From Development to Production

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An introduction to development, testing and production patterns on AWS. Slides from the 'Powering games with Amazon Web Services' event in London.

Technology Business

Idea

Develop

Release

Evaluate

Iterate
OR

Pivot

Many applications,
many environments,
many developers

Usage

Midnight 7am 9am Noon 3pm 5pm Midnight

Time of day

Usage

60%

Midnight 7am 9am Noon 3pm 5pm Midnight

Time of day

State Conﬁg
Code Services

Amazon Machine
Image
ami-219387e

Auto-scaling
RDS
EC2 SNS
SimpleDB
SQS

Resources
Elastic Beanstalk CloudWatch
Security groups Tags

Template CloudFormation

Provisioned
resources

Free
Only pay for the provisioned resources

Trafﬁc

CNAME

Web front end

App servers

Data store

Full stack

Trafﬁc

CloudFormation
CNAME

Web front end

App servers

Data store

Full stack

Trafﬁc

CloudFormation
CNAME

Web front end Web front end

App servers App servers

Data store Data store

Full stack

Trafﬁc

New CNAME

Web front end Web front end

App servers App servers

Data store Data store

Full stack

Trafﬁc

New CNAME

Web front end

App servers

Data store

Full stack

Same building blocks
Speciﬁc conﬁgurations

Development

Integration build

Performance test

Web front end

App servers

Data store

Decouple

Web front end

App servers

Data store

Resource tiers

Web

App servers

Data store

Resource tiers

Web

App

Data store

Single point of failure

CloudFront

Data store
Amazon S3

Static asset delivery

CloudFront

Data store
Amazon S3

Asynchronous processing Spot

Background processing

What's hot

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Building Digital Trust in a Digital Economy Veronica Tan, Director - Cyber Security Agency of Singapore Apidays Singapore 2024: Connecting Customers, Business and Technology (April 17 & 18, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

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Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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Heather Hedden, Senior Consultant at Enterprise Knowledge, presented “The Role of Taxonomy and Ontology in Semantic Layers” at a webinar hosted by Progress Semaphore on April 16, 2024. Taxonomies at their core enable effective tagging and retrieval of content, and combined with ontologies they extend to the management and understanding of related data. There are even greater benefits of taxonomies and ontologies to enhance your enterprise information architecture when applying them to a semantic layer. A survey by DBP-Institute found that enterprises using a semantic layer see their business outcomes improve by four times, while reducing their data and analytics costs. Extending taxonomies to a semantic layer can be a game-changing solution, allowing you to connect information silos, alleviate knowledge gaps, and derive new insights. Hedden, who specializes in taxonomy design and implementation, presented how the value of taxonomies shouldn’t reside in silos but be integrated with ontologies into a semantic layer. Learn about: - The essence and purpose of taxonomies and ontologies in information and knowledge management; - Advantages of semantic layers leveraging organizational taxonomies; and - Components and approaches to creating a semantic layer, including the integration of taxonomies and ontologies

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Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.

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From Development to Production

1. From Development to Production

2. Application life cycle

3. Idea Develop Release Evaluate Iterate OR Pivot

4. Idea Develop Release Evaluate Iterate OR Pivot

5. Idea Develop Release Evaluate Iterate OR Pivot

6. Accelerate

7. Friction

8. How can elastic infrastructure help?

9. Idea Develop Release Evaluate Iterate OR Pivot

10. Many applications

11. Many applications, many environments

12. Dev, test, QA, integration, staging

13. Many applications, many environments, many developers

14. Complexity

15. Unlimit development velocity

16. Development and testing are spiky

17. Spin up and down

18. Usage Midnight 7am 9am Noon 3pm 5pm Midnight Time of day

19. Usage 60% Midnight 7am 9am Noon 3pm 5pm Midnight Time of day

20. Environmental consistency

21. Amazon Machine Image

22. State Conﬁg Code Services Amazon Machine Image ami-219387e

23. Collaborate

24. Full stack collaboration

25. Template

26. Deﬁne a full infrastructure stack

27. Auto-scaling RDS EC2 SNS SimpleDB SQS Resources Elastic Beanstalk CloudWatch Security groups Tags

28. Template CloudFormation Provisioned resources

29. Complete deﬁnition

30. Atomic

31. Idempotent

32. Free Only pay for the provisioned resources

33. Trafﬁc CNAME Web front end App servers Data store Full stack

34. Trafﬁc CloudFormation CNAME Web front end App servers Data store Full stack

35. Trafﬁc CloudFormation CNAME Web front end Web front end App servers App servers Data store Data store Full stack

36. Trafﬁc New CNAME Web front end Web front end App servers App servers Data store Data store Full stack

37. Trafﬁc New CNAME Web front end App servers Data store Full stack

38. Environmental freedom

39. Same building blocks Speciﬁc conﬁgurations

40. Development Integration build Performance test

41. Developer freedom

42. Architectural freedom

43. Technology freedom

44. Continuous deployment

45. A/B testing

46. Conﬁguration management

47. Low cost

48. Agility++

49. Meet business opportunities

50. Moving to production

51. Architect for elasticity

52. Web front end App servers Data store

53. Web front end App servers Data store Decouple

54. Web front end App servers Data store Resource tiers

55. Web App servers Data store Resource tiers

56. Web App Data store Resource tiers

57. Web App Data store Single point of failure

58. Data store Redundancy

59. Data store Load balance

60. Data store Graceful failure

61. Data store

62. Data store Add capacity over the day.

63. Data store Reduce capacity at night.

64. Data store Autoscaling

65. Data store Multi-AZ deployment

66. CloudFront Data store Amazon S3 Static asset delivery

67. CloudFront Data store Amazon S3 Asynchronous processing Spot Background processing

68. ElastiCache

69. Elastic Beanstalk

70. Thank you

From Development to Production

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Editor's Notes