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© 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Lyon Data Science – 07/01/2016
julsimon@amazon.fr
@julsimon
Building prediction models with
Amazon Redshift and Amazon ML
Julien Simon, Technical Evangelist, AWS
Navigating the seven seas of Big Data
Universal Pictures
You
Your boss
Your infrastructure
You need a better boat, not a bigger one
Let’s face it, Big Data is great fun until:
a) terabytes of data are lost forever
b) tons of money are spent on non-scalable systems
c) months are wasted on plumbing
d) all of the above…
How about simple, cost-efficient, managed services that
non-experts could use to build Big Data apps?

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Collect Store Analyze Consume
A
iOS
 Android
Web Apps
Logstash
Amazon
RDS
Amazon
DynamoDB
Amazon
ElasticSearch
Amazon

S3
Apache
Kafka
Amazon

Glacier
Amazon

Kinesis
Amazon

DynamoDB
Amazon
Redshift
Impala
Pig
Amazon ML
Streaming
Amazon

Kinesis
AWS
Lambda
AmazonElasticMapReduce
Amazon
ElastiCache
SearchSQLNoSQLCache
StreamProcessing
Batch
Interactive
Logging
StreamStorage
IoT
Applications
FileStorage
Analysis&Visualization
Hot
Cold
Warm
Hot
Slow
Hot
ML
Fast
Fast
Amazon
QuickSight
Transactional Data
File Data
Stream Data
Notebooks
Predictions
Apps & APIs
Mobile
Apps
IDE
Search Data
ETL
AWS re:Invent 2015 | (BDT310) Big Data Architectural Patterns and Best Practices on AWS
Interactive &
Batch analytics
Producer Amazon S3
Amazon EMR
Hive
Pig
Spark
Amazon
ML
process
store
Consume
Amazon
Redshift
Amazon EMR
Presto
Impala
Spark
Batch
Interactive
Batch Prediction
Real-time Prediction
AWS re:Invent 2015 | (BDT310)
Big Data Architectural Patterns and Best Practices on AWS
Real-time analytics
Producer
Apache
Kafka
Kinesis
Client Library
AWS Lambda
Spark
Streaming
Apache
Storm
Amazon
SNS
Amazon
ML
Notifications
Amazon
ElastiCache
Amazon
DynamoDB
Amazon
RDS
Amazon
ES
Alert
App state
Real-time Prediction
KPI
process
store
DynamoDB
Streams
Amazon
Kinesis
AWS re:Invent 2015 | (BDT310)
Big Data Architectural Patterns and Best Practices on AWS
Amazon Redshift
an enterprise-level, petabyte scale, fully managed
data warehousing service
Column-oriented database
Optimized for OLAP and BI workloads
Based on PostgreSQL 8.0.2
•  SQL is all you need to know
•  PostgreSQL ODBC and JDBC drivers are supported
•  https://docs.aws.amazon.com/fr_fr/redshift/latest/dg/c_redshift-
and-postgres-sql.html
Free tier: https://aws.amazon.com/fr/redshift/free-trial/
Available on-demand from $0.25 / hour / node (us-east-1)
As low as $0.094 / hour / node (us-east-1, 3-year RI)
https://aws.amazon.com/redshift

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Amazon Redshift architecture
https://docs.aws.amazon.com/fr_fr/redshift/latest/dg/c_high_level_system_architecture.html
Parallel processing
Columnar data storage
Data compression
Query optimization
Compiled code
Workload management
https://docs.aws.amazon.com/fr_fr/redshift/latest/mgmt/working-with-clusters.html#rs-about-clusters-and-nodes
Instance types
Case study: Photobox
Maxime Mezin, Data & Photo Science Director:
“L’entrepôt de données ne comportait que les données du site e-commerce liées aux ventes.
Alors que nous avions la volonté d’intégrer des données du service clients et des données
d’analyse (…) Nous avions atteint la limite du stockage de la base Oracle, et cela ne marchait
pas très bien en termes de performances”
“Avec Redshift, la rapidité d’exécution des traitements a été multipliée par 10. Sans parler de
la vitesse de chargement des données”
“On paie en fonction de la quantité de données que l’on va stocker. Chez Google, cela était
plus compliqué”
•  2 Redshift clusters : 1 for historical data, 1 for real-time processing (SSD)
•  TCO divided by 7 (90K€à13K€)
http://www.lemagit.fr/etude/Photobox-consolide-et-analyse-ses-donnees-avec-AWS-RedShift
Case study: Financial Times
•  BI analysis of customer usage, to decide which stories to cover
•  Conventional data warehouse built using Microsoft technologies
•  Scalability issues, impossible to perform real-time analytics à Amazon Redshift PoC
•  Amazon Redshift performed so quickly that some analysts thought it was malfunctioning J
John O’Donovan, CTO:
“Some of the queries we’re running are 98 percent faster, and most things are running 90
percent faster (…) and the ability to try Redshift out before having to invest a significant
amount of capital was a huge bonus.”
“Amazon Redshift is the single source of truth for our user data.”
“Being able to explore near-real-time data improves our decision making massively. We can
make decisions based on what’s happening now rather than what happened three or four
days ago.”
https://aws.amazon.com/solutions/case-studies/financial-times/

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Amazon Redshift performance
No indexes, no partitioning, etc.
Distribution key
•  How data is spread across nodes
•  EVEN (default), ALL, KEY
Sort key
•  How data is sorted inside of disk blocks
•  Compound and interleaved keys are possible
Both are crucial to query performance! Universal Pictures
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Demo gods, I’m your humble servant,
please be good to me
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sort key + distribution key (3)
Load 1 billion lines (~45GB) from Amazon S3
Run queries (A, B, C) on a 4-node cluster
… while resizing the cluster to 16 nodes J
932
529
233
0
100
200
300
400
500
600
700
800
900
1000
4 8 16
Load 1B lines
241
111
50,00
153
73
42,50
24,8
13,2
6,00
0
50
100
150
200
250
300
4 8 16
A1
A2
A3
27,1
15,1
6,80
18,6
9,8
5,10
14,2
7,2
3,70
0
5
10
15
20
25
30
4 8 16
B1
B2
B3
18,5
9,2
4,20
1,2 0,72 0,65
0,48 0,49 0,450
2
4
6
8
10
12
14
16
18
20
4 8 16
C1
C2
C3
Amazon Machine Learning
A managed service for building ML models
and generating predictions
Integration with Amazon S3, Redshift and RDS
Data transformation, visualization and exploration
Model evaluation and interpretation tools
API for inspection and automation
API for batch and real-time predictions
$0.42 / hour for analysis and model building (eu-west-1)
$0.10 per 1000 batch predictions
$0.0001 per real-time prediction
https://aws.amazon.com/machine-learning

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•  Binary attributes à binary classification
•  Categorical attributes à multi-class classification
•  Numeric attributes à linear regression
•  Code samples on
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Chief Technology Officer
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Other Amazon ML use cases
ICAO classifies and
categorizes international
aviation incidents daily
Plans to train ML models to predict
fuel efficiency based on vehicle
metadata, to push near-real-time data
to customers
Securely sorts millions of mail pieces
for clients every day for tremendous
cost savings then brings it to the USPS
for delivery
DEMO #2
Demo gods, I know I’m pushing it,
but please don’t let me down now
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Load data from Amazon Redshift
Train and evaluate a regression model with Amazon ML
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Perform real-time prediction from Java app
(https://raw.githubusercontent.com/juliensimon/aws/master/ML/MLSample.java)

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cloud computingcloudkatataipei2013cloud kata
Thank you! Let’s keep in touch J
Want to start a local AWS User Group? https://aws.amazon.com/fr/usergroups/
Many events and meetups in 2016 all across France
à Please follow us at @aws_actus @julsimon
AWS Summit Paris : 31/05/2016 (free!) https://aws.amazon.com/fr/paris16/
14/01
13/01
BONUS SLIDES
https://docs.aws.amazon.com/fr_fr/redshift/latest/dg/c_columnar_storage_disk_mem_mgmnt.html
Row storage vs columnar storage
Amazon Redshift & Machine Learning resources
Documentation
https://aws.amazon.com/documentation/redshift/
https://aws.amazon.com/documentation/machine-learning/
Big Data videos from AWS re:Invent 2015
https://blogs.aws.amazon.com/bigdata/post/Tx3D3UYOXB9XG6Z/Videos-now-available-for-
AWS-re-Invent-2015-Big-Data-Analytics-sessions
If you’re going to watch only one: https://www.youtube.com/watch?v=K7o5OlRLtvU

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More Amazon Redshift resources
Articles by Werner Vogel, CTO, Amazon.com
http://www.allthingsdistributed.com/2012/11/amazon-redshift.html
http://www.allthingsdistributed.com/2013/02/amazon-redshift-resilience.html
http://www.allthingsdistributed.com/2013/05/amazon-redshift-designing-for-security.html
Tuning Amazon Redshift
https://docs.aws.amazon.com/fr_fr/redshift/latest/dg/t_Sorting_data.html
https://docs.aws.amazon.com/fr_fr/redshift/latest/dg/t_Distributing_data.html
http://blogs.aws.amazon.com/bigdata/post/Tx31034QG0G3ED1/Top-10-Performance-Tuning-
Techniques-for-Amazon-Redshift
Creating and deleting an Amazon Redshift cluster
$ aws redshift create-cluster --cluster-identifier CLUSTER_NAME
--node-type dc1.large --number-of-nodes 4
--db-name DATABASE_NAME
--master-username USER_NAME
--master-user-password USER_PASSWORD
--publicly-accessible ß probably not what you want!
$ aws redshift delete-cluster --cluster-identifier CLUSTER_NAME
--skip-final-cluster-snapshot ß data will be lost!
Connecting to Amazon Redshift with psql
$ psql -h xxx.redshift.amazonaws.com -p 5439
-d DB_NAME -U USER_NAME
Force SSL:
$ psql -h xxx.redshift.amazonaws.com -p 5439
-U USER_NAME "dbname=DB_NAME sslmode=require”
Loading data from Amazon S3 to Amazon Redshift
COPY command example
$ copy TABLE_NAME
from 's3://BUCKET_NAME/FOLDER_NAME/'
region 'eu-west-1’
credentials ‘aws_access_key_id=MY_ACCESS_KEY;
aws_secret_access_key=MY_SECRET_KEY’
delimiter ',’ bzip2 maxerror 1000;
View last 10 load errors
select * from stl_load_errors order by starttime desc limit 10;

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Resizing an Amazon Redshift cluster
$ aws redshift modify-cluster
--cluster-identifier mycluster
--number-of-nodes 8
Listing Amazon ML models
$ aws machinelearning describe-ml-models
--query "Results[*].{Name:Name, Id:MLModelId,
Type:MLModelType}"

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Building prediction models with Amazon Redshift and Amazon ML

  • 1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Lyon Data Science – 07/01/2016 julsimon@amazon.fr @julsimon Building prediction models with Amazon Redshift and Amazon ML Julien Simon, Technical Evangelist, AWS
  • 2. Navigating the seven seas of Big Data
  • 4. You need a better boat, not a bigger one Let’s face it, Big Data is great fun until: a) terabytes of data are lost forever b) tons of money are spent on non-scalable systems c) months are wasted on plumbing d) all of the above… How about simple, cost-efficient, managed services that non-experts could use to build Big Data apps?
  • 5. Collect Store Analyze Consume A iOS Android Web Apps Logstash Amazon RDS Amazon DynamoDB Amazon ElasticSearch Amazon
 S3 Apache Kafka Amazon
 Glacier Amazon
 Kinesis Amazon
 DynamoDB Amazon Redshift Impala Pig Amazon ML Streaming Amazon
 Kinesis AWS Lambda AmazonElasticMapReduce Amazon ElastiCache SearchSQLNoSQLCache StreamProcessing Batch Interactive Logging StreamStorage IoT Applications FileStorage Analysis&Visualization Hot Cold Warm Hot Slow Hot ML Fast Fast Amazon QuickSight Transactional Data File Data Stream Data Notebooks Predictions Apps & APIs Mobile Apps IDE Search Data ETL AWS re:Invent 2015 | (BDT310) Big Data Architectural Patterns and Best Practices on AWS
  • 6. Interactive & Batch analytics Producer Amazon S3 Amazon EMR Hive Pig Spark Amazon ML process store Consume Amazon Redshift Amazon EMR Presto Impala Spark Batch Interactive Batch Prediction Real-time Prediction AWS re:Invent 2015 | (BDT310) Big Data Architectural Patterns and Best Practices on AWS
  • 7. Real-time analytics Producer Apache Kafka Kinesis Client Library AWS Lambda Spark Streaming Apache Storm Amazon SNS Amazon ML Notifications Amazon ElastiCache Amazon DynamoDB Amazon RDS Amazon ES Alert App state Real-time Prediction KPI process store DynamoDB Streams Amazon Kinesis AWS re:Invent 2015 | (BDT310) Big Data Architectural Patterns and Best Practices on AWS
  • 8. Amazon Redshift an enterprise-level, petabyte scale, fully managed data warehousing service Column-oriented database Optimized for OLAP and BI workloads Based on PostgreSQL 8.0.2 •  SQL is all you need to know •  PostgreSQL ODBC and JDBC drivers are supported •  https://docs.aws.amazon.com/fr_fr/redshift/latest/dg/c_redshift- and-postgres-sql.html Free tier: https://aws.amazon.com/fr/redshift/free-trial/ Available on-demand from $0.25 / hour / node (us-east-1) As low as $0.094 / hour / node (us-east-1, 3-year RI) https://aws.amazon.com/redshift
  • 9. Amazon Redshift architecture https://docs.aws.amazon.com/fr_fr/redshift/latest/dg/c_high_level_system_architecture.html Parallel processing Columnar data storage Data compression Query optimization Compiled code Workload management
  • 11. Case study: Photobox Maxime Mezin, Data & Photo Science Director: “L’entrepôt de données ne comportait que les données du site e-commerce liées aux ventes. Alors que nous avions la volonté d’intégrer des données du service clients et des données d’analyse (…) Nous avions atteint la limite du stockage de la base Oracle, et cela ne marchait pas très bien en termes de performances” “Avec Redshift, la rapidité d’exécution des traitements a été multipliée par 10. Sans parler de la vitesse de chargement des données” “On paie en fonction de la quantité de données que l’on va stocker. Chez Google, cela était plus compliqué” •  2 Redshift clusters : 1 for historical data, 1 for real-time processing (SSD) •  TCO divided by 7 (90K€à13K€) http://www.lemagit.fr/etude/Photobox-consolide-et-analyse-ses-donnees-avec-AWS-RedShift
  • 12. Case study: Financial Times •  BI analysis of customer usage, to decide which stories to cover •  Conventional data warehouse built using Microsoft technologies •  Scalability issues, impossible to perform real-time analytics à Amazon Redshift PoC •  Amazon Redshift performed so quickly that some analysts thought it was malfunctioning J John O’Donovan, CTO: “Some of the queries we’re running are 98 percent faster, and most things are running 90 percent faster (…) and the ability to try Redshift out before having to invest a significant amount of capital was a huge bonus.” “Amazon Redshift is the single source of truth for our user data.” “Being able to explore near-real-time data improves our decision making massively. We can make decisions based on what’s happening now rather than what happened three or four days ago.” https://aws.amazon.com/solutions/case-studies/financial-times/
  • 13. Amazon Redshift performance No indexes, no partitioning, etc. Distribution key •  How data is spread across nodes •  EVEN (default), ALL, KEY Sort key •  How data is sorted inside of disk blocks •  Compound and interleaved keys are possible Both are crucial to query performance! Universal Pictures
  • 14. DEMO #1 Demo gods, I’m your humble servant, please be good to me Create tables: no key (1), sort key (2), sort key + distribution key (3) Load 1 billion lines (~45GB) from Amazon S3 Run queries (A, B, C) on a 4-node cluster … while resizing the cluster to 16 nodes J
  • 15. 932 529 233 0 100 200 300 400 500 600 700 800 900 1000 4 8 16 Load 1B lines 241 111 50,00 153 73 42,50 24,8 13,2 6,00 0 50 100 150 200 250 300 4 8 16 A1 A2 A3 27,1 15,1 6,80 18,6 9,8 5,10 14,2 7,2 3,70 0 5 10 15 20 25 30 4 8 16 B1 B2 B3 18,5 9,2 4,20 1,2 0,72 0,65 0,48 0,49 0,450 2 4 6 8 10 12 14 16 18 20 4 8 16 C1 C2 C3
  • 16. Amazon Machine Learning A managed service for building ML models and generating predictions Integration with Amazon S3, Redshift and RDS Data transformation, visualization and exploration Model evaluation and interpretation tools API for inspection and automation API for batch and real-time predictions $0.42 / hour for analysis and model building (eu-west-1) $0.10 per 1000 batch predictions $0.0001 per real-time prediction https://aws.amazon.com/machine-learning
  • 17. Amazon ML prediction algorithms •  Binary attributes à binary classification •  Categorical attributes à multi-class classification •  Numeric attributes à linear regression •  Code samples on https://github.com/awslabs/machine-learning-samples
  • 18. Case study: BuildFax “Amazon Machine Learning democratizes the process of building predictive models. It's easy and fast to use, and has machine- learning best practices encapsulated in the product, which lets us deliver results significantly faster than in the past” Joe Emison, Founder & Chief Technology Officer https://aws.amazon.com/solutions/case-studies/buildfax/
  • 19. Other Amazon ML use cases ICAO classifies and categorizes international aviation incidents daily Plans to train ML models to predict fuel efficiency based on vehicle metadata, to push near-real-time data to customers Securely sorts millions of mail pieces for clients every day for tremendous cost savings then brings it to the USPS for delivery
  • 20. DEMO #2 Demo gods, I know I’m pushing it, but please don’t let me down now Load data from Amazon S3 to Redshift and explore Train and evaluate a regression model with Amazon ML Perform batch prediction from data stored in Amazon S3 Load data from Amazon Redshift Train and evaluate a regression model with Amazon ML Create a real-time prediction API Perform real-time prediction from Java app (https://raw.githubusercontent.com/juliensimon/aws/master/ML/MLSample.java)
  • 21. Thank you! Let’s keep in touch J Want to start a local AWS User Group? https://aws.amazon.com/fr/usergroups/ Many events and meetups in 2016 all across France à Please follow us at @aws_actus @julsimon AWS Summit Paris : 31/05/2016 (free!) https://aws.amazon.com/fr/paris16/ 14/01 13/01
  • 24. Amazon Redshift & Machine Learning resources Documentation https://aws.amazon.com/documentation/redshift/ https://aws.amazon.com/documentation/machine-learning/ Big Data videos from AWS re:Invent 2015 https://blogs.aws.amazon.com/bigdata/post/Tx3D3UYOXB9XG6Z/Videos-now-available-for- AWS-re-Invent-2015-Big-Data-Analytics-sessions If you’re going to watch only one: https://www.youtube.com/watch?v=K7o5OlRLtvU
  • 25. More Amazon Redshift resources Articles by Werner Vogel, CTO, Amazon.com http://www.allthingsdistributed.com/2012/11/amazon-redshift.html http://www.allthingsdistributed.com/2013/02/amazon-redshift-resilience.html http://www.allthingsdistributed.com/2013/05/amazon-redshift-designing-for-security.html Tuning Amazon Redshift https://docs.aws.amazon.com/fr_fr/redshift/latest/dg/t_Sorting_data.html https://docs.aws.amazon.com/fr_fr/redshift/latest/dg/t_Distributing_data.html http://blogs.aws.amazon.com/bigdata/post/Tx31034QG0G3ED1/Top-10-Performance-Tuning- Techniques-for-Amazon-Redshift
  • 26. Creating and deleting an Amazon Redshift cluster $ aws redshift create-cluster --cluster-identifier CLUSTER_NAME --node-type dc1.large --number-of-nodes 4 --db-name DATABASE_NAME --master-username USER_NAME --master-user-password USER_PASSWORD --publicly-accessible ß probably not what you want! $ aws redshift delete-cluster --cluster-identifier CLUSTER_NAME --skip-final-cluster-snapshot ß data will be lost!
  • 27. Connecting to Amazon Redshift with psql $ psql -h xxx.redshift.amazonaws.com -p 5439 -d DB_NAME -U USER_NAME Force SSL: $ psql -h xxx.redshift.amazonaws.com -p 5439 -U USER_NAME "dbname=DB_NAME sslmode=require”
  • 28. Loading data from Amazon S3 to Amazon Redshift COPY command example $ copy TABLE_NAME from 's3://BUCKET_NAME/FOLDER_NAME/' region 'eu-west-1’ credentials ‘aws_access_key_id=MY_ACCESS_KEY; aws_secret_access_key=MY_SECRET_KEY’ delimiter ',’ bzip2 maxerror 1000; View last 10 load errors select * from stl_load_errors order by starttime desc limit 10;
  • 29. Resizing an Amazon Redshift cluster $ aws redshift modify-cluster --cluster-identifier mycluster --number-of-nodes 8
  • 30. Listing Amazon ML models $ aws machinelearning describe-ml-models --query "Results[*].{Name:Name, Id:MLModelId, Type:MLModelType}"