Full Stack Analytics on Amazon Web Services by Ian Robinson at Big Data Spain 2017

Building analytics applications requires more than just one good service. It requires the ability to capture a vast amount of data, and react to data changes in real time.

https://www.bigdataspain.org/2017/talk/full-stack-analytics-on-amazon-web-services

Big Data Spain 2017
November 16th - 17th Kinépolis Madrid

Transcript

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2. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
   Specialist Solutions Architect, Data and Analytics, EMEA
   November 17th, 2017
   Full Stack Analytics on AWS
   Ian Robinson
   

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3. Forces and Trends Prompting the Move to Cloud
   Cost Optimization
   Licenses
   Hardware
   Data center and operations
   Dark Data
   Prematurely discarding data
   Agility
   Experimentation (data & tools)
   Democratised Access to Data
   Time-to-first-results
   Terminate failed experiments early
   From BI to Data Science
   In-house data science
   From back office to product
   

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4. Storage is the Gravity for Cloud Applications
   Store all your data, for ever, at every stage of its lifecycle
   Apply it using the right tool for the job
   

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5. Storage is Job #1
   

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6. Object Storage is Foundational
   

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7. Standard
   Active data Archive data
   Infrequently accessed data
   Standard - Infrequent
   Access
   Amazon Glacier
   Create
   Delete
   Events and Lifecycle Management
   

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8. S3 as the Data Lake Fabric
   • Unlimited number of objects
   and volume
   • 99.99% availability
   • 99.999999999% durability
   • Versioning
   • Tiered storage via lifecycle
   policies
   • SSL, client/server-side
   encryption at rest
   • Low cost (just over
   $2700/month for 100TB)
   • Natively supported by big
   data frameworks (Spark, Hive,
   Presto, etc)
   • Decouples storage and
   compute
   • Run transient compute
   clusters (with Amazon EC2
   Spot Instances)
   • Multiple, heterogeneous
   clusters can use same data
   

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9. Database Migration
   Service
   Automated Data Ingestion
   

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10. Stream Events to S3 Using Kinesis Firehose
    

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11. Write Database Changes to S3 with DMS
    //LOAD001.csv
    //LOAD002.csv
    //.csv
    Full Load
    Change Data Capture
    

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12. Scalable (secure, versioned, durable) storage +
    Immutable data at every stage of its lifecycle +
    Versioned schema and metadata
    =
    Data discovery, lineage
    Storage + Catalog
    

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13. AWS Glue
    • Data Catalog Discover
    and store metadata
    • Job Authoring Auto-
    generated ETL code
    • Job Execution
    Serverless scheduling
    and execution
    

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14. Hive metastore-compatible, highly-
    available metadata repository:
    • Classification for identifying and
    parsing files
    • Versioning of table metadata as
    schemas evolve
    • Table definitions – usable by
    Redshift, Athena, Glue, EMR
    Populate using Hive DDL, bulk import, or
    automatically through crawlers.
    Glue Data Catalog
    

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15. semi-structured
    per-file schema
    semi-structured
    unified schema
    identify file type
    and parse files
    enumerate
    S3 objects
    file 1
    file 2
    file N
    …
    int
    array
    int
    char
    struct
    char int
    array
    struct
    char
    bool int
    int
    array
    int
    char
    char int
    custom classifiers
    app log parser
    metrics parser
    …
    system classifiers
    JSON parser
    CSV parser
    Apache log parser
    …
    bool
    Crawlers: Automatic Schema Inference
    

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16. AWS Lambda
    AWS Lambda
    Metadata Index
    (Amazon DynamoDB)
    Search Index
    (Amazon Elasticsearch)
    ObjectCreated
    ObjectDeleted PutItem
    Update Stream
    Update Index
    Extract Search Fields
    Indexing and Searching Using Metadata
    Amazon S3
    

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17. Security is Job #0
    

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18. Data Access & Authorisation
    Give your users easy and secure access
    Storage & Catalog
    Secure, cost-effective storage in Amazon S3.
    Robust metadata in AWS Catalog
    Protect and Secure
    Use entitlements to ensure data is secure and users’ identities are verified
    

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19. Identity and Access Management
    • Manage users, groups, and roles
    • Identity federation with Open ID
    • Temporary credentials with Amazon Security Token
    Service (Amazon STS)
    • Stored policy templates
    • Powerful policy language
    • Amazon S3 bucket policies
    

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20. IAM
    Amazon
    S3
    Amazon
    ElastiCache
    Amazon
    DynamoDB
    Amazon
    EMR
    Amazon
    Kinesis
    Amazon
    Athena
    Service API Access
    Security at the Data Level
    

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21. Third Party Ecosystem Security Tools
    Amazon
    S3
    AWS
    CloudTrail
    http://amzn.to/2tSimHj
    Amazon
    Athena
    Access Logging
    API Logging
    Access Log
    Analytics
    IAM
    Amazon
    EMR
    http://amzn.to/2si6RqS
    Storage Level Support for Access Logging and Audit
    

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22. Encryption Options
    AWS Server-Side encryption
    • AWS managed key infrastructure
    AWS Key Management Service
    • Automated key rotation & auditing
    • Integration with other AWS services
    AWS CloudHSM
    • Dedicated Tenancy SafeNet Luna SA HSM Device
    • Common Criteria EAL4+, NIST FIPS 140-2
    

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23. Serverless Processing and
    Analytics
    

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24. • Python code generated
    by AWS Glue
    • Connect a notebook or
    IDE to AWS Glue
    • Existing code brought
    into AWS Glue
    Managed ETL with AWS GLue
    

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25. • Schedule-based
    • Event-based
    • On demand
    Job Execution with AWS Glue
    

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26. Amazon Kinesis Analytics
    • Interact with streaming data in real time using SQL
    • Build fully managed and elastic stream processing
    applications that process data for real-time visualizations
    and alarms
    

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27. SELECT STREAM author,
    count(author) OVER ONE_MINUTE
    FROM Tweets
    WINDOW ONE_MINUTE AS
    (PARTITION BY author
    RANGE INTERVAL '1' MINUTE PRECEDING)
    WHERE text LIKE ‘%#BigDataSpain%';
    Amazon Kinesis Analytics – Simple SQL Interface
    

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28. Amazon Athena – Analyze Data in S3
    • Interactive queries
    • ANSI SQL
    • No infrastructure or administration
    • Zero spin up time
    • Query data in its raw format
    • AVRO, Text, CSV, JSON, weblogs, AWS service logs
    • Convert to an optimized form like ORC or Parquet for the
    best performance and lowest cost
    • No loading of data, no ETL required
    • Stream data from directly from Amazon S3, take advantage
    of Amazon S3 durability and availability
    

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29. Simple query editor
    with syntax highlighting
    and autocomplete
    Data Catalog
    Query History, Saved Queries, and
    Catalog Management
    

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30. QuickSight allows you to connect to data from a wide variety of AWS, third-party, and on-premises sources including Amazon Athena
    Amazon RDS
    Amazon S3
    Amazon Redshift
    Amazon Athena
    Using Amazon Athena with Amazon QuickSight
    

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32. Building Smarter Applications
    

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33. Add Machine Learning Capabilities
    Amazon Machine Learning Service
    Batch and online predictions
    Train using data in S3, RDS and
    Redshift
    Amazon EMR
    Comprehensive machine learning
    libraries (eg Spark MLlib, Anaconda)
    Provision analytics clusters in minutes,
    autoscale with data volume or query
    demand
    

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34. Amazon AI Services
    Amazon Polly – Lifelike Text-to-Speech
    47 voices, 24 languages
    Low-latency, real time
    Amazon Rekognition – Image Analysis
    Object and scene detection
    Facial analysis
    Amazon Lex – Conversational Engine
    Speech and text recognition
    Enterprise connectors
    

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35. Demographic Data
    Facial Landmarks
    Sentiment Expressed
    Image Quality
    Facial Analysis with Rekognition
    Brightness: 25.84
    Sharpness: 160
    General Attributes
    

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36. Up to ~40k CUDA cores
    Pre-configured CUDA drivers
    Jupyter notebook with Python2,
    Python3, Anaconda
    CloudFormation Template
    AWS Marketplace – one-click deploy
    AWS Deep Learning AMI
    

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37. Kinesis Firehose
    Athena
    Query Service Glue
    Machine Learning
    Predictive analytics
    Data Access & Authorisation
    Give your users easy and secure access
    Data Ingestion
    Get your data into S3
    quickly and securely
    Processing & Analytics
    Use of predictive and prescriptive
    analytics to gain better understanding
    Protect and Secure
    Use entitlements to ensure data is secure and users’ identities are verified
    Amazon AI
    Storage & Catalog
    Secure, cost-effective storage in Amazon S3.
    Robust metadata in AWS Catalog
    

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38. Thank You
    Full Stack Analytics on AWS
    

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