AWS Data Analytics on AWS

AWS Partner: Data Analytics on
AWS – Technical
Amey Birje
Sr AWS Partner Trainer

Course objectives
In this course, you will learn how to:
• Identify Amazon Web Services (AWS) services in the AWS analytics stack
• Describe decision points and technology selections for data analytics architectures
• Discuss the AWS Data Pipeline and the customer data analytics journey using the Data Flywheel
• Describe five AWS data analytics technical solutions:
• Modernizing a data warehouse with Amazon Redshift
• Data lakes
• Streaming data
• Data governance
• Machine learning (ML)
• Locate and use AWS Partner Network (APN) Partner resources for opportunities and training
© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved. 3

About this course
• This course is for technical professionals at APN Consulting Partner organizations
who are engaged in pre-sales discussions with customers to help architect data
analytic solutions on AWS and answer technical questions about using AWS
data analytics services.
• This 1-day course is focused on educating technical professionals with sufficient
technical knowledge on AWS data analytics services and solutions to
successfully engage with and help customers.
• This course is not designed to be a technical deep dive into AWS data analytics
services and solutions. It provides the necessary resources and learning path
towards gaining deeper knowledge into the services.
4
© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved.

Module 2: AWS Data Analytics
Portfolio

Objectives
In this module, you will learn how to:
• Understand customer challenges related to data analytics in their business
• Provide a technical overview of AWS data analytics portfolio
• Discuss technical advantages and position of data analytics solutions on AWS
• Explain how to build a data analytics pipeline
• Explain the Data Flywheel

Customer challenges and
opportunities for APN Partners

https://assets.ey.com/content/dam/ey-sites/ey-com/en_gl/topics/workforce/Seagate-WP-DataAge2025-March-2017.pdf
Data
every 5 years
There is more data than
people think
15
years
live for
Data platforms must
1,000x
scale
>10x
grows
Data is ever-growing

New realities
By making 10% more data accessible, a typical Fortune 1000
company will see a $65 million increase in net income.*
Explosion of data-
connected devices, apps,
and systems generate more
data than ever before.
Pay-as-you-go pricing
allows organizations to
analyze data to gain
insights.
*Source: Forbes Online; New Vantage Partners - Big Data Executive Survey
https://www.forbes.com/sites/cognitiveworld/2019/02/06/data-the-fuel-powering-ai-digital-transformation/#5062b36b578b
Demand growing for faster
decision making on
real-time data.

Customers need your help
13
85% of businesses want
to be data driven,
but only 37% have
been successful.
https://www.forbes.com/sites/cognitiveworld/2019/02/06/data-the-fuel-powering-ai-digital-transformation/#51efb027578b
http://newvantage.com/wp-content/uploads/2017/01/Big-Data-Executive-Survey-2017-Executive-Summary.pdf

Common data analytics challenges
14
Top four challenges
involve knowledge, skill,
security, and privacy
This is your opportunity
Data security (unauthorized access to company data)
Data privacy issues (safety of personal data)
What challenges do you see when using big data
analytics/technologies? (n=545)
Inadequate technical know-how in our company
53%
49%
48%
48%
Inadequate analytical know-how in our company
https://bi-survey.com/challenges-big-data-analytics

AWS data analytics portfolio
overview

Secure infrastructure for analytics
Customers need multiple levels of security, identity and access
management, encryption, and compliance to secure their data lake.
16
Compliance
AWS Artifact
Amazon Inspector
AWS CloudHSM
Amazon Cognito
AWS CloudTrail
Security
Amazon GuardDuty
AWS Shield
AWS Well-Architected Tool
Amazon Macie
Amazon Virtual Private
Cloud (Amazon VPC)
Encryption
AWS Certificate Manager
Private Certificate Authority
(ACM Private CA)
AWS Key Management Service
(AWS KMS)
Encryption at rest
Encryption in transit
Bring your own keys,
hardware security module
(HSM) support
Identity
AWS Identify and Access
Management (IAM)
AWS Single Sign-On
Amazon Cloud Directory
AWS Directory Service
AWS Organizations

AWS data analytics portfolio
AWS Database Migration Service (AWS DMS) | AWS Snowball | AWS Snowmobile | Amazon Kinesis Data Firehose
Amazon Kinesis Data Streams | Amazon Managed Streaming for Apache Kafka
Data movement
Amazon
QuickSight
Amazon
SageMaker
Amazon
Comprehend
Amazon
Lex
Amazon
Polly
Amazon
Rekognition
Amazon
Translate
Amazon
Pinpoint
AWS Data
Exchange
Data visualization, engagement, and machine learning
Amazon
Redshift
Amazon EMR
(Spark and Presto)
Amazon
Athena
Amazon Opensearch
Service
Amazon Kinesis
Data Analytics
AWS Glue
(Spark and Python)
Analytics
Amazon Simple Storage Service (Amazon S3)
Amazon S3 Glacier
AWS Glue
AWS Lake Formation
Data lake infrastructure and management

Data movement services
Help customers move data from on premises to the cloud
18
AWS DMS AWS Snowball AWS Snowmobile
Amazon Managed
Streaming for
Kafka
Amazon Kinesis
Data Streams
Amazon Kinesis
Data Firehose

Data lake services
Customers are constrained by volume, variety, veracity, and velocity of
on-premises data, and data silos pose a major challenge.
19
Amazon S3 Amazon S3 Glacier AWS Lake Formation AWS Glue

Analytics services
Help customers extract value out of their data
20
Amazon Redshift Amazon EMR AWS Glue
Amazon
OpenSearch
Amazon Athena Amazon Kinesis
Data Analytics

Data visualization, engagement, and
machine learning services
Help customers understand and visualize their data, and use
machine learning (ML) for advanced analytics and predictions
21
Amazon QuickSight Amazon SageMaker
AWS Data Exchange

AWS value proposition

Standards, formats, and open source
• Apache Flink
• Ganglia
• Apache HBase
• HCatalog
• Hadoop Distributed
File System (HDFS)
• Apache Hive
• Hudi
• Java
• JupyterHub
• Apache Kafka
• Apache Livy
• Apache Mahout
• MapReduce
• Apache MXNet
• MySQL
• Apache Oozie
• Apache ORC
• Apache Parquet
• Phoenix
• Apache Pig
• Presto
• Python
• PyTorch
• R
• Scala
• Apache Spark
• Sqoop
• SQL
• TensorFlow
• Tez
• Yarn
• Apache Zeppelin
• Apache Zookeeper
…and many more
23

AWS alternatives to open source
24
Amazon EMR
Amazon
OpenSearch Service
Managed Streaming
for Apache Kafka
Real-time
analytics
Kafka
Operational
analytics
Elasticsearch
Logstash
Kibana
Spark, Hive, Presto,
Flink, HBase
Hadoop
Spark

Data analytics pipeline

Data management challenges
How can customers:
• Collect a variety of data types accumulating at varying velocities?
• Collect data from numerous sources accumulating at differing velocities?
• Store massive amounts of data without running out of space?
• Cleanse and augment data quality to be analyzed?
Can they automate these steps?
26

Data analytics pipeline
Collect
Store
Process and
analyze
Visualize
Insights
Time-to-answer (latency)
Balance of throughput and cost
Data Insights
https://d1.awsstatic.com/whitepapers/architecture/AWS_Well-Architected_Framework.pdf?did=wp_card&trk=wp_card
27

Data pipeline challenges
Building a data pipeline is challenging. Customers must:
• Manage updates, patches, and software integrations
• Handle increased overhead costs plus need for support
• Maintain focus on the core task of building applications that lead to data insights
28

AWS data analytics pipeline services
29
Collect Store Process and analyze Visualize
Automate
Amazon Kinesis
Data Firehose
AWS Direct
Connect
Amazon Kinesis
Data Streams
AWS
Snowball
Amazon
S3 Glacier
Amazon S3
Amazon DynamoDB Amazon RDS
Amazon Aurora
Amazon OpenSearch
Amazon EMR
Amazon Kinesis
Data Analytics
Amazon
QuickSight
Amazon Redshift
Amazon Athena
AWS Database Migration Service
Amazon
SageMaker
AWS Glue
Amazon Managed
Streaming for
Kafka

Data Flywheel

010010010
01010001
100010100
Data Flywheel and customer journey
Build data-driven
applications
Modernize data
warehouse and
build a data lake
Migrate data and
workloads to the cloud
ü Save time
ü Save costs
Store and
manage data
ü Agility
ü Global distribution
ü Scale and performance ü New and faster insights
ü Broader access to analytics
Innovate with
machine learning
ü Better experiences
ü Deeper engagement
ü Efficient processes
Attract new customers
Generate more data
Data
https://pages.awscloud.com/data-flywheel.html

Summary
In this module, you learned about:
• Customer challenges related to data analytics
• AWS data analytics portfolio
• Technical benefits of AWS data analytics solutions
• Data analytics pipeline
• Data Flywheel

© 2020 Amazon Web Services, Inc. or its affiliates. All rights reserved. This work may not be reproduced or redistributed, in whole or in part, without prior written permission
from Amazon Web Services, Inc. Commercial copying, lending, or selling is prohibited. Corrections or feedback on the course, please email us at: aws-course-
feedback@amazon.com. For all other questions, contact us at: https://aws.amazon.com/contact-us/aws-training/. All trademarks are the property of their owners.
Thank you
33

Module 3: Data Analytics
Solutions on AWS – Part I

Objectives
• Explain data migration options from on premises to the AWS Cloud
• Describe two AWS data analytics technical solutions
• Modernizing a data warehouse with Amazon Redshift
• Data lakes
Evolution of data architecture
Traditional
data warehousing
Data lakes
on AWS
Real-time
analytics with
streaming data
Data warehouse
modernization
Data
governance
10011000010010101110010
10101110010101000010111
11011010
0011110010110010110
0100011000010
Machine
learning

Data migration options

Journey to a modern data architecture
Traditional
data warehousing
Data lakes
on AWS
Data
warehouse
modernization
100110000100
101011100101
010111001010
100001011111
011010
001111001011
0010110
010001100001
0
Types of data
Data
governance
Machine
learning
Real-time analytics
with streaming data

AWS data migration options
38
AWS Snowball
AWS Storage
Gateway
Amazon S3 Transfer
Acceleration
AWS Direct
Connect
AWS Database
Migration Service
Amazon Kinesis
Data Firehose
• File gateway
• Tape gateway
• Volume gateway
• Snowball Edge storage
optimized
• AWS Snowmobile

Solution 1: Modernizing a data
warehouse with Amazon Redshift

Traditional
data warehousing
Data lakes
on AWS
Data
warehouse
modernization
100110000100
101011100101
010111001010
100001011111
011010
001111001011
0010110
010001100001
0
Types of data
Data
governance
Machine
learning
Real-time analytics
with streaming data

Data warehouses
41

44
Traditional architecture and on-premises
data warehouse challenges
• Difficult to scale
• Long lead times for hardware procurement
• Complex upgrades are the norm
• High overhead costs for administration
• Expensive licensing and support costs
• Proprietary formats do not support newer open data formats, which results in data silos
• Data not cataloged, unreliable quality
• Licensing cost limits number of users and how much data can be accommodated
• Difficult to integrate with services and tools

Amazon Redshift
45

Amazon Redshift
A fully managed data warehouse that is highly integrated with
other AWS services. Features include:
• Optimized for high performance
• Support for open file formats
• Petabyte-scale capability
• Support for complex queries and analytics, with data visualization
tools
• Secure end-to-end encryption and certified compliance
• Service Level Agreement (SLA) of 99.9 percent
• Based on open source Postgres database
• Cost efficient
https://aws.amazon.com/redshift/pricing/
Amazon Redshift
Secure data warehouse that extends seamlessly to a data lake
46

Amazon Redshift performance features
Breaks a large job it into smaller
tasks, then distributes the tasks to
multiple compute nodes
47
Independent and resilient nodes
without any dependencies
Data from each column is stored
together so the data can be
accessed faster, without scanning
and sorting all other columns
Massively parallel processing
(MPP)
Columnar storage Shared-nothing architecture
Result: Faster processing time Result: Compression of stored
data improves performance
Result: Improves scalability

Amazon Redshift architecture
48
Client applications
Leader node
Compute Node 1 Compute Node 2
Data warehouse cluster
Java Database
Connectivity
(JDBC)
Open Database
Connectivity
(ODBC)
https://docs.aws.amazon.com/redshift/index.html
Node slices Node slices

Leader node
Responsible for communication with the client application and
compute notes
49
Amazon Redshift leader
node:
• SQL endpoint
• Metadata
• Query compilation and
optimization
• Coordinates parallel SQL
processing
• Machine learning (ML)
optimizations
Leader node
Compute node 1 Compute node 2

Compute node
• SQL running powerhouses
• Compute node can load, unload, backup, and
restore data to and from Amazon S3.
• Node clusters range from 1 to 128.
50
Runs queries in parallel and returns the result to the leader node
Leader node

Compute node slices
51
Slices are a symmetric multiprocessing (SMP) mechanism.
Slice 1 | Slice 2
Local disk Local disk
Virtual core Virtual core
7.5 GB
RAM
7.5 GB
RAM
• Partitioned into slices.
• Slices work in parallel to
complete operations.
• Virtual processors contained in
each compute node.
• Each slice is allocated an equal
amount of memory, compute
allowance, and disk space.
• Each slice operates in parallel
but can request data from
other slices.

Amazon Redshift cluster resizing:
Two approaches
52
Elastic resize
Exiting cluster is modified to add or remove nodes in
two stages.
Stage 1
• Cluster is temporarily unavailable while elastic
resize migrates cluster metadata.
• Typically completes in minutes.
• Amazon Redshift holds session connections
while queries remain queued.
Stage 2
• Session connections are reinstated and queries
resume.
• Redistributes data to node slices in the
background.
• Cluster is available for read and write
operations.
Classic resize
Can be reconfigured to different node count and
instance type.
• Might take one or more hours to complete,
depending on data size.
• Involves streaming all data from original
cluster to newly configured cluster.
• During the resize, original cluster is in read-
only mode.
• Customer charged for only one cluster.

Amazon Redshift instance types
53
https://docs.aws.amazon.com/redshift/latest/gsg/getting-started.html

Management interfaces
54
https://us-west-2.console.aws.amazon.com/redshiftv2/home?region=us-west-2#query-editor

Amazon Redshift
differentiating features
55

Amazon Redshift
differentiating features
56
Federated query
Amazon Redshift
lake house architecture

Federated query
Data warehouse Amazon Aurora
OLTP ERP CRM LOB
Integrate queries on live data in Amazon RDS for
PostegreSQL and Amazon Aurora PostgreSQL with
queries on Amazon Redshift and Amazon data lake
Reduce data moved over the network with
Amazon Redshift’s intelligent optimizer. Pushes
and distributes portions of computation directly
into remote operational databases
Benefits
• Incorporate live data into business intelligence
(BI) and reporting applications
• Ingest data into Amazon Redshift
• Query operational databases directly
• Apply transformations on the fly
• Load data into target tables without
complex ETL pipelines
57

Amazon Redshift
lake house architecture
With Amazon Redshift lake house
architecture, customers can:
• Query data in the data lake and
write data back in open formats
• Use familiar SQL statements to
combine and process data across
data stores
• Run queries on live data in
operational databases without
requiring data loading and ETL
pipelines
58
Amazon Redshift lake house queries are run by a fleet of nodes that are
owned and maintained by AWS.
https://aws.amazon.com/redshift/lake-house-architecture/

SQL clients, business intelligence tools
Leader node
Compute node 1
Node slices
JDBC/ODBC
Compute node 2
Node slices
Amazon S3 AWS Glue Data
Catalog
Amazon Redshift
lake house
Amazon Redshift
lake house fleet
1
SELECT COUNT(*)
FROM
S3.EXT_TABLE
GROUP BY…
Query
2
Query is optimized and compiled
using ML at the leader node.
Determine what is run locally and
what goes to Amazon
Redshift lake house.
3 Query plan sent
to all compute
nodes.
4 Compute nodes
obtained from the Data
Catalog; dynamically
prune partitions.
5 Each compute node issues
multiple requests to Amazon
Redshift lake house layers.
6 Amazon Redshift lake house
nodes scan Amazon S3 data.
7 Amazon Redshift lake
house projects, filters, joins,
and aggregates.
8 Final aggregations and joins
with local Amazon Redshift
tables done in-cluster.
9 Result is sent to client.

Advanced Query Accelerator (AQUA)
A new distributed and hardware-accelerated cache that makes Amazon Redshift
faster than other cloud data warehouses, without increasing cost
60
Minimizes data movement over the network
by pushing operations to Advanced Query
Accelerator (AQUA) nodes
AQUA nodes with custom AWS designed
analytics processors to make operations
(compression, encryption, filtering, and
aggregations) faster than traditional CPUs
RA3
cluster
AQUA node
Custom
AWS designed
processor
Running in parallel
Amazon Redshift managed storage
RA3
cluster
RA3
cluster
AQUA node
Custom
AWS designed
processor
AQUA node
Custom
AWS designed
processor
AQUA node
Custom
AWS designed
processor

Migration to Amazon Redshift
61

AWS SCT data extractors
63
Amazon Redshift extracts data
through local migration agents.
Data is optimized for Amazon
Redshift and saved in local files.
Files are loaded to an Amazon S3
bucket (through network or AWS
Snowball) and then to Amazon
Redshift.
Amazon
Redshift
AWS SCT S3 Bucket
Amazon S3 Amazon
Redshift
AWS SCT
Legacy data
warehouse

Use case: Equinox
64
Challenge
Their data warehouse had limited
integration, was very expensive,
and required a lot of platform-
specific domain knowledge.
They needed to reduce
administration and costs, blend
structured and semi-structured
data for analytics, and evolve into
a data lake strategy.
Solution
Equinox migrated from a legacy
data warehouse to Amazon
Redshift to combine data from
disparate sources like clickstream
data, cycling log data, club
management software, and
more.
They land data directly in an
Amazon S3 data lake and
perform analytics using Amazon
Redshift, Redshift Spectrum, and
Amazon EMR.
Benefits
Their monthly Amazon Redshift
bill is now 20% of prior yearly
maintenance of their legacy data
warehouse.
AWS data lake and analytics
reduced report delivery time
from months to days.
Equinox sees faster reports, 80% cost savings with Amazon Redshift.
https://www.youtube.com/watch?v=EvDicFx9StE

Solution 2: Data lakes

Traditional
data warehousing
Data lakes
on AWS
Data
warehouse
modernization
100110000100
101011100101
010111001010
100001011111
011010
001111001011
0010110
010001100001
0
Types of data
Data
governance
Machine
learning
Real-time analytics
with streaming data

Data lakes defined
69
• Stores all structured, semi-structured,
unstructured, and binary data at unlimited scale
• Holds curated and raw data
• Uses AWS data analytics tools for analytics
• Increases pace of innovation by extracting insights
from data
• Enables more organizational agility
• Reduces cost and delivers results with predictive
analytics and ML
Architectural approach for a centralized
enterprise data repository stored on
Amazon S3
Machine
learning
Business
intelligence
and
analytics
Data
warehousing
Data lake
Open formats
central catalog

Secure data lake on Amazon S3
70
Amazon S3
Access Points
Amazon S3
object lock
Amazon S3
object tags
Amazon S3
Block Public Access
Amazon FSx
for Lustre
• Multi-tenant bucket
• Dedicated access points
• Customer permissions
from an Amazon Virtual
Private Cloud (Amazon
VPC)
• Across AWS accounts and
Amazon S3 bucket level
• Specify public permissions
using Access Control List
(ACL) or policy
• Four settings:
• BlockPublicAcls
• IgnorePublicAcls
• BlockPublicPolicy
• RestrictPublicBuckets
• Access control, lifecycle
policies, and analysis
• Classify data with
metadata
• Use tags to filter objects
• Define replication policies
• Populate tags with AWS
Lambda functions or S3
Batch Operations
• Immutable Amazon S3
objects
• Retention management
controls
• Data protection and
compliance
https://aws.amazon.com/compliance/services-in-scope

71
IAM
Amazon CloudWatch AWS STS AWS CloudTrail
AWS KMS
Protect and secure
Machine
learning
Amazon QuickSight Amazon EMR
Amazon
Redshift
Amazon Athena
Processing and analytics
Amazon Kinesis
AWS
Direct Connect AWS Snowball
AWS DMS
AWS Data Exchange
Data ingestion
AWS Glue Amazon ES
Amazon DynamoDB
Catalog and search
Amazon API Gateway IAM Amazon Cognito
Access and user interface
Amazon S3
Central storage
Reference architecture:
Data lake on AWS

Data services – AWS Glue
72

Cleansing data
After migration, data still presents challenges:
Data is increasingly diverse
• Volume
• Variety
• Velocity
• Veracity
It accumulates rapidly
• Missing or incorrect data
• Wrong data format
• Partial missing data
Avoid unsearchable data
It must be cleansed before
analyzed by many applications
How can customers provide access to users to gain insights?

AWS Glue
74
AWS Glue Data
Catalog
Job authoring
Job running
Job workflow
§ Hive metastore compatible with enhanced functionality
§ Crawlers automatically extracts metadata and creates tables
§ Integrates with Amazon Athena, Amazon EMR, and many more
§ Run jobs on a serverless Spark platform
§ Use flexible scheduling, job monitoring, and alerting
§ Generates ETL code
§ Build on open frameworks – Python, Scala, and Apache Spark
§ Developer-centric – editing, debugging, sharing
§ Orchestrate triggers, crawlers, and jobs
§ Author and monitor entire flows and integrated alerting

AWS Glue crawlers
Amazon Redshift
Amazon DynamoDB
Amazon S3
Databases
AWS IAM role
AWS Glue crawler
JDBC
connection
NoSQL
connection
Object
connection
Built-in classifiers
MySQL
MariaDB
PostgreSQL
Amazon Aurora
Oracle
Amazon Redshift
Apache Avro
Parquet
ORC
XML
JSON and JSONPaths
AWS CloudTrail
Binary JSON (BSON)
Logs
Delimited
… growing

AWS Glue Data Catalog services
76
AWS Glue Data
Catalog
Amazon Redshift
lake house
Amazon Athena
AWS Glue ETL
Amazon EMR

Use case: Log aggregation with ETL
77
AWS service logs
Web application logs
Server logs
Amazon S3
bucket
AWS Glue
crawler
Update table partition
Create partition
on Amazon S3
Query data
AWS Glue ETL
Amazon S3
bucket
AWS Glue Data
Catalog
Amazon Athena

Data services – AWS Data
Exchange and Amazon Athena
78

AWS Data Exchange
Find diverse data in one place Analyze data Access third-party data
Find and subscribe to third-party data in the cloud
• More than 1,000 data products
• More than 80 data providers
• Download of copy of data to
Amazon S3
• Combine, analyze, and model with
existing data
• Streamlined access to data
• Minimize legal reviews and
negotiations
79

Amazon Athena
80
No setup costs Streamlined
Open
Pay per query
Interactive query service to analyze data in Amazon S3 using standard SQL
SQL
$
Zero setup costs,
point to Amazon S3
and start querying
Pay only for queries run,
save 30%–90% on
per-query costs through
compression
ANSI SQL interface,
JDBC/ODBC drivers, multiple
formats, compression types,
and complex joins and data
types
Serverless, zero
infrastructure, zero
administration,
integrated with Amazon
QuickSight

AWS Lake Formation
81

Challenges of building a secure data lake
Typical steps to build a secure data lake
Move data
2 Cleanse,
prepare, and
catalog data
3
Configure and
enforce security and
compliance policies
4
Make data available
for analytics
5
Set up
storage
1
Data engineer Data security officer Data analyst
Ingestion and cleaning Security
Analytics and machine learning

AWS Lake Formation for a secure data lake
Secure and control Collaborate and use Monitor and audit
Ingest and organize
Automates creating data
lake and data ingestion.
Sets up fine-grained
access control and data
governance.
Search and data
discovery using Data
Catalog metadata.
To protect data, all
access is checked against
set policies.
Based on data access
and governance policies,
alert notifications are
raised on policy violation
and logged.
2 3 4
1

AWS Lake Formation builds on AWS Glue
84
Blueprints
AWS Glue ETL jobs
Workflow
AWS Glue crawlers
AWS Glue Data Catalog
Connections,
databases, tables
Monitoring
Security, search, collaboration
AWS Glue
AWS Lake Formation

AWS Lake Formation benefits
85
Amazon Redshift
Amazon Athena
AWS Glue
Amazon EMR
Amazon
QuickSight
Amazon
SageMaker
AWS Lake
Formation
Blueprints ML
Transforms
Data Catalog Access
control
Amazon S3
data lake storage
Cost effective, durable storage
includes global replication
capabilities.
Simplified ingest and cleaning
enables data engineers to build
faster.
Centralized management of
fine-grained permissions
empowers security officers.
Comprehensive set of integrated
tools enables every user equally.

Data visualization with Amazon
QuickSight
86

Amazon QuickSight
87
BI service built for the cloud with pay-per-session pricing and ML insights
Scalable
Automatically scales with use and
activity, with no additional
infrastructure requirements.
Seamlessly grows with customers.
Pay monthly or annually.
With pay-per-session pricing, customers
only pay when they access their reports
and dashboards, with no upfront costs.
Pay for use
Fully managed cloud application,
meaning there's no upfront cost,
software to deploy, capacity planning,
maintenance, upgrades, or migrations.
Serverless and fully
managed Deeply integrated with data sources and
other AWS services like Amazon Redshift,
Amazon S3, Athena, Amazon Aurora,
Amazon RDS, IAM, AWS CloudTrail, and
Amazon Cloud Directory– providing
customers with everything they need for an
end-to-end cloud BI solution.
Fully integrated

Serverless data lakes and analytics
Amazon S3
AWS Glue
crawler
AWS Glue Data
Catalog
Amazon Athena
Amazon EMR
Amazon Redshift
Spectrum
Amazon
QuickSight
Amazon RDS
Web app data
Other databases
On-premises data
Streaming data

Different users solving different problems
89
AWS Glue Data
Catalog
Amazon Athena
Amazon EMR
Amazon Redshift
Spectrum
Amazon Redshift
Amazon
QuickSight
Data lake
Amazon
SageMaker
Machine learning
Amazon Redshift
Spectrum
Kibana
Apache Zeppelin
Jupyter
Tableau
MicroStrategy
Data scientists
Data engineers
Business
reporting

Use case: COVID-19
90

Use case: COVID-19 pandemic
91
Challenge
The COVID-19 pandemic has
stressed healthcare systems,
businesses, and economies. It has
disrupted the daily lives of
people around the world.
People need a solution to
capture data (diagnosis,
mortality, and recovery rates)
globally in real time, and turn the
data into insights they can share
and respond to with confidence.
Solution
Amazon worked with APN
Partners Salesforce, Tableau, and
MuleSoft to create a secure data
lake using AWS Data Exchange,
AWS Glue, Amazon Athena, and
Amazon S3 as a store of trusted
data from open source COVID-19
data providers.
Benefits
Health workers, scientists, and
decision makers can access and
compare international data to
their local data, enabling
understanding and visualization
of the impact of COVID-19
locally and globally.
This solution enables decision
making and deeper insights to
help manage and flatten the
COVID-19 curve until a vaccine is
available.

Summary
93
Traditional
data warehousing
Data lakes
on AWS
Real-time
analytics with
streaming data
Data warehouse
modernization
Data
governance
10011000010010101110010
10101110010101000010111
11011010
0011110010110010110
0100011000010
Machine
learning
Amazon Redshift
• Amazon S3
• AWS Glue
• AWS Data Exchange
• Amazon Athena
• AWS Lake Formation
• Amazon QuickSight
AWS data
migration
options

Thank you
94

Module 4: AWS Data Analytics
Solutions – Part II

Objectives
In this module, you will learn about three key types of data analytics
technical solutions on AWS:
• Streaming and real-time analytics with Amazon Kinesis
• Data governance
• Extended solution: Insights and monetization with machine learning (ML)
106
Traditional
data warehousing
Data lakes
on AWS
Real-time
analytics with
streaming data
Data warehouse
modernization
Data
governance
10011000010010101110010
10101110010101000010111
11011010
0011110010110010110
0100011000010
Machine
learning

Solution 3: Streaming and
real-time analytics with
Amazon Kinesis

Traditional
data warehousing
Data lakes
on AWS
Real-time
analytics with
streaming data
Data warehouse
modernization
Data
governance
10011000010010101110010
10101110010101000010111
11011010
0011110010110010110
0100011000010
Machine
learning
Types of data used

Streaming data defined
109
Data that is generated continuously from thousands of
data sources, sent simultaneously
Player-game interactions
Geolocation of
cars and devices
Music downloads
Website clicks
Social media streams

Common use cases: Real-time analytics
110
Milliseconds Seconds Minutes Hours
• Messaging between
microservices
• Response analytics
(web and mobile
application
notifications)
• Log ingestion
• Internet of Things (IoT)
device maintenance
• Change data capture (CDC)
• Streaming ETL into
data lakes and
data warehouse
The value of data diminishes over time

Enabling real-time analytics
111
Data streaming technology enables a customer to ingest, process, and analyze high
volumes of high-velocity data from a variety of sources, in real time.
1. 2. 3. 4. 5.

Data streaming solution challenges
Difficult to set up
Difficult to achieve high availability
Error prone and complex to
manage
Tricky to scale
Integration requires development
Expensive to maintain
Challenges of building on-premises, real-time streaming solutions:

AWS streaming data solutions
Efficiently collect, process, and analyze data streams in real time
Amazon Kinesis
Data Streams
Amazon Kinesis
Data Firehose
Amazon Kinesis
Data Analytics

Data generators: Simple streaming
data patterns
114
Data producers Streaming services Data consumers
Amazon Kinesis
Data Firehose
Amazon Kinesis
Data Analytics
Amazon Kinesis
Data Streams
Mobile and applications
Amazon Kinesis Agent
Amazon Kinesis Data Streams
Amazon CloudWatch Logs
Amazon CloudWatch Events
AWS IoT
Apache Kafka
Amazon Kinesis Producer
Library (KPL)
Amazon EMR
Amazon Redshift
Amazon Simple
Storage Service (S3)
Amazon EC2
Amazon Kinesis
Connector Library

115

116
Massively scalable, highly durable data ingestion and processing service
optimized for real-time data streaming
No upfront cost
low, pay-as-
you-go pricing
70
Data collected is
available within
milliseconds
Real-time analytics
• Dashboards
• Anomaly detection
• Dynamic pricing
3
7
Data synchronously
replicates data across
Availability
Zones in a
Region
Data can be stored up to
days
Serverless, can scale
dynamically to handle
MB to TB Thousands
to millions
each hour
of PutRecords
each second
and
https://aws.amazon.com/kinesis/data-streams/faqs/?nc=sn&loc=5

How Kinesis Data Streams works
117
Amazon Kinesis
Data Analytics
Amazon EC2
AWS Lambda
Input
Output
Spark on Amazon EMR
Amazon Kinesis
Data Streams
Capture and send data Ingest and store data
streams for processing
Build custom, real-time
applications
Analyze streaming data
using BI tools

Kinesis Data Streams architecture
118
Amazon EC2 instances
Client
Mobile client
Traditional
server
Data producers
Shard 1
Shard 2
Shard N
Amazon Kinesis
Data Stream
EC2 instance
EC2 instance
Data consumers
Amazon Redshift
Amazon S3
Amazon Kinesis
Data Firehose
Amazon EMR
Amazon DynamoDB
Shard 1
Data record
• Sequence #
• Partition Key
• Data blob
Data stream
Amazon Kinesis
Data Firehose
Amazon Kinesis
Data Analytics

Kinesis Data Streams provisioning
119

Amazon Kinesis Data Firehose
120

How Kinesis Data Firehose works
121
Amazon Kinesis
Data Firehose
Input
Output
Splunk
Amazon Redshift
Amazon S3
Amazon
Elasticsearch Service
Capture and send data Prepares and loads data
continuously to the
selected destinations
Durably store the data
for analytics
Analyze streaming data
using analytics tools

Kinesis Data Streams and
Kinesis Data Firehose
122
Characteristics Amazon Kinesis Data Streams Amazon Kinesis Data Firehose
Processing time As fast as 70 milliseconds after ingestion Between 60–900 seconds
Stream storage and
duration
In shards, default 24 hours and up to 7
days
Max buffer size 128 MB and max time 900
seconds
Data transformation and
conversion
None Uses AWS Lambda and AWS Glue
Data producer
Amazon Kinesis Agent, applications using Amazon Kinesis Producer Library (KPL), AWS SDK
for Java, Amazon CloudWatch Logs and CloudWatch Events, AWS IoT
Data consumer
AWS Lambda, Amazon Kinesis Data
Analytics, Amazon Kinesis Data Firehose,
Applications using the Kinesis Client Library
(KCL) and SDK for Java
AWS Lambda, Amazon Kinesis Data Analytics,
and Kinesis Data Firehose, apps using the KCL
and SWK for Java, Amazon S3, Amazon
Redshift, Amazon ES, Splunk, and Amazon
Kinesis Data Analytics
Data compression None gzip, Snappy, Zip, or no data compression
https://aws.amazon.com/kinesis/data-firehose/faqs/?nc=sn&loc=5

When to use Kinesis Data Streams and
Kinesis Data Firehose
123
Amazon Kinesis
Data Firehose
For data streaming applications with massive ingestion requirements
• Requires data to be sent to consumer analytics services for millisecond
response time
• Massively scalable
• Data retention time ranging from hours to days
• Example: Real-time gaming
Amazon Kinesis
Data Streams
For data streaming applications that require near real-time responses in seconds
• Need for data augmentation, data transformation, or data compression
• Need to save data to Amazon S3, Amazon Redshift, Amazon ES, Splunk, or
send data to Amazon Kinesis Data Analytics for analytics
• Example: Log analytics

Amazon Kinesis Data Analytics
124

Amazon Kinesis Data Analytics
125
Input
Amazon Kinesis
Data Analytics Output
Capture streaming data
with Amazon MSK,
Amazon Kinesis Data
Streams, Amazon Kinesis
Data Firehose, or other
data sources
Query and analyze
streaming data
Send processes data
to analytics tools to
create alerts and
respond in real time

Use case: Clickstream analytics
s127
Amazon Kinesis
Data Firehose
Input Output
Amazon Kinesis
Data Firehose
Amazon Kinesis
Data Analytics
Amazon Redshift
Evolve from batch processing to real-time analytics
Websites send
clickstream data
Collects the data and
sends to Kinesis Data
Analytics
Processes data in
near-real time
Loads processed
data into
Amazon Redshift
Runs analytics
models to identify
content
recommendations
Readers see
personalized content
suggestions and
increase
engagement

Case study: Epic Games
130
Challenge
Needed a way to process and analyze
over 100 PB of data (125 million
events each minute) ingested from
game clients and game servers to
understand and adapt to player
engagement.
Solution
Epic Games turned to AWS for an
Amazon S3 data lake in combination
with Amazon EMR, Amazon EC2, and
Amazon Kinesis.
Benefits
The data provides a constant
feedback loop for designers, and an
up-to-the-minute analysis of gamer
satisfaction to drive gamer
engagement.
Continually improves Fortnite for 250+ million players globally
https://aws.amazon.com/solutions/case-studies/EPICGames/

Solution 4: Data governance

Traditional
data warehousing
Data lakes
on AWS
Real-time
analytics with
streaming data
Data warehouse
modernization
Data
governance
10011000010010101110010
10101110010101000010111
11011010
0011110010110010110
0100011000010
Machine
learning
Types of data used

Challenges of data in data lakes
• Securing data
• Auditing data usage
• Managing data access
• Safeguarding sensitive data and PII
• Maintaining regulations and mandates
134

Data security and governance
© ENTERPRISE STRATEGY GROUP, 2019.
With big data comes big responsibility.
More than one in three companies cite data privacy and governance as
a hurdle to both digital transformation and IoT initiatives
34% 37%
of IT decision makers cite ensuring data
governance/privacy as one of their
organization’s biggest digital
transformation challenges
of IT decision makers cite ensuring
security/compliance upon movement of
data as one of their most important IoT
priorities over the next 18–24 months
https://www.esg-global.com/hubfs/ESG-Infographic-IT-Spending-Intentions-2019.pdf

Resolving PII dangers
136
Personally
identifiable
information
(PII)
Consumer
consent
violation
Data
breach
Spyware
Unsecured
devices
Rogue
agents
Second-
party
misuse
Espionage
External
hacking
• Do these issues need to be
resolved?
• Is there a solution architecture
that solves all PII issues?
• What best practices can be
used to mitigate PII dangers?

Amazon Macie
137
Amazon Macie
Continually evaluate
Amazon S3
environment
Discover sensitive
data
Take action
Enable Amazon
Macie with one-click
in the AWS
Management
Console or with a
single API call
Automatically
generates an
inventory of
Amazon S3 bucket
and details on the
bucket-level security
and access controls
Analyzes bucket
using ML and
pattern matching to
discover sensitive
data, like PII
Generates findings
and sends to
Amazon
CloudWatch Events
for integration into
workflows and
remediation actions
• Financial
• Personal
• National
• Medical
• Credentials and secrets

De-identified data lake (DIDL) on AWS
A de-identified data lake (DIDL) is an architectural approach that reduces the risks
associated with managing data, particularly personally identifiable information (PII).
Benefits
Reduce risk
• Remove PII before it enters a data lake
Understand all the data
• Create a Data Catalog of an entire data lake
Reduce compliance costs
• Automate the discovery, classification, de-identification,
and ongoing monitoring of data across an organization
Turn data into an asset, not a liability
• Enable a broader set of governed analytic and machine learning use cases

Masking PII data
139
Email Customer ID Transcript
csalazar@example.com 19664 Just talked to Carlos Salazar
mary@example.com 23423 Mary’s SSN is 000000000
mateo@example.com 99644 Mateo is moving to Nevada
NA 02945 It is expected to rain tomorrow
Email Customer ID Transcript
4t34gttt 7462391 Just talked to Jane Roe
44e5325 1239474 Jorge’s SSN is 666666666
0we&yrw 9983487 Sofia is moving to Texas
NA 3344325 It is expected to rain tomorrow
Email ID Name, SSN, State

Extended solution 5: Insights and
monetization with ML on AWS

Traditional
data warehousing
Data lakes
on AWS
Real-time
analytics with
streaming data
Data warehouse
modernization
Data
governance
10011000010010101110010
10101110010101000010111
11011010
0011110010110010110
0100011000010
Machine
learning
Types of data used

Data lakes and machine learning
Machine learning requires:
• More data: Collect all types of data
• Flexibility: Define schema during analysis
• Scalability: Scale storage and compute (CPU or
GPU) independently
• Data transformation and processing: Run a broad
set of processing and analytics on the
same data without movement
• Security: Networking, identity, encryption, and
compliance
OLTP ERP CRM LOB
Data warehouse
Business analytics
10011000010010101
11001010101110010
10100001011111011
010
00111100101100101
10
0100011000010
Data lake
Devices Web Sensors Social
Data Catalog
AI and
machine learning
Data warehouse
queries
Big data
processing
Interactive Real time

Amazon SageMaker
143
Machine learning at enterprise scale
Build
Train and tune
Deploy and manage
Notebooks for
common problems
High-performance
algorithms
• Managed Jupyter for enterprise data science
• Sample notebooks for most common use cases
• Single-pass, streaming training algorithms
One-click training Hyperparameter
optimization
One-click
deployment
Fully managed
elastic hosting
• Training models at scale without DevOps
assistance
• ML on ML to optimize hyperparameters
• Deploy to production with a single call
• Fully managed, production-grade inferences
https://aws.amazon.com/machine-learning/?nc2=h_ql_prod_ml

Machine learning resources
• Fundamental digital course on
how SageMaker mitigates the
core challenges of
implementing an ML pipeline
• Duration: 30 minutes
• https://www.aws.training/Detai
ls/Video?id=49646
145
• Explore how to use the
machine learning pipeline to
solve a real business problem
(intermediate)
• Duration: 4 days
• https://www.aws.training/Sessi
onSearch?pageNumber=1&cou
rseId=38910
• Learn to solve real-world use
cases with machine learning
(intermediate)
• Duration: 1 day
• https://www.aws.training/Sessi
onSearch?pageNumber=1&cou
rseId=40748
AWS Foundations: How Amazon
SageMaker Can Help
Practical Data Science with
Amazon SageMaker
The Machine Learning Pipeline
on AWS
https://partnercentral.awspartner.com/LmsSsoRe
direct?RelayState=%2flearningobject%2fcurriculu
m%3fid%3d25521
AWS STP: Machine Learning (ML) on
AWS for ML Practitioners - Technical

Use case: Next Caller
146
https://www.youtube.com/watch?v=K27WjYwyqw8&list=PLhr1KZpdzukdeX8m
Q2qO73bg6UKQHYsHb&index=1&did=ta_card&trk=ta_card

2022 – re:Invent – What’s new in Analytics?
© 2022 Amazon Web Services, Inc. or its Affiliates. All rights reserved. What’s new
Amazon OpenSearch
• Serverless (Preview)
Redshift
• Multi-AZ for RA3 clusters (Preview)
• Auto-copy from S3 (Preview)
• Integration for Apache Spark
• Amazon Aurora zero-ETL
• Dynamic Data Masking (Preview)
• AWS Backup support
• Streaming Ingestion for Kinesis
Data Streams (Preview)
• Centralized access control with
AWS Lake formation (Preview)
AWS Glue
• Data Quality (Preview)
• AWS Glue 4.0
• Custom visual transforms
• AWS Glue for Ray (Preview)
Amazon Athena
• Spark support
QuickSight
• Automated data preparation
• Paginated Reports

Summary
148
Traditional
data warehousing
Data lakes
on AWS
Real-time
analytics with
streaming data
Data warehouse
modernization
Data
governance
10011000010010101110010
10101110010101000010111
11011010
0011110010110010110
0100011000010
Machine
learning
• Kinesis Data Streams
• Kinesis Data Firehose
• Kinesis Data Analytics
Amazon Macie Amazon SageMaker

Thank you

Module 5: APN Partner
Opportunities and Resources

Objectives
• Describe how to collaborate with AWS for data analytics
• Describe AWS Data and Analytics resources for APN Partners:
• Competency categories
• AWS Immersion Days
• AWS Certified Data Analytics and learning resources
• Access the AWS Marketplace
• Perform the calls to action

APN Partners and
AWS for Data Analytics

Discounting and funding programs
Enterprise
Discount
Program
(EDP)
Migration
programs
POC funding

AWS Data and Analytics Competency
categories
Data Analytics
Platforms
NoSQL/New SQL
Data Integration and
Preparation
Business Intelligence
(BI) and Data
Visualization
Data Governance and
Security
Provide a set of integrated tools to solve data
analytics challenges within a standard framework
Provide highly scalable databases that
organize data into a structure
Enable customers to move and consolidate data
from disparate sources, transform it, and
prepare it for analytics
Help customers turn raw data into actionable business
information, such as reporting, dashboards, and data visualization
Help customers discover, categorize, and control their data
205

AWS data analytics solutions and
Immersion Days

AWS Data Lab program
• The AWS Data Lab program offers accelerated joint engineering
engagements between a team of customer builders and AWS
technical resources to create tangible deliverables that accelerate data
and analytics modernization initiatives.
• Two offerings:
Design Lab
Focus on real-world
architectural design
Build Lab
Focus on providing
guidance with building
a functioning
prototype with a
customer team
Duration
Half day to 5 days
Location
Virtual or AWS Data Lab hub – Seattle, NYC,
Herndon (VA), London, Bangalore
Cost
Free. Reach out to your APN support
team for more information.
210
https://aws.amazon.com/aws-data-lab/

AWS Immersion Days
Designed to help APN Advanced and Premier Consulting Partners deliver technical data analytics
workshops to their customers and help grow their businesses
Data Engineering
Immersion Day
Build a serverless data lake
solution on AWS including
modules focusing on
ingestion, hydration,
exploration, and consumption
https://aws.amazon.com/partners/immersion-days/
Amazon EMR
Immersion Day
Focus on unique facets of
Amazon EMR for big data
workloads
Database Migration
Immersion Day
Give your customers a head
start with the AWS Database
Migration Service and the
Schema Conversion Tool
… and many more.
Benefits: Access to technical workshop content, AWS usage credits, Market Development Funds (MDF)
opportunities, and support from AWS teams
211

Data solutions in AWS
Marketplace

AWS Marketplace
https://aws.amazon.com/marketplace/search/results?searchTerms=data+and+analytics
213

Use case: Modern data warehouse

AWS Certified data analytics and
learning resources

AWS Technical Professional Learning Path

Why I got AWS certified
Customers talk about the value of AWS certification

Partner Cast: Analytics
219

Call to action

Build a data analytic practice on AWS
Build packaged
solutions
Know your Partner
Solutions Architect
Ask for customer
references
Engage with AWS
service teams
Develop customer
workshops
Achieve an APN
competency

Call to action
Use the Data Flywheel
to perform
assessments
Work with your
Partner team to
schedule an
Immersion Day for
your customers
View the analytics
customer case studies
https://aws.amazon.com/
big-data/datalakes-and-
analytics/
Create a specialized
service around one of
the analytics services
Participate in the
AWS Data Lab
https://aws.amazon.com/
aws-data-lab/
Prepare for the AWS
Data Analytics –
Specialty certification
Build relationships
with APN teams for
funding opportunities
for your marketing
and sales efforts
222

Thank You

AWS Data Analytics on AWS

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