This document discusses Amazon Redshift and Intuit's modernization of their cloud data warehousing practices on AWS. It provides an overview of AWS databases and analytics services including Redshift, Athena, EMR, and more. It then discusses improvements to Redshift's speed, scale, simplicity and security. Finally, it discusses Intuit's transition from an on-premises datacenter to using AWS cloud services.

2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Modern Cloud Data Warehousing ft.
Intuit: Optimize Analytics Practices
A N T 2 0 2 - R 1
Maor Kleider
Principal Product Manager
Amazon Web Services
Jason Rhoades
Systems Architect
Intuit

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Raise your hand if you’re using
Amazon Redshift
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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AWS Databases and Analytics
Broad and deep portfolio, built for builders
AWS Marketplace
Redshift
Data warehousing
EMR
Hadoop + Spark
Athena
Interactive analytics
Kinesis Analytics
Real-time
Elasticsearch service
Operational Analytics
RDS
MySQL, PostgreSQL, MariaDB,
Oracle, SQL Server
Aurora
MySQL, PostgreSQL
QuickSight SageMaker
DynamoDB
Key value, Document
ElastiCache
Redis, Memcached
Neptune
Graph
Timestream
Time Series
QLDB
Ledger Database
S3/Glacier
Glue
ETL & Data Catalog
Lake Formation
Data Lakes
Database Migration Service | Snowball | Snowmobile | Kinesis Data Firehose | Kinesis Data Streams | Data Pipeline | Direct Connect
Data Movement
AnalyticsDatabases
Business Intelligence & Machine Learning
Data Lake
Managed
Blockchain
Blockchain
Templates
Blockchain
Comprehend Rekognition Lex Transcribe DeepLens
250+ Solutions
730+ Database
solutions
600+ Analytics
solutions
25+ Blockchain
solutions
20+ Data lake
solutions
30+ solutions
RDS on VMWare

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Data
every 5 years
There is more data
than people think.
years
live for
Data platforms need to
scalegrows

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There are more
data types than
ever before.

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Hadoop Elasticsearch
There are more
ways to analyze data
than ever before.
Years ago
11 8 5 4
Presto Spark
Didn’t exist

8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
What does
data warehouse
modernization
mean? Easy to use
Extends to
your Data Lake
Don’t waste time on
menial administrative
tasks and maintenance
Directly analyze data
stored in your data lake
in open formats
Any scale of data,
workloads, and users
Dynamically scale up to
guarantee performance even
with unpredictable demands
and data volumes
Faster
time-to-insights
Consistently fast
performance, even with
thousands of concurrent
queries and users

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Amazon Redshift
Fastest
Get faster time-to-insight
for all types of analytics
workloads; powered by
machine learning, columnar
storage and MPP
Unlimited
scale
Extends your
Data Lake
1/10th
the cost
Dynamically scale up to
guarantee performance
even with unpredictable
analytical demands and
data volumes
Analyze data in the Amazon
S3 Data Lake in-place and in
open formats, together with
data loaded into Redshift’s
high performance SSDs
Start at $0.25 per hour,
save costs with automated
administration tasks and
eliminate business impact
due to downtime; as low as
$1,000 per terabyte per year
Fast, simple, cost-effective data
warehouse that can extend queries to your Data Lake
Analyze data in open formats
such as Parquet, ORC, and JSON, using SQL tools

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for their cloud
data warehouse
workloads than
anyone else
Amazon
Redshift

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Selected Amazon Redshift Partners
Data Integration Business Intelligence Systems Integrators

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Amazon Redshift
The 4 things that matter most
Speed Scale SecuritySimplicity
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Let’s dig into what we’ve done
in the past several months
and what’s coming…

14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
features and
enhancements
released*
Amazon Redshift is growing fast and innovating faster
Automatically enabled
short query acceleration
Support for lateral column
alias reference
New Quick Starts
New CloudWatch metrics
Customized
Recommendations
with Advisor
Current and trailing tracks
for release update
Federated authentication
with single sign-on
Improved performance
for commits
COPY from Parquet and
ORC file formats
Additional Spectrum regions
Support for Scalar JSON
and Ion data types
Late materialization for
faster query processing
Support for DATE data
type with Spectrum
Short Query
Acceleration
Utilization reports
Machine learning integration
to accelerate dashboards
and interactive analysis
Improved resource
management for
memory-intensive queries
Faster string manipulation
Support for Parquet and
ORC in Kinesis Data Firehose
Improved workload
management console
experience
Query Editor
Support for late-binding views
SQL Scalar user-defined
functions
Integration with AWS Glue
Support for Nested
Data with Spectrum
Spectrum support
for DATE data type
Improved performance
for UNION ALL queries
Free upgrade from
DC1 to DC2 RIs
Query monitoring rules (QMR)
Support for Zstandard high
compression encoding
Query processing
improvements
Support for Python
UDF logging module
Enhanced VPC routing
Automatically hopping
queries without restarts
Support for uppercase
column names
Result Caching for
Repeat Queries
Support for LISTAGG DISTINCT
Support for ORC and
Grok file formats
Integration with QuickSight
DMS support with Redshift
3.5x Improved
Throughput
Improved performance
for repeat queries
Since we last spoke…
*since re:Invent 2017

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Amazon Redshift
is now >3x
faster than
6 months ago
Normalized Queries Per Hour (QPH)
Assuming Redshift’s QPH 6 months ago=100%
Queriesperhour
Asa%ofRedshift6monthsago
JUL 2018 AUG 2018 SEP 2018 OCT 2018MAY 2018
100%
181%
237%
284%
350%
Higher is better
115%
JUN 2018

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*Since re:Invent 2017
Compiled code cache
Support for lateral
column alias reference
Resource management for
memory-intensive queries
Late materialization
Result caching
Joins involving large numbers of
NULL values in a join key column
Queries with intermediate subquery
results that can be distributed
Cluster
resize operations
Queries that refer to stable
functions with constant expressions
Short query
acceleration
Queries operating over CHAR
and VARCHAR columns
Single-row inserts
Improvements to speed
Expressions on the partition
columns of external tablesFaster string manipulation
Complex EXCEPT
subqueries
Commit processing
enhancements
DC2 nodes
2x the number of tables
in a cluster
Hash join memory utilization
optimizations and cache line
prefetching
COPY operation when
ingesting data from Parquet
and ORC formats
Performance improvement for
queries that refer to stable functions
over constant expressions
Improvements for the COPY
operation when ingesting data
from Parquet and ORC formats
Query processing
improvements
Query rewrites that pushdown selective joins
into a subquery
Query planning
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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How we leverage fleet telemetry

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Performance
improvements
in query speed
- Minero Aoki
Senior Data Engineer, Cookpad Inc.
Redshift query performance and
scalability has been increasing,
even though our data has
grown. In the last 10 months, we
have seen commit performance
increase by 500% without any
increase in cost.

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“20 percent of our queries now
complete in less than one second.
Best of all, we didn’t have to
change anything to get this
speed-up with Redshift, which
supports our mission-critical
workloads.”
-Greg Rokita,
Executive Director of Technology, Edmunds

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Amazon Redshift Elastic Resize (GA)
Adds
additional
nodes
to Redshift
cluster
Distributes
data
across new
configuration
Minimal
transition time
Quickly scale
for varying
workload
demands
Scale up and down in minutes
New!
Redshift
Cluster
Redshift Managed S3
JDBC/ODBC
Leader Node
Backup

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Caching Layer
Concurrency Scaling for bursts of user activity (Preview)
Creates
more
clusters
automatically
on-demand
Consistently
fast
performance
even with
thousands of
concurrent queries
No
advance
hydration
required
Handles
unpredictable
demand variability
New!
Backup
Redshift Managed S3

22. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
For every 24 hours
that your main cluster
is in use, you accrue a
one-hour credit for
Concurrency Scaling.
Concurrency Scaling is
free for more than 97%
of Redshift customers.
Concurrency Scaling for bursts of user activity (Preview) New!

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*Since re:Invent 2017
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Improvements
to simplicity
CloudWatch metrics for
Workload Execution
Breakdown
Current and trailing tracks
for release updates
Lateral column
alias reference
CloudWatch metrics
for Query Duration
by WLM Queues
Cluster resize operations
CloudWatch
Query Runtime Breakdown metric
Stream real-time data in
Parquet or ORC formats
using Kinesis Data Firehose
DISTSTYLE AUTO
distribution style
Free upgrade from for DC1
RIs to DC2
Query Monitoring Rules (QMR)
now support 3x more rules
Short query
acceleration is
self-optimizing
Redshift Advisor for best
practice recommendationsCloudWatch metrics
for Query Throughput
by WLM Queues
Cluster resize Query Editor
Enhancements to
VACUUM DELETE
Manage components
of a multi-part query
in the AWS console
Automatic vacuum delete
Efficiency of backup performance
CloudWatch metrics for Query
Throughput, Query Duration

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Redshift Query Editor
Query data
directly from
the AWS Console
Results are instantly visible
within the console
No need to install
an external JDBC/ODBC client
Launched in October!

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Amazon Redshift intelligent maintenance
VacuumAnalyze WLM
Concurrency
Setting
AutoAuto Auto
Maintenance processes like
vacuum and analyze will
automatically run in the
background.
Redshift will automatically adjust the
WLM concurrency setting to deliver
optimal throughput.
Moving towards
zero-maintenance.
Coming Soon!

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*Since re:Invent 2017
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Improvements to scale
Integrate seamlessly with your data lake
DATE data type
Retrieving metadata for late-binding
viewsSupport for Enhanced VPC Routing
IN-list predicate processing
in Spectrum scans
Query external tables
during a resize operation
Specify the root of an
S3 bucket as the source
for an existing table
Spectrum queries with
aggregations on partition columns
Renaming external
table columns
Table property to specify the file
compression type for external tables
Push the LENGTH()
string function to
Spectrum
ALTER TABLE ADD/DROP
COLUMN for external tables is now
supported via standard JDBC calls
Map datatypes in
Spectrum to contain
arrays
Support for Parquet, ORC, Avro,
CSV, and other open file formats
New Spectrum
regions
Spectrum support
for JSON and ION
Spectrum support
for nested data
Arrays of arrays and
arrays of maps

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Amazon Redshift Spectrum
Redshift Spectrum
query engine
Query across
Redshift and S3
Redshift
data
S3
data lake
Extend the data warehouse to exabytes of data in Amazon S3 Data Lake
No data loading required
Scale compute and storage separately
Directly query data stored in Amazon S3
Parquet, ORC, Avro, JSON, and CSV data formats
 Unload to Parquet
 Spectrum Request Accelerator
Coming
Soon!

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Amazon Redshift is Scalable
Redshift Spectrum: Exabyte data lake query in under three minutes
Compression
Columnar file format
Scanning with 2500 nodes
Static partition elimination
Dynamic partition elimination
Amazon Redshift query optimizer
* Query used a 20 node DC1.8XLarge Amazon Redshift cluster
* Not actual sales data—generated for this demo based on data format used by Amazon Retail.
Imagine you are the manager at a Seattle book store.
An author released her 8th book in a popular series,
and you need to figure out how many copies to order.
Amazon S3
Redshift Spectrum
<3 minutes
5X
10X
2,500X
2X
350X
40X
Roughly 140 terabytes of customer
item order detail records for each
day over the past 20 years
190 million files across 15,000
partitions in S3
One partition per day for USA and
rest of world
Total data size is over an exabyte

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Security is built-in
Compliance certifications
10 GigE (HPC)
Customer
VPC
Internal
VPC
JDBC/ODBC
Compute
Nodes
Leader
Node
End-to-end encryption
Integration with AWS Key
Management Service

30. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The power of data lakes
Most ways to bring data in
Terabyte – Exabyte scale
Security
compliance, and audit capabilities
Run any analytics
on the same data without movement
Scale
storage and compute independently
Designed for low-cost
storage and analytics
Redshift
EMR Athena
AI Services
ElasticsearchKinesis
Snowball
Kinesis
Video Streams
Kinesis
Data
Streams
Kinesis
Data Firehose
Snowmobile

31. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Unload
to Parquet
Amazon Redshift
New features
Speed
Scale
WLM
Concurrency
Setting
Simplicity
Amazon Lake
Formation
integration
Security
Auto Data
Distribution
Deferred
Maintenance
Snapshot
Scheduler
Spectrum
Request
Accelerator
Auto data
distribution
Elastic
resize
Concurrency
Scaling
Improving
short query
acceleration
Auto-
vacuum
Auto-
analyze

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From desktop software to web-scale SaaS

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From traditional datacenter to AWS
November 2014 - Intuit announces it’s going
“all-in” with AWS at re:Invent.
July 2018 – With the end of its transition in sight,
Intuit sells its major data center in Quincy, WA.

35. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Intuit’s growing cloud business platform
Cloud cost optimization program
$100s of Thousands
Saved per day
$100s of Millions
Prepay under management
+4 Billion
Rows processed per day per node
Time
Progress
is ~70% migrated to AWS. Focus is shifting from migration speed to efficient operations and growth.

36. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Our goals
Handle
explosive
growth in
data volume
Maximize
investment
in value-add,
not operations
Provide
deeper
insights faster,
fresher
Maintain
compliance
with SOX
regulations

37. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Scaling challenges with our previous solution
M rows
per minute
(~1M steady)
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0
6/4/20177:15:25PM
6/6/20176:38:29PM
6/8/20178:13:48PM
6/11/201711:57:59PM
6/14/201710:14:47PM
6/20/20175:05:26PM
6/22/201712:01:01AM
6/24/201712:17:26AM
6/29/20171:04:50AM
7/3/20172:21:43AM
7/6/20174:24:01AM
7/7/20177:54:18PM
7/11/201710:27:38PM
7/14/20175:35:55AM
7/16/201710:42:34PM
7/19/201712:23:23AM
7/20/201712:33:25PM
7/23/201711:33:26PM
7/26/20176:18:38AM
7/31/20175:26:14AM
8/3/20175:47:56AM
8/4/20177:22:16PM
8/7/20178:15:04PM
8/9/20178:56:14PM
8/11/20179:53:35PM
8/18/20172:14:43PM
8/21/201711:12:11PM
8/23/201711:42:26PM
8/26/20172:18:56AM
8/29/20171:02:18AM
8/31/20171:37:50AM
9/2/20172:15:31AM
9/4/20177:10.35PM
9/7/20177:53:13PM
9/10/20178:46:45PM
9/13/20175:37:06AM
9/14/201710:09:33PM
9/18/201711:16:19PM
9/21/201712:22:45AM
9/23/201712:52:31AM
9/27/20171:43:52AM
9/29/20171:35:27AM
10/2/20173:25:10AM
10/4/20177:42:26PM
10/6/20178:33:58PM
10/11/20175:46:08AM
10/12/20179:39:50PM
10/15/201710:35:26PM
10/18/201711:11:24PM
10/21/201712:05:27AM
10/24/20171:15:09AM
10/26/20171:52:47AM
10/28/20173:47:06AM
10/31/20173:16:54AM
11/2/20173:54:31AM
Batch
duration
(Minutes)
Batch
size
(M rows)
Previous solution’s performance
was constant, not accommodating
increasing data volumes.
Scaling in the datacenter
took weeks and required significant
manual effort and cost.
1000
0
900
800
700
600
500
400
300
200
100
1.5
0
1
.5

38. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Redshift performance scales to demand
Rows
per Minute
(Millions)
140
120
100
80
60
40
20
0
4500
0
4000
3500
3000
2500
2000
1500
1000
500
Batch
duration
(Minutes)
Rows
processed
(Millions)
10/1/20176:06AM
10/4/20178:01PM
10/8/20176:47PM
10/12/20175:30PM
10/17/20178:10AM
10/22/20173:16PM
10/27/20176:26AM
11/2/20174:12AM
11/5/201710:12AM
11/10/20173:19AM
11/16/201710:22AM
11/19/201710:50PM
11/23/201710:35PM
11/28/201710:53AM
12/2/20177:09PM
12/6/20172:45AM
12/10/20179:41AM
12/14/20176:34AM
12/17/201719:33PM
12/21/20173:57AM
12/25/20179:14PM
12/29/20179:53AM
1/3/20183:00AM
1/8/20184:35PM
1/12/20187:34PM
1/16/20183:04PM
1/21/201812:37AM
1/26/20188:19PM
2/5/20187:44PM
2/9/20189:28AM
2/13/201812:52PM
2/17/20184:40PM
2/21/20186:18PM
3/1/20185:13AM
3/6/20182:58AM
3/10/20183:28PM
3/14/20184:39PM
3/18/20184:40AM
3/22/20187:07AM
3/30/20187:15AM
4/4/20187:10PM
4/8/20188:50PM
4/14/20183:39PM
4/18/20185:48PM
4/30/20181:54PM
5/6/20183:22PM
5/11/20184:55PM
5/16/201810:16AM
5/28/20184:12PM
6/4/201811:44AM
6/10/201811:42AM
6/18/201811:19PM
6/25/20182:00AM
7/1/20182:17AM
7/6/20182:51AM
7/10/20187:09PM
7/14/20184:14PM
7/26/20185:57PM
8/4/20183:52PM
8/8/20181:04AM
8/12/20181:17PM
8/18/201812:08AM
8/22/20187:25PM
8/30/20185:02AM
9/4/20182:50AM
9/7/20185:00PM
9/13/20187:02PM
9/18/20181:48AM
9/25/20186:25AM
9/29/201812:24AM

39. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Intuit cloud business architecture
Data
Demarcation
Downstream
Consumers
AI/ML
Visualizations
Business
Intelligence
Stage Process Consume
Data Platform
Ingestion Processing Platform Processing Platform
Orchestration Layer
API

40. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Intuit cloud business services
Amazon
S3
Amazon
S3
Amazon
SageMaker
Amazon
QuickSight
Amazon
CloudWatch
Amazon
RDS
AWS Step
Functions
Amazon
SNS
AWS
Lambda
Amazon
EC2
AWS
Lambda
Amazon
EC2
AWS
Lambda
Stage Process Consume
Amazon Redshift

41. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Lessons learned – orchestration applications
Alternative warehouses
(MS SQL, Oracle) provide all-in-one database
application development platforms
AWS provides
an extensive collection of services to
supplement Amazon Redshift
The absence of system-native workflows can be intimidating at first.
However, the broad collection of low-overheard compute, storage, and application
development services provided by AWS allow for higher performing, more scalable,
and lower cost solutions than previously possible.
Amazon
S3
AWS
Snowball*
AWS
Batch
AWS
Lambda
Amazon
EC2
Amazon
RDS
AWS
DMS
Amazon
CloudWatch
AWS
CloudTrail
AWS
Glue
Amazon
Kinesis
Amazon
EMR
AWS Step
Functions
… and
many
more!

42. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Lessons learned
By linking Amazon Redshift with RDS PostgreSQL, the combined feature set can power a
broader array of use cases and provide the best solution for each task.
Amazon Redshift
Fast, simple, cost-effective data
warehouse that can extend queries to
your data lake.
Redshift strengths:
High performance against large data sets
Easily scaled MPP Platform
Fast, simple ingestion from Amazon S3
PostgreSQL
Amazon RDS PostgreSQL
instances provide strong affinity
to Redshift due to common
PostgreSQL code roots.
RDS PostgreSQL strengths:
Performance for many small writes
Stored Procedure support
Additional Postgres 9.x features

43. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Next steps – Concurrency Scaling for key workloads
Redshift Concurrency Scaling is expected to provide consistently fast
performance for our analysts, even with thousands of concurrent queries. All of
this with a minimal-to-no additional cost.
Our platform’s next stage of intelligence and optimization will be
derived from AI/ML applied against our data.
Query patterns that are more complex and less predictable might increase the chances of
concurrency conflicts with our key automated jobs.
Further opening the system to internal data science
teams means increasing Redshift analyst user base
several-fold.

44. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Next steps – scaling with Amazon Redshift
Unload historical data
from our largest tables
to Amazon S3 Using
Unload to Parquet.
Transparently query
unloaded Amazon S3 data
with Redshift-resident
data using Redshift
Spectrum.
Performance excels for
infrequently accessed
data when Parquet’s
columnar format is
combined with the
Redshift Spectrum
Request Accelerator.
These three features in concert allow one to seamlessly scale data outside of Amazon Redshift,
increasing flexibility of storage and compute provisioning. Specifically, it will allow us to age
older data out to S3, while keeping its retrieval seamless and performant.

45. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Redshift migration benefits
Performance
& scaling
Architecture has scaled
over 7x data volume with
no effort on our end
>20x hardware-
normalized performance
with large batches
Cost
66% reduction in
operations overhead
more than offsets slight
Opex increase
Business outcomes
>90% reduction in time-
to-insight
0 minutes of unscheduled
downtime
50% reduction in story
cycle time to implement
new features

46. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Maor Kleider
Maor@amazon.com
Jason Rhoades
Jason_Rhoades@intuit.com

47. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.