How to Build an Open Data Lake Analytics Stack

Transcript

1. How to Build an
   Open Data Lake
   Analytics Stack
   Dipti Borkar
   Cofounder, Chief Product Officer &
   Chief Evangelist
   Chairperson |Community Team
   Presto Foundation
   

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2. 2
   Today’s Speaker
   Dipti is a Cofounder, CPO & Chief Evangelist of Ahana with over 15 years experience
   in distributed data and database technology including relational, NoSQL and
   federated systems. She is also the Presto Foundation Outreach Chairperson. Prior to
   Ahana, Dipti held executive roles at Alluxio, Kinetica and Couchbase. At Alluxio, she
   was Vice President of Products and at Couchbase she held several leadership
   positions there including VP, Product Marketing, Head of Global Technical Sales and
   Head of Product Management. Earlier in her career Dipti managed development
   teams at IBM DB2 Distributed where she started her career as a database software
   engineer. Dipti holds a M.S. in Computer Science from UC San Diego, and an MBA
   from the Haas School of Business at UC Berkeley.
   Dipti Borkar
   Cofounder, Chief Product
   Officer and Chief Evangelist
   Ahana
   

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3. 3
   Why Open Data Lake Analytics?
   Enterprise Data
   Beyond Enterprise
   Data
   IoT, Third-party,
   Telemetry, Event
   1000X
   More Data
   Terabytes to
   Petabytes
   Open &
   Flexible
   Open Source,
   Open Formats
   Reporting &
   Dashboarding
   Data
   Science
   In-data lake
   transformation
   Reporting &
   Dashboarding
   Data Warehouse
   Open Data Lakes
   

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4. 4
   What is Open Data Lake Analytics
   

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5. 5
   The Traditional
   Data Warehouse
   • Relational Database
   • Columnar Structure
   • In-Database Analytics
   • Structured Data
   • Modeled Data
   • Extract, Transform, Load
   • SQL Access
   Challenges
   • Expensive
   • Difficult to Manage
   • Costly to Maintain
   • Limited Data
   • Limited Access
   5
   

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6. 6
   The Drivers Behind Modernization
   Digital
   Transformation
   Real Time
   Events
   Modern
   Processing
   Techniques
   More Data
   Fast Data
   Smart Data
   The Deconstructed Database
   

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7. 7
   The Traditional Data Lake
   • File System Data Store / Object Store
   • Structured / Semi-Structured Data
   • Ingestion
   • Discovery
   • Data Science
   • Notebook and Python Access
   • Less expensive, but…
   • Good enough performance
   • Supports ~70% of DW workloads
   • Different approach to governance
   7
   

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8. 8
   Data
   SQL Query Processing
   Data Warehouse
   Cloud Data Lake
   Data Processing
   1-10 TB
   1TB -> PB
   Open Data Lake Analytics
   Reporting &
   Dashboarding
   Data
   Science
   In-data lake
   transformation
   Open Data Lake Analytics
   Reporting & Dashboarding
   

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9. 9
   Data Warehouse
   Operational
   Data Stores
   Third Party
   Data
   Machine Learning
   Semi- | unstructured
   Data Virtualization /
   Federated Access
   Streaming &
   IoT Data
   SQL Query Processing
   SQL Query
   Processing
   The Data Platform
   ETL
   ELT
   Data
   Engg
   Storage
   Compute
   1-10 TB
   Query & Processing
   Storage Compute
   SQL
   Structured Workloads
   1TB -> PB
   Data Lake
   Reporting
   Dashboards
   Visualizations
   Notebooks
   Custom Apps
   

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10. 10
    Cloud data lake driving open source SQL query engines
    Presto is the De-Facto SQL Engine for Data Lakes
    https://db-engines.com/en/ranking_trend/relational+dbms
    

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11. Merging the Data Warehouse and the Data Lake with a Distributed Query Engine
    11
    1. SQL Access
    2. Data Lake and Data Warehouse Access
    3. Unified Analytics
    4. Distributed Queries
    5. Limitless Scale
    6. Complex Data Types
    • Leverage Resources
    • Better Insight
    • More Use Cases
    • Leverage Platforms
    • Remove Limits
    • Amplified Insight
    

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12. Data Lake Use Cases
    

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13. 13
    Emerging
    use cases
    Use Cases
    Data Lakehouse
    analytics
    Reporting &
    dashboarding
    Interactive
    querying
    use cases
    Transformation
    using SQL (ETL)
    Federated access
    across data sources
    SQL
    Data Science
    Customer-facing
    app analytics
    

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14. 14
    The Data LakeHouse Components
    Dashboad /
    notebooks
    Compute /
    Query
    engine
    Operational
    catalog
    Table Format
    / Transaction
    manager
    Storage
    

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15. 15
    The Data LakeHouse Stack
    

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16. Considerations for Open Analytics Decision
    © 2021 Enterprise Management Associates, Inc. 16
    | @ema_research
    Data Analytics Users Platform
    Cloud Enterprise Business Cost
    

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17. Considerations for Any Unified Analytics Decision
    Data Structured
    Semi-Struc
    tured
    Real Time
    Structured
    Complex
    Data Types
    Textual Streaming
    © 2021 Enterprise Management Associates, Inc. 17
    | @ema_research
    

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18. Considerations for Any Unified Analytics Decision
    Data Analytics Users Platform
    SQL Python Notebook Search
    © 2021 Enterprise Management Associates, Inc. 18
    | @ema_research
    

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19. Considerations for Any Unified Analytics Decision
    Data Analytics Users Platform
    Engineer Analyst Scientist Business
    © 2021 Enterprise Management Associates, Inc. 19
    | @ema_research
    

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20. Considerations for Any Unified Analytics Decision
    Data Analytics Users Platform
    Cloud Enterprise Business Cost
    

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21. Considerations for Any Unified Analytics Decision
    Elasticity Scale Mobility Globality
    Cloud Enterprise Business Cost
    © 2021 Enterprise Management Associates, Inc. 21
    | @ema_research
    

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22. Considerations for Any Unified Analytics Decision
    Security Privacy Governance Unification
    Cloud Enterprise Business Cost
    © 2021 Enterprise Management Associates, Inc. 22
    | @ema_research
    

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23. Considerations for Any Unified Analytics Decision
    Semantics Logic Value Optimization
    Cloud Enterprise Business Cost
    © 2021 Enterprise Management Associates, Inc. 23
    | @ema_research
    

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24. Considerations for Any Unified Analytics Decision
    Forecast Containment Chargeback Scale
    Cloud Enterprise Business Cost
    © 2021 Enterprise Management Associates, Inc. 24
    | @ema_research
    

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25. 25
    Challenges with SQL on Open Data Lakes
    Cloud DW / AWS Serverless
    options get very expensive for
    growing data volumes
    ▪ Cloud data warehouse
    costs grow much faster
    than compute engine costs
    ▪ Serverless options like
    AWS Athena charge /query
    and get expensive
    “Do it yourself” approach
    is complicated
    ▪ Big data skills in platform
    teams are limited
    ▪ Presto is complicated and
    operationally very time
    consuming
    Presto on AWS like AWS
    Athena has limited capabilities
    and doesn’t scale
    ▪ Limited concurrency of 20
    per account
    ▪ No visibility into cluster
    logs, query logs, no
    flexibility / control on scale
    

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26. Presto & Presto Community
    

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27. 27
    Open Source Presto Overview
    • Distributed SQL query engine
    • Created at
    • ANSI SQL on Databases, Data lakes
    • Designed to be interactive & access
    petabytes of data
    • Open source, hosted at
    https://github.com/prestodb
    

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28. 28
    Presto
    Users
    

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29. Ahana Overview
    

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30. 30
    How Ahana Cloud works?
    ~ 30 mins to create the compute plane
    https://app.ahana.cloud/signup Create Presto Clusters in your account
    

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31. 31
    Ahana Cloud for Presto
    Ahana Console (Control Plane)
    CLUSTER
    ORCHESTRATION
    CONSOLIDATED
    LOGGING
    SECURITY &
    ACCESS
    BILLING &
    SUPPORT
    In-VPC Presto Clusters (Compute Plane)
    AD HOC CLUSTER 1
    TEST CLUSTER 2
    PROD CLUSTER N
    Glue
    S3
    RDS
    Elasticsearch
    Ahana
    Cloud Account
    Ahana console
    oversees and
    manages every
    Presto cluster
    Customer
    Cloud Account
    In-VPC orchestration of
    Presto clusters, where
    metadata, monitoring,
    and data sources
    reside
    

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32. 32
    Ahana Cloud Overview
    1. Ahana Managed Service
    Console
    2. Add data sources
    3. Query data where it lives with
    Federated Connectors (in place)
    4. Cluster management
    

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33. 33
    Case study: Securonix
    NextGen SIEM
    Cluster
    AWS S3 Data
    Lake
    Glue
    Metastore
    ▪ Securonix is a Security information and
    event management software
    ▪ They use Ahana for in-app SQL
    analytics on data from AWS S3 for
    threat hunting
    ▪ They pull in billions of events per day
    that get stored in S3
    ▪ With Ahana Cloud, they saw 3x better
    price performance compared with
    Presto on AWS
    

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34. 34
    Ahana Cloud for Presto - Summary
    ▪ Brings SQL on AWS S3 with an open data lake
    +
    USER
    ▪ Presto compute brought to your data in your
    VPC in your account
    ▪ Fully managed Presto cluster life cycle
    including idle-time management
    ▪ Query AWS DBs - RDS/MySQL , RDS/Postgres,
    Elasticsearch, Redshift, Elasticsearch
    ▪ Cloud-native and highly available running on
    Kubernetes
    ▪ Bring your own
    ▪ BI tool / Data Science Notebook
    ▪ Metadata Catalog
    ▪ Transaction Manager
    Easy to use
    3
    x
    Price Performance
    Open & Flexible
    

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35. 12/17/20
    

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