The document discusses building data lakes and analytics on AWS. It provides an overview of challenges posed by big data including volume, velocity, variety and veracity of data. It then describes how AWS services like S3, Glue and Athena can help address these challenges by allowing quick ingestion and storage of raw data in its original format. The document also discusses best practices for preparing and analyzing data in the lake using services like EMR, Redshift and SageMaker to derive insights and drive machine learning models.

1. Build Data Lakes and Analytics on AWS: Patterns
Best Practices

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Big Data: Different forms of challenges
VisualizationVariability
Volume Velocity Variety Veracity Value

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Challenges are often driven by:
https://www.promptcloud.com
https://john-popelaars.blogspot.com
https://ww.signiant.com
https://www.linkedin.com/pulse/world-today-data-rich-information-poor-
guru-p-mohapatra-pmp/
Data growth
faster than ever
Data variety is
increasing

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AWS Data Lake helps address this
Quickly ingest and store
any type of data
Insights and security,
together …
Run the right tool for the
right job without manually
copying data around

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Data Lakes from AWS
Analytics
Machine
Learning
Real-time data
movement
Traditional
Data Lake
on AWS
data movement
Ingestion
Intelligence
Storage
Catalog
Variety of
ingestion tools
Decoupled
analytics from
storage/catalog

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What data do I have?

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What data do I have?
Gartner:
“Through 2018, 80% of data lakes will not include
effective metadata management capabilities, making them
inefficient.”
”Metadata Is the Fish Finder in Data Lake”
Data Lake on AWS
Storage | Archival Storage | Data Catalog

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AWS Glue components
Job AuthoringData Catalog Job Execution
Apache Hive Metastore compatible
Integrated with AWS services
Automatic crawl and discover data
Discover
Auto-generates ETL code
Python and Apache
Spark
Edit, debug, and share
Develop
Serverless execution
Flexible scheduling
Monitoring and alerting
Deploy

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What can crawlers discover?
IAM Role
AWS Glue Crawler Databases
Amazon
Redshift
Amazon S3
JDBC Connection
Object Connection
Built-in classifiers
MySQL
MariaDB
PostreSQL
Aurora
Oracle
Amazon Redshift
Avro
Parquet
ORC
XML
JSON & JSONPaths
AWS CloudTrail
BSON
Logs
(Apache (Grok), Linux(Grok), MS(Grok), Ruby, Redis,
and many others)
Delimited
(comma, pipe, tab, semicolon)
< ALWAYS GROWING…>
Create additional custom
classifiers
Amazon
DynamoDB
NoSQL Connection

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But I have my own data formats …?
− There is a custom classifier for that …
Row-Based
GROK Classifier
A grok pattern is a
named set of
regular expressions
(regex) that are used
to match data one
line at a time.
XML
XML Classifier
XML tag that
defines a table row
in the XML
document.
JSON
JSON Classifier
JSON path to the
object, array, or value
that defines a row of
the table being
created. Type the
name in either dot or
bracket JSON syntax
using AWS Glue
supported operators

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Other ways of populating the catalog
Call the AWS Glue CreateTable API
Create table manually DDL statement (in Amazon Athena or Amazon EMR)
Apache Hive
Metastore
AWS GLUE ETL AWS GLUE
DATA CATALOG
Import from Apache Hive Metastore

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How do I hydrate my data lake?

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How do I drive value?
Amazon SageMaker
AWS Deep Learning AMIs
Amazon Rekognition
Amazon Lex
AWS DeepLens
Amazon Comprehend
Amazon Translate
Amazon Transcribe
Amazon Polly
Amazon Athena
Amazon EMR
Amazon Redshift
Amazon Elasticsearch Service
Amazon Kinesis
Amazon QuickSight
AWS Direct Connect
AWS Snowball
AWS Snowmobile
AWS Database Migration Service
AWS IoT Core
Amazon Kinesis Data Firehose
Amazon Kinesis Data Streams
Amazon Kinesis Video Streams
Data Lake on AWS
Storage | Archival Storage | Data Catalog
AnalyticsMachine learning
Real-time data movementTraditional data movement

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Ingest data based on the type of data
Open and comprehensive
• Data movement from on-premises
datacenters
• Dedicated network connection
• Secure appliances
• Ruggedized shipping container
• Database migration
• Gateway that lets applications write to the cloud
• Data movement from real-time sources
• Connect devices to AWS
• Real-time data streams
• Real-time video streams
AWS Direct Connect
AWS Snowball
AWS Snowmobile
AWS Database Migration Service
AWS Storage Gateway
AWS IoT Core
Amazon Kinesis Data Firehose
Amazon Kinesis Data Streams
Amazon Kinesis Video Streams
Data movement from
real-time sources
Data movement from
your datacenters
Amazon S 3
Amazon Gl ac ier
AWS Gl u e

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Real-time data movement and data lakes on AWS
Amazon
Kinesis Data
Firehose
AWS Glue
Data Catalog
Amazon
S3 Data
Data Lake
on AWS
Amazon
Kinesis Data
Streams
Data definitionKinesis Agent
Apache Kafka
AWS SDK
LOG4J
Flume
Fluentd
AWS Mobile SDK
Kinesis Producer Library

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IMPORTANT: Ingest data in its raw form …
Open and comprehensive
Amazon S 3
Amazon Gl ac ier
AWS Gl u e
• Store the data in its raw form:
• BEFORE
• Transforming
• Analyzing
• Manipulating
• Doing … anything … to it
CSV
ORC
Grok
Avro
Parquet
JSON
• This becomes your source of record you can
always go back to …
• Lifecycle policies allow you to shift it to
warm and cold storage.

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Tiered storage to optimize price / performance
Lowest cost
• Tiered storage to optimize price/performance
• Amazon S3 Standard
• Amazon S3 Standard—Infrequent Access
• Amazon S3 One Zone—Infrequent Access
• Amazon Glacier
• Migrate between tiers based on lifecycle policies
• Store data at $0.023*/GB/month with Amazon
S3
• Store data at $0.004*/GB/month with Amazon
Glacier
Amazon S3
Standard
Amazon S3
Standard
Infrequent Access
Amazon S3 One
Zone-IA
Amazon Glacier
Active Infrequent Archive

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Datasets in the Lake?
Raw datasets – immutable datasets that you can always
go back to.
• Abstract out the complexities of how the data is stored
through the catalog and SerDes
Optimizing Analytics and Machine Learning:
Curated datasets – query-optimized for consumption
across wide number of tools

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Preparing raw data for consumption
Raw data stored in Data Lake:
Preparation:
Normalized
Partitioned
Compressed
Storage Optimized
Extract – Load – Transform
Raw
Ingestion
Curated
DataSets
Data Catalog
ELT

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Which tool should I use to analyze my
data?

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How do I drive value?
Amazon SageMaker
AWS Deep Learning AMIs
Amazon Rekognition
Amazon Lex
AWS DeepLens
Amazon Comprehend
Amazon Translate
Amazon Transcribe
Amazon Polly
Amazon Athena
Amazon EMR
Amazon Redshift
Amazon Elasticsearch Service
Amazon Kinesis
Amazon QuickSight
AWS Direct Connect
AWS Snowball
AWS Snowmobile
AWS Database Migration Service
AWS IoT Core
Amazon Kinesis Data Firehose
Amazon Kinesis Data Streams
Amazon Kinesis Video Streams
Data Lake
on AWS
Storage | Archival Storage | Data Catalog
AnalyticsMachine Learning
Real-time dataTraditional movementdata movement

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Different tools for different users…
Business
Reporting
Data
Catalog
Central
Storage
SagemakerMachine Learning/Deep Learning
Data Scientists
Data Engineer

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Amazon Athena – interactive analysis
Interactive query service to analyze data in Amazon S3 using standard SQL
No infrastructure to set up or manage and no data to load
Ability to run SQL queries on data archived in Amazon Glacier (coming soon)
$ SQL
Query instantly
Zero setup cost; just
point to Amazon S3
and start querying
Pay per query
Pay only for queries run;
save 30%–90% on per-
query costs through
compression
Open
ANSI SQL interface,
JDBC/ODBC drivers, multiple
formats, compression types,
and complex joins and data
types
Easy
Serverless: zero
infrastructure, zero
administration
Integrated with Amazon
QuickSight

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Amazon EMR – big data processing
Analytics and ML at scale
19 open-source projects: Apache Hadoop, Spark, HBase, Presto, and more
Enterprise-grade security
$
Latest versions
Updated with the latest
open source frameworks
within 30 days of release
Low cost
Flexible billing with per-
second billing, Amazon
EC2 Spot, Reserved
Instances, and Auto
Scaling to reduce costs
50%-80%
Use Amazon S3 storage
Process data directly in
the Amazon S3 data lake
securely with high
performance using the
EMRFS connector
Easy
Launch fully managed
Hadoop & Spark in minutes;
no cluster setup, node
provisioning, cluster tuning
Data Lake
100110000100101011100
1010101110010101000
00111100101100101
010001100001

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Hadoop / Spark Analytics on AWS
YARN (Hadoop Resource Manager)
NoSQLMachine
learning
Real-timeInteractiveScriptBatch
Data Lake
on AWS
Amazon S3
Amazon EMR
Managed Hadoop / Spark
Object storage

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Fitting this into the Common Data Catalog
Amazon S3
Interactive Spark cluster
Amazon EMR
Amazon EMR
EMRFS
HDFS
Transient ETL job
Source of Truth
EMRFS
HDFS
Describes the data
MySQL DB
instance
Unifieddataview
AWS Glue
Data Catalog
Stores the data
…

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Amazon Redshift – data warehousing
Fast, powerful, simple, and fully managed data warehouse at 1/10 the cost
Massively parallel, scale from gigabytes to petabytes
Fast at scale
Columnar storage
technology to improve I/O
efficiency and scale query
performance
$
Inexpensive
As low as $1,000 per
terabyte per year, 1/10 the
cost of traditional data
warehouse solutions; start
at $0.25 per hour
Open file formats Secure
Audit everything; encrypt
data end-to-end;
extensive certification and
compliance
Analyze optimized data
formats on the latest SSD,
and all open data formats in
Amazon S3

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Data warehouse …
Amazon Redshift Data Warehouse Relational data
Gigabytes to petabytes scale
Reporting and analysis
Schema defined prior to data load
AWS
Glue ETL
On Prem
Amazon QuickSight
Existing or new
BI tool
Redshift
COPY

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A Data Lake is not an Enterprise Data Warehouse
Complementary to EDW (not replacement) EDW can be sourced from Data Lake
Schema on read (no predefined schemas) Schema on write (predefined schemas)
Structured/semi-structured/Unstructured data Structured data only
Fast ingestion of new data/content Time consuming to introduce new content
Data Science + Prediction/Advanced Analytics + BI use
cases
BI use cases
Data at low level of detail/granularity Data at summary/aggregated level of detail
Loosely defined SLAs Tight SLAs (production schedules)
Flexibility in tools (open source/tools for advanced
analytics)
Limited flexibility in tools (SQL only)
Elastic storage and compute capacity – decoupled
Explicitly sized environments, compute and storage
scaled in linearly
Data Lake EDW

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Amazon Redshift Spectrum
Extend the data warehouse to exabytes of data in Amazon S3
data lake
Amazon S3
Data Lake
Amazon
Redshift data
Amazon Redshift
Spectrum
query engine
Exabyte Redshift SQL queries against Amazon S3
Join data across Redshift and Amazon S3
Scale compute and storage separately
Stable query performance and unlimited concurrency
CSV, ORC, Grok, Avro, & Parquet data formats
Pay only for the amount of data scanned

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A m a z o n R e d s h i f t S p e c t r u m
Q u e r y y o u r D a t a L a k e
Amazon
Redshift
JDBC/ODBC
...
1 2 3 4 N
Amazon Redshift
Spectrum
Scale-out serverless compute
AWS Glue Data Catalog
COPY
commands
Hot data
Query directly
on Data Lake

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Data Lakes extend the traditional data warehouse
Data warehouse
Business intelligence
OLTP ERP CRM LOB
• Relational and nonrelational data
• TBs–EBs scale
• Diverse analytical engines
• Low-cost storage & analytics
Devices Web Sensors Social
Data lake
Big data processing,
real-time, machine learning

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Machine Learning & Big Data

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Big Data driving Machine Learning
Better
Decisions
Object Storage
Databases
Data warehouse
Streaming analytics
BI
Hadoop
Spark/Presto
Elasticsearch
Better
Products Machine Learning
Deep Learning/ AI
More
Users
More
Data
Click stream
User activity
Generated content
Purchases
Clicks
Likes
Sensor data

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Agility in Machine Learning
Amazon SageMaker
AWS Deep Learning AMIs
Amazon Rekognition
Amazon Lex
AWS DeepLens
Amazon Comprehend
Amazon Translate
Amazon Transcribe
Amazon Polly
Amazon Athena
Amazon EMR
Amazon Redshift
Amazon Elasticsearch Service
Amazon Kinesis
Amazon QuickSight
AWS Direct Connect
AWS Snowball
AWS Snowmobile
AWS Database Migration Service
AWS IoT Core
Amazon Kinesis Data Firehose
Amazon Kinesis Data Streams
Amazon Kinesis Video Streams
Data Lake
on AWS
Storage | Archival Storage | Data Catalog
AnalyticsMachine Learning
Real-time dataOn-premises movementdata movement

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In Summary…

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Core Tenants
• Data lakes and data warehouses complement each other
• Loose coupling, but highly performant
• Storage, analytics, metadata management, etc..
• Future-proof your analytics
• Choosing the best tool for the job
• Elasticity and multiple clusters for dedicated purposes
• Replace capacity planning with a consumption model
• Don’t forget metadata management

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Use the right storage tier and data format
Data structure → Fixed schema, JSON, key-value
Access patterns → Store data in the format you will access it
Data characteristics → Hot, warm, cold
Cost → Right cost

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