: Crawlers
• Automatically discover new data and extract schema definitions
• Detect schema changes and version tables
• Detect Apache Hive style partitions on Amazon S3
• Built-in classifiers for popular data types
• Custom classifiers using Grok expressions
• Run ad hoc or on a schedule; serverless – only pay when crawler runs

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Crawlers: Classifiers
IAM Role
Glue Crawler
Data Lakes
Data Warehouse
Databases
Amazon
RDS
Amazon
Redshift
Amazon S3
JDBC Connection
Object Connection
Built-In Classifiers
MySQL
MariaDB
PostreSQL
Aurora
Redshift
Avro
Parquet
ORC
JSON & BJSON
Logs
(Apache, Linux, MS, Ruby, Redis, and many others)
Delimited
(comma, pipe, tab, semicolon)
Compressed Formats
(ZIP, BZIP, GZIP, LZ4, Snappy)
Create additional Custom
Classifiers with Grok!

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Example Classifier
2018-03-18T01:44:19+00:00 [prefix-p-123-a-7z] WARN: There is a message
Grok expression example:
%{TIMESTAMP_ISO8601:timestamp} [%{MESSAGEPREFIX:message_prefix}] %{CRAWLERLOGLEVEL:loglevel} :
%{GREEDYDATA:message}
Built in patterns:
TIMESTAMP_ISO8601 %{YEAR}-%{MONTHNUM}-%{MONTHDAY}[T ]%{HOUR}:?%{MINUTE}(?::?%{SECOND})?%{ISO8601_TIMEZONE}?
GREEDYDATA .*
Custom patterns
CRAWLERLOGLEVEL (BENCHMARK|ERROR|WARN|INFO|TRACE)
MESSAGEPREFIX .*-.*-.*-.*-.*
Handy Grok debugger:
https://grokdebug.herokuapp.com/

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Crawler: Detecting Partitions
file 1 file N… file 1 file N…
date=10 date=15…
month=Nov
S3 bucket hierarchy Table definition
Estimate schema similarity among files at each level to
handle semi-structured logs, schema evolution…
sim=.99 sim=.95
sim=.93
month
date
col 1
col 2
str
str
int
float
Column Type

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Glue Data Catalog

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Glue Data Catalog: Table Properties
Table schema
Table properties
Data statistics
Nested fields

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Glue Data Catalog: Version control
List of table versionsCompare schema versions

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How Do I Build The ETL?

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Job Authoring: Automatic Code Generation

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Job Authoring: Automatic Code Generation

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Job Authoring: ETL Code
 Human-readable, editable, and portable PySpark code

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Job Authoring: Glue Dynamic Frames
Dynamic frame schema
A C D [ ]
X Y
B1 B2
Like Apache Spark’s Data Frames, but better for:
• Cleaning and (re)-structuring semi-structured
data sets, e.g. JSON, Avro, Apache logs ...
No upfront schema needed:
• Infers schema on-the-fly, enabling
transformations in a single pass
Easy to handle the unexpected:
• Tracks new fields, and inconsistent changing
data types with choices, e.g. integer or string
• Automatically mark and separate error records

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Job Authoring: Glue Transforms
B B B
project
B
cast
B
separate into cols
B BResolveChoice()
Apply
Mapping()
C
YX
A
A X Y

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Job Authoring: Relationalize() Transform
Semi-structured schema Relational schema
FKA B C.X C.Y
PK ValueOffset
A C D [ ]
X Y
B B
B

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Job Authoring: ETL Code
• Human-readable, editable, and portable PySpark code
• Flexible: Glue’s ETL library simplifies manipulating complex, semi-structured data
• Customisable: Use native PySpark, import custom libraries, and/or leverage Glue’s libraries

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Job Authoring: Developer Endpoints
Remote
Interpreter
• Environment to iteratively develop and test ETL code.
• Connect your IDE or notebook (e.g. Zeppelin) to a Glue development endpoint.
• When you are satisfied with the results you can create an ETL job that runs your code.
Interpreter
Server
Glue Apache Spark environment

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Job Authoring: ETL Code
 Human-readable, editable, and portable
PySpark code
 Flexible: Glue’s ETL library simplifies
manipulating complex, semi-structured data
 Customisable: Use native PySpark, import
custom libraries, and/or leverage Glue’s
libraries
 Collaborative: share code snippets via
GitHub, reuse code across jobs

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How do I run ETL jobs?

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Serverless Job Execution
Auto-configure VPC & role-based access
security & isolation preserved
Customers can specify job capacity
using Data Processing Units (DPU)
Automatically scale resources
Only pay for the resources you consume
per-second billing (10-minute min)
Customer VPC Customer VPC
Compute instances

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Data Processing Units (DPUs)
1 DPU = 4 vCPU + 16GB RAM
Storage:
• Free for the first million objects stored
• $1 per 100,000 objects stored above 1M, per month
Requests:
• Free for the first million requests per month
• $1 per million requests above 1M in a month

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Job Composition: Example
Data based
>10 MB new
ad-click
logs
Sales: Revenue by
customer segment
Schedule
Central: ROI by
customer segment
weekly
sales

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Sounds Good In Theory…
What’s It Really Like?

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Demo context
Amazon
RDS
Amazon S3
AWS Glue
Amazon Redshift &
Redshift Spectrum
Amazon EMR
Amazon Athena

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How AWS Glue Helps with ETL
Automatically discover your data
Generate ETL code
Run your ETL jobs serverless

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Speaker Contact:
Ben Thurgood
Principal Solutions
Architect
btgood@amazon.com
Homework suggestion:
https://amzn.to/2iWVYey

53. Thank you