Organizations cannot afford to have a data warehouse that scales slowly or requires a trade-off between performance and concurrence. Amazon Redshift scales to provide consistently fast performance with rapidly growing data and high user and query concurrence. In this session, we highlight Amazon Redshift’s current features and those that are coming soon. Next, we discuss how your Amazon Redshift data warehouse and Amazon S3 data lake enable you to scale storage and compute resources automatically and on demand. We also demo Amazon Redshift’s intelligent maintenance and administration operations that ensure your clusters perform at any scale.

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What’s new with Amazon Redshift
Dennis J. Waldron
Principal Business Development Manager, Amazon Redshift
AWS
A D B 2 0 3

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

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process more than 2 Exabytes of data
Most popular Fastest
More than 15K
customers

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AWS received a score of 5/5 (highest score possible) in the market presence category, use cases, ability to execute, and
road map criteria.
AWSrecognizedasaleaderindatawarehousingandanalyticsbyForresterandGartner
Gartner Magic Quadrant for Data Management
Solutions for Analytics, 2018Forrester Wave™ Cloud Data Warehouse Q4 2018

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Based on the cloud DWH benchmark derived from TPS-DS 30 TB dataset, 4-node cluster
Redshift Vendor 1 Vendor 2
Queries Per Hour
(Higher is better)

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Fastest Most cost-effective
up to 75%
Use RI to reduce the price up to 75% with Reserved Instances (RIs)

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Fastest Most cost-effective Integrates with your data lake
Amazon
Redshift
Amazon
Redshift

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What’s new with Amazon Redshift?

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Unload
to Parquet
Amazon Redshift: Newly launched features
Speed
Scale
Dynamic WLM
ConcurrencySimplicity
AWS Lake
Formation
integration
Security
Auto-Vacuum
& Auto-
Analyze
Auto Data
Distribution
Deferred
Maintenance
Snapshot
Scheduler
Spectrum
Request
Accelerator
10x average
performance
improvement
Elastic resize
Concurrency
Scaling
Improving short
query
acceleration
Stored
procedures

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Concurrency Scaling
Backup
Amazon Redshift automatically adds transient clusters,
in seconds, to serve sudden spikes in concurrent requests with consistently
fast performance. No hydration required.
Caching Layer
How it works:
All queries go to the leader node;
user only sees less wait
for queries.
When queries in designated WLM
queue begin queuing, Amazon
Redshift automatically routes them
to the new clusters, enabling
Concurrency Scaling automatically.
Amazon Redshift automatically spins
up a new cluster, processes waiting
queries and automatically shuts
down the Concurrency Scaling
cluster.
1
2
3
For every 24 hours that your main
cluster is in use, you accrue a one-
hour credit for Concurrency
Scaling. This means that
Concurrency Scaling is free for >
97% of customers.
New!

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Enabling Concurrency Scaling

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For every 24 hours your main cluster
is in use, we’ll provide a one-hour
credit for concurrent cluster usage
97% of users will never see a
charge for auto-scale resources
0
2000
4000
6000
8000
10000
12000
5 40 80 120 150 180
QueriesperHour(QpH)
Number of concurrently active users
Throughput scales linearly
Amazon Redshift’s throughput scales linearly with
concurrent users

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Concurrency Scaling is a powerful new feature of
Amazon Redshift. We were really impressed by the
performance of the feature, especially with its
ability to instantly add transient capacity with
nothing to manage on our end. As Redshift
administrators at Yelp, we think that Concurrency
Scaling will keep our many users happy, even under
peak load. We’re excited that Concurrency Scaling
provides the flexibility to handle significant variance
in our workloads over the course of a day.
Shahid Chohan
Software Engineer, Yelp
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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Amazon Redshift elastic resize
New!
Amazon Redshift
Cluster
Amazon Redshift
Managed S3
JDBC/ODBC
1
2
3
Leader Node
Backup
How it works:
Amazon Redshift updates the snapshot on
S3 with the most recent data.
New nodes are added (for scaling up) or
removed (for scaling down) during this
period.
The cluster is fully available for read and
write queries. Queries that were being held
are queued for execution automatically.
1
2
3
Scale your Amazon Redshift
clusters up and down in
minutes to get the optimal
performance.

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Ease of use
• Auto Analyze (GA): Automatically collects table statistics to deliver enhanced query
performance
• Auto data distribution (GA): Automatically selects table distribution style based on table
size
• Auto Vacuum Delete (GA): Automatically re-sorts and reclaims space from deleted rows,
improving performance and space utilization
• Snapshot scheduler enhancements (GA): Provides more control over automated snapshot
schedule and allows setting snapshot expiration date, and bulk removal of expired manual snapshots
• Stored procedures in Amazon Redshift (GA): Makes migration to Amazon Redshift easier;
you can bring your existing stored procedures
New!
New!
New!
New!
New!

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New!

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Amazon Redshift: Auto Vacuum
• Amazon Redshift automatically runs the
VACUUM DELETE operation to reclaim
disk space occupied by rows that were
marked for deletion by previous UPDATE
and DELETE operations during idle
periods.
• It defragments the tables to free up
consumed space and improves
performance for your workloads.
Vacuum Delete
Less storage used,
higher performance

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Amazon Redshift: Auto Vacuum
https://twitter.com/esh/status/1076239047813545984

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stored procedures
New!

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Amazon Redshift integrates
seamlessly with your data
lake
DATE data type
Retrieving metadata for late-binding views
Support 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
S U M M I T

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Amazon Redshift Spectrum
Amazon Redshift Spectrum
query engine
Query across Amazon
Redshift and S3
Amazon
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
Spectrum Request Accelerator
→ Unload to Parquet
Coming
Soon!

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Amazon Redshift is scalable
Amazon Redshift Spectrum: Exabyte data lake query in
under three minutes
Compression
Columnar file format
Scanning with 2,500 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 bookstore. An
author released her 8th book in a popular series, and you
need to figure out how many copies to order.
Amazon S3
Amazon 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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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
Amazon
Redshift
EMR Athena
AI services
ElasticsearchKinesis
Snowball
Kinesis
Video Streams
Kinesis
Data
Streams
Kinesis
Data Firehose
Snowmobile

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Integration with AWS Lake Formation Coming Soon!
KinesisSocial Web
Sensors Devices
LOBCRM
ERPOLTP
IAM AWS
KMS
Data
Catalog
Amazon
Athena
Amazon EMR
Amazon Elasticsearch Service
AI services
Amazon
QuickSight
Amazon
Redshift

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Built-in security, w/o extra cost
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

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Unload Amazon Redshift data as
Parquet to Amazon S3
Amazon Redshift now
supports exporting data
to Amazon S3 in Parquet
format. This makes
sharing data across the
data lake easier and
faster, without
conversion.
supported by
Amazon EMR, Amazon
Athena,
and Amazon Redshift.
Amazon Redshift Unload command
now supports Parquet format. This
allows data in Amazon Redshift to be
exported as Parquet to be processed
by Amazon EMR or Amazon Athena
without any data conversion.
The feature is in preview now and
GA in Q3 ’19.
Amazon EMR
Amazon
Redshift
Amazon
Athena
Amazon S3
AWS Glue
Private
Preview

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Focus areas for 2019…
• Concurrency, elasticity, performance, zero tuning
Amazon Redshift performance & out-of-box performance improvement
Automate remaining tuning knobs
• Ease of use, zero admin, migration
Simplify console, operation, management
• Data lake
More data lake integration for security and access control
Leverage the scale of data lake and different processing engines
• Security, availability
Azure AD integration, more flexible access control

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Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Dennis J. Waldron
walddenn@amazon.com