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Towards Cloud Enabled Data Intensive Digital Transformation



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Towards Cloud Enabled Data Intensive Digital Transformation

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Towards Cloud Enabled Data Intensive Digital Transformation

  1. 1. Towards a Cloud Enabled  Data Centric Digital  Transformation  Crishantha Nanayakkara VP ­ Technology, Auxenta
  2. 2. ● Introduction – Digital Transformation Vs Digitization  – Enterprise Architecture (EA) ● Enterprise System Evolution – Pre­SOA – (Traditional/ Monolithic) – SOA  – Microservices ● Cloud Native Applications  – Microservices with Containers ● Data Intensive (Big Data) Applications Agenda
  3. 3. Digital transformation  Vs  Digitization
  4. 4. Digital transformation is the application of digital  technologies to fundamentally impact all aspects of business  and society. Products People Process
  5. 5. Digital transformation  Vs  Enterprise Architecture
  6. 6. Enterprise Architecture is the complete expression of the  Enterprise. It is a description of the goals of an organization, how these goals are  realized by business processes, and how these business processes can  be better served through technology. Source: TOGAF 9.2 Phase 0 – Preliminary Phase A – Architecture Vision Phase B – Business Architecture Phase C – Information Systems Architecture Phase D – Technology Architecture Phase E – Opportunities and Solutions Phase F – Migration Planning Phase G – Implementation Governance Phase H – Architecture Change Management 
  7. 7.           Pre­SOAPre­SOA           (Monolithic)(Monolithic)           Pre­SOAPre­SOA           (Monolithic)(Monolithic)               SOASOA              SOASOA     MicroservicesMicroservices    MicroservicesMicroservices (1990s) (2000s) (2010s) Enterprise System Evolution Cloud Enabled On Premise
  8. 8. Reference: Microservices: The resurgence of SOA principles and an alternative to the monolith (PWC) Enterprise System Evolution
  9. 9. 1.0 Pre­SOA  (Monolithic)
  10. 10. Monolithic application has single code base with multiple modules.  Modules are divided as either for business features or technical  features. It has single build system which build entire application  and/or dependency. It also has single executable or deployable binary Single code base with multiple modules On-premise or VM based cloud deployment
  11. 11. 2.0 Service Oriented Architecture (SOA)
  12. 12. The Enterprise Enterprise Application Silos Enterprise Application Enterprise Application Enterprise Application Enterprise ApplicationEnterprise Application Enterprise Application
  13. 13. MiddlewareMiddleware Enterprise Application Enterprise Application Enterprise Application Enterprise Application Enterprise Application Enterprise Application Enterprise Application
  14. 14. 14 SOA (Service Oriented Architecture) Source: Open Source SOA
  15. 15. Enterprise Service BusEnterprise Service Bus Cloud Infrastructure 48 Ministries 60 Departments 8 Provincial Councils 271 DS Offices Application Application Services Application Services 32 Provincial Ministries 16 Provincial Departments Application Services Message BrokerBPS Service Registry An Example ­ SOA Smart Pipe
  16. 16. 3.0 Microservices
  17. 17. ● Cloud Native Applications – Microservices – Containers – DevOps ● Data Intensive Applications  – Big Data – Data Lakes / Data Warehousing – Machine Learning / Deep Learning Microservices
  18. 18. 3.1 Cloud Native Applications
  19. 19. ● Cloud Native Applications – Microservices – Containers – DevOps – Continuous Delivery ● The Cloud Native Computing Foundation (CNCF) Source: Cloud Native Applications
  20. 20. “It is an approach that builds software applications as  microservices and runs them on a containerized and  dynamically orchestrated platform to utilize the  advantages of the cloud computing model.” Cloud Native Applications
  21. 21. 21 Source:
  22. 22. Microservices 1. Domain Driven Design (DDD) ­ Stateless 2. Single Responsibility Principle (SRP) 3. Having a separate data store 4. Published Service Contracts (Swagger /RAML) 5. Container based Deployment (independent, lightweight) 6. Lightweight Communications (REST/HTTP, Websockets) 7, Dumb Pipes, Smart Endpoints 8. Horizontal Scaling (Y­Scale)
  23. 23. MSA (Micro­Services Architecture) Reference: Getting Started with Microservices – By Arun Gupta
  24. 24. Netflix started Microservices implementation with the Cloud Migration
  25. 25. Monolithic First – Stanrgler Pattern (A different Perspective) Reference:
  26. 26. Monolithic First – Stanrgler Pattern (A different Perspective) According to MF observed, ● Almost all the successful microservice stories  have started with a monolith that got too big  and was broken up ● Almost all the cases where I've heard of a  system that was built as a microservice system  from scratch, it has ended up in serious  trouble.
  27. 27. Integrating Microservices with  Legacy / Monolithic Systems
  28. 28. Containers 1. They are fast, lightweight, consistent  2. Solves the deployment problems by deploying your application  with all dependencies into a single container
  29. 29. ● Container Orchestration will help to  – Monitor your system – Trigger the startup and shutting down the containers – Balance the load between the active application  instances – Handle authentication between instances ● Current available Orchestration Solutions: – Kubernettes, Docker Swarm, Apache Mesos, AWS ECS Container Orchestration
  30. 30. Microservice­Container Deployment
  31. 31. Serverless Design ● Moving away from servers and infrastructure concerns and  allowing developers to primarily focus on the code is the  ultimate goal of being serverless. 
  32. 32. 3.2 Data Intensive (Big Data)  Applications
  33. 33. ● Big Data – Variety (Text, Audio, Video, etc) – Volume (Tera and Peta bytes) – Velocity (Data Frequency, Not under your control) Big Data
  34. 34. ● What lead to Big Data? – Cloud Adoption, Social Media, Mobile Usage, Sensor  Data What lead to Big Data?
  35. 35. ● What is a Big Data Application? – Data must be in Tera or Peta bytes – More than one source / form – Huge processing loads – Real time stream processing – Process and Persist in scalable and flexible data stores – APIs for query purposes – Provide advanced analytics  Big Data Applications / Solutions
  36. 36. ● What are Big Data Technologies? – Numerous companies. Numerous products – Mainly open source – Cloud focused – Mostly immature (It is a good thing!) Big Data Technologies
  37. 37. The Big Data Evolution (The Open Source Stack) Batch Processing Stream Processing Artificial Intelligence
  38. 38. The Big Data Evolution (The AWS Cloud Stack) Batch Processing Stream Processing Artificial Intelligence
  39. 39. The Big Data Pipeline
  40. 40. The AWS Big Data Pipeline
  41. 41. Thank YouThank You