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Practical cloud

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Core deck on the state of the public cloud. Focuses on patterns (i.e. VMs, Serverless, Containers), Vendors (AWS, GCP) and practices.

Veröffentlicht in: Technologie
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Practical cloud

  1. 1. • 5 – 15x market share of all others (US market)AWS Dominates • AWS cloud bills are for ‘old services’ (EC2, RDS)80% EC2++ • First-time cloud projects outcome rate*50% FAIL
  2. 2. Definitions Vendors Patterns Lessons 1 2 3 4
  3. 3. 10 Million Executions = ~ $ 500 USD
  4. 4. Files AWS S3 RDBMS AWS RDS VMs AWS EC2
  5. 5. Just write code –> “tiny” Function / Method Auto-scales Very NewTechnology Use Dockerfiles –> “small” Application Coordinate via Container Mgr. NewTechnology
  6. 6. 100 10 1 VM Container Lambda Drive to Lambda – Save Money Cost
  7. 7. 5 50 95 Lambda Container VM UseVMs – Keep Control Control
  8. 8. “But why are Compute AND File Storage commodities on Azure, AWS AND GCP?”
  9. 9. Compute EC2 Containers Lambda Files S3 Glacier Data RDS Redshift DynamoDB Other Machine Learning Kinesis Serverless now Serverless option(s)
  10. 10. Alpha Some parts may work Service may be changed Service may be discontinued Beta Many parts should work Service may be changed Year One Most parts should work Can include some service integrations YearTwo All parts should work Patterns and scripts emerge YearThree Service is stable Tools and partners emerge
  11. 11. Functions • Logic Apps • No Code • Generates JSON Which Vendor?
  12. 12. Server-based Solutions Are simple, right?
  13. 13. Server-based Solutions need… High Availability Core Security
  14. 14. Server-based Solutions need.. High Availability More Security
  15. 15. Server-based Solutions need… High Availability Security Scalability Cost Control
  16. 16. Google Compute Engine Very fast to start …globally Automatically discounted for sustained use Easier to size via the ’slider’
  17. 17. Use image Files -> “BIG” Application PLUS OS Coordinate with Load Balancers Mature Technology Use Dockerfiles -> “small” Application Coordinate with Container Managers New Technology
  18. 18. Hadoop/Spark Transform Visualization Client Data Lake ANSI SQL Exploratory ANSI SQL Warehouse
  19. 19. ETL Glue PySpark Visualization Client - QuickSight Data Lake – S3 Explore SQL S3 Select Athena Warehouse Redshift Spectrum
  20. 20. AWS
  21. 21. AWS
  22. 22. ETL Dataflow/Beam Visualization Client - DataStudio Data Lake – GCS Explore SQL BigQuery MPP SQL BigQuery
  23. 23. Servers / IaaS? PaaS? Serverless? Integration testing? Orchestration? Deployment?
  24. 24. “My” Programming Language ? Debugging? Unit testing? Integration testing? Orchestration? Deployment?
  25. 25. “ ”
  26. 26. POC will be done on greenfield project Developers are excited and ready to jump in DBA is hesitant, maybe even resistant DBA is excluded from POC
  27. 27. AWS X-Ray
  28. 28. Reduce Attack Surface Test external connections Minimal permissions Granular policies Unique credentials
  29. 29. Service Costs Training Costs Tooling Costs Migration Costs Learning Costs
  30. 30. ServiceType Servers (or Containers) Serverless Compute EC2 Lambda Files File Services on EC2 S3 SQL on Relational Data RDBMS on EC2 or RDS Athena / Redshift Spectrum Data Pipeline Kafka cluster on EC2 Kinesis Machine Learning EMR with Spark ML or Hadoop on EC2 Machine Learning API IoT MQTT Message Broker RabbitMQ on EC2 IoT Broker NoSQL MongoDB… on EC2 DynamoDB