Your browser does not support JavaScript!

AWS and Cloud Computing Landscape: Current Practices and Innovations

GOOVER DAILY REPORT July 6, 2024
goover

TABLE OF CONTENTS

  1. Summary
  2. Managing AWS IAM and Security Practices with Terraform
  3. Overview of Amazon's Cloud Computing Ecosystem
  4. Product Launches and Updates in AWS Serverless Technologies
  5. Data Science and Machine Learning Roles at Amazon
  6. Comparative Analysis of Cloud Service Providers
  7. Database Management Systems and Cloud-Based Services
  8. Guides and Practical Insights for AWS Services
  9. Trends and Transformations in the Software Industry
  10. Conclusion

1. Summary

  • The report titled 'AWS and Cloud Computing Landscape: Current Practices and Innovations' delves deeply into the intricacies of AWS management, cloud computing services, and the pivotal roles of data science and machine learning within Amazon. It provides practical guidelines for using AWS IAM and Terraform, managing serverless architectures with AWS Lambda, and optimizing data operations with Amazon S3. Additionally, it offers comparative insights into major cloud service providers like AWS, Azure, and Google Cloud, along with a detailed analysis of database management systems including CouchDB, OpenSearch, and PostgreSQL. Furthermore, the report underscores the significance of data science and applied machine learning in enhancing Amazon’s operational efficiency and customer experiences, spotlighting key job roles such as Senior Data Scientists and Applied Scientists within the organization. Emphasizing industry trends, the report highlights the transformative impact of open source software, SaaS, and cloud computing on the software industry.

2. Managing AWS IAM and Security Practices with Terraform

  • 2-1. Best practices for AWS IAM

  • In managing AWS IAM effectively, several best practices are recommended. Key practices include deleting the root user access keys to prevent misuse, creating dedicated admin groups and users for better permission handling, enforcing Multi-Factor Authentication (MFA) with customer-managed policies for an added layer of security, and customizing password policies to enhance password strength. Centralizing IAM across multiple AWS accounts can streamline user management, and creating EC2 instance profiles can ensure security credentials are managed properly for apps running on instances. Furthermore, using HashiCorp Vault for just-in-time access can provide temporary permissions based on need, adhering to zero-trust and least privilege access principles.

  • 2-2. Deleting root user access keys

  • Deleting root user access keys is a critical practice to enhance security. The root user has unrestricted access to all AWS resources, making it a significant risk if compromised. To delete the access key, one must use the AWS Management Console, sign in as the root user, navigate to 'Security Credentials,' and delete the access key under the 'Access keys for CLI, SDK, & API access' section. Locking the root user away by deleting the access key is advised for everyday operations to avoid inadvertent exposure to security risks.

  • 2-3. Creating admin groups and users

  • Creating admin groups and users in AWS IAM can be efficiently managed using Terraform. A sample Terraform script defines the configuration to create an 'Administrators' group, attach the 'AdministratorAccess' policy, create an admin user, and add this user to the admin group. This setup allows for scalable and manageable admin access. Additionally, sensitive information like passwords can be handled securely within Terraform by marking outputs as sensitive, ensuring they are protected during operations.

  • 2-4. Enforcing MFA with customer-managed policies

  • To enforce Multi-Factor Authentication (MFA) in AWS, a customer-managed policy can be created using Terraform. This policy mandates MFA for performing critical actions by denying all operations that aren’t related to MFA device management unless MFA is enabled. By defining a policy condition that checks for MFA presence, organizations can ensure that even if a user attempts to perform any operation without enabling MFA, the attempt will be denied.

  • 2-5. Customizing password policies

  • Customization of AWS IAM password policies includes enforcing stricter requirements for password length and complexity, and defining rules for password rotation. Using Terraform, a sample policy can require a minimum of 10 characters, include uppercase, lowercase, numerals, and symbols, and enforce password rotation every 90 days. This ensures passwords are both complex and regularly updated, thereby reducing the risk of unauthorized access.

  • 2-6. Centralizing IAM for multiple accounts

  • Centralizing IAM management for multiple AWS accounts optimizes user and permission management across an organization's environments. By creating users in a central account and using IAM roles for cross-account access, administrative overhead is reduced. This setup only requires user creation in one account, with permissions delegated through roles to other accounts (like dev/test/prod), allowing users to access resources in different accounts without needing separate credentials for each.

  • 2-7. Creating EC2 instance profiles

  • EC2 instance profiles facilitate managing permissions for applications running on AWS EC2 instances. Using Terraform, an IAM role is created and assigned an instance profile, which can then be attached to an EC2 instance. This approach ensures temporary credentials are used for accessing AWS resources, enhancing security by avoiding the use of long-lived access keys, and enabling fine-grained permission control dynamically.

  • 2-8. Using HashiCorp Vault for Just-In-Time access

  • HashiCorp Vault can be utilized for managing just-in-time access to AWS resources. This method allows creating temporary permissions for users needing one-time or limited-duration access without establishing permanent accounts, thus adhering to least privilege principles. Vault automates the creation and expiration of temporary credentials, reducing operational overhead and minimizing security risks associated with lingering access permissions.

3. Overview of Amazon's Cloud Computing Ecosystem

  • 3-1. Amazon's evolution from an online bookstore to a tech giant

  • Amazon.com is a Seattle, Washington–based e-commerce and cloud computing giant whose humble beginnings can be traced to founder Jeff Bezos’s garage. Initially, Bezos started selling books online. Amazon began as an online book seller but evolved into a major disruptor across various sectors and industries. From the beginning, Bezos viewed Amazon as a technology company. This vision led Amazon to expand into multiple business areas including retail, cloud computing, media, and more. The company's early days focused on expanding sales and improving customer experiences.

  • 3-2. AWS leadership in the cloud market

  • Amazon Web Services (AWS), launched in 2002, is a comprehensive cloud computing suite featuring more than 200 services globally. AWS was developed to solve Amazon's own database and software issues and later on began renting these solutions to other businesses. As of 2023, AWS holds about one-third of the cloud market, leading ahead of Microsoft’s Azure (23%) and Google Cloud (11%). AWS historically accounts for 15% to 18% of Amazon's total sales but nearly 50% of the company’s operating income. AWS has thus cemented Amazon's leadership position in the cloud computing industry.

  • 3-3. Acquisitions and technology innovations

  • Amazon has a history of acquisitions and technological innovations that have significantly contributed to its growth. In 2005, Amazon introduced Prime, a membership program that started with free two-day shipping and now includes a suite of services. Amazon's acquisition of upscale organic grocery chain Whole Foods in 2017 for $13.7 billion marked its entry into the grocery industry. Amazon's foray into artificial intelligence (AI) began with the launch of the Echo smart device in 2014, powered by the virtual assistant Alexa. In 2023, Amazon announced a $4 billion investment in AI start-up Anthropic to advance its capabilities in generative AI.

  • 3-4. Revenue streams and market segments

  • As of 2024, Amazon has a diverse revenue stream with significant contributions from different segments. The company’s annual revenue amounts to $590.74 billion. While Amazon continues to generate substantial revenue from its e-commerce operations, a majority of its operating profits come from AWS. Amazon.com’s market cap stands at $2.06 trillion, and the share price as of July 3, 2024, is $197.59. Third-party seller services account for roughly 60% of sales, reflecting strong performance in this segment. Another crucial segment is Amazon Prime, with its membership estimated at 200 million across 19 countries.

4. Product Launches and Updates in AWS Serverless Technologies

  • 4-1. Event-Driven Architecture and AWS Lambda

  • The AWS Serverless team hosted the third Event-Driven Architecture (EDA) Day in London on May 14, 2024. This event brought together prominent figures in the EDA community and featured 13 sessions, 2 workshops, and a Q&A panel. David Boyne's keynote speech focused on the complexity of event-driven architectures. AWS Lambda celebrated its 10th anniversary and launched support for Ruby 3.3, based on the new Amazon Linux 2023 runtime.

  • 4-2. Amazon ECS and AWS Step Functions updates

  • AWS released several updates to Amazon ECS and AWS Step Functions. Amazon ECS now supports customer-managed keys (CMKs) to encrypt ephemeral storage for ECS tasks. Windows containers on AWS Fargate can now start up to 42% faster. AWS Step Functions introduced the TestState API which allows for testing individual states independently, accelerating workflow development.

  • 4-3. Advancements in AWS Amplify and Generative AI

  • AWS Amplify Gen 2 is now generally available, offering a code-first experience with TypeScript for building full-stack applications. AWS Amplify also introduced new workflows for team environments and an improved file storage experience. In the realm of Generative AI, Amazon Bedrock was updated to support new models like Anthropic’s Claude 3.5 and AI21 Labs’ Jamba-Instruct. The Bedrock Converse API now simplifies multi-turn conversations with a consistent invocation method.

  • 4-4. Enhancements in Amazon API Gateway and AppSync

  • Amazon API Gateway now supports extended integration timeouts for Regional and private REST APIs and allows the use of Amazon Verified Permissions to secure REST APIs. AWS AppSync has been updated to allow event-driven invocations of Lambda functions, enabling asynchronous API responses and more efficient handling of long-running operations. AppSync now also supports passing application request headers to custom authorizer functions.

  • 4-5. Storage improvements and webinar resources

  • Amazon S3 no longer charges for certain HTTP error codes initiated from outside an AWS account. Amazon Elastic File System (EFS) now supports up to 1.5 GiB/s throughput per client, a threefold increase. Additionally, numerous serverless-related webinars, blog posts, and video resources are available, providing deep dives and practical guides on various AWS services, including Lambda, ECS, and API Gateway.

5. Data Science and Machine Learning Roles at Amazon

  • 5-1. Optimization of Inventory Placement and Supply Chain Planning

  • Amazon employs Senior Data Scientists to develop advanced scientific solutions aimed at optimizing inventory placement and supply chain planning. The role involves designing mathematical models to optimize product flows and statistical models to plan supply chains under uncertain conditions. These models are intended to enhance customer experience, minimize costs, and reduce carbon footprints. The scientists collaborate with technical teams to build optimization tools for network flow planning and execution, and also work with various business and operational stakeholders to influence strategy and gather inputs necessary for problem-solving. Analytical skills, scientific expertise, and strong communication abilities are key requirements for success in these roles.

  • 5-2. Machine Learning Applications in Package Movement and Execution

  • Applied Scientists in the Amazon Shipping team tackle a range of machine learning challenges associated with package movement and execution. They develop machine learning models for auditing transportation costs and predicting shipping costs accurately at the package level. These models also forecast the number of packages to be collected from shipper warehouses to minimize First Mile shipping costs, and predict delivery delays using internal network signals and external factors like weather conditions. The developed models help improve buyer experience by enabling proactive corrective actions and customer notifications. Scientists in this role need expertise in diverse machine learning paradigms, including supervised, unsupervised, semi-supervised, and reinforcement learning, and must ensure their solutions scale across different regions and package movement types.

  • 5-3. Senior Data Scientist and Applied Scientist Roles

  • At Amazon, Senior Data Scientists and Applied Scientists play crucial roles in various departments. For instance, the Supply Chain Optimization Technology team focuses on managing inventory health and maximizing the net present value of inventory by driving actions like pricing markdowns and promotional deals. Senior Data Scientists are responsible for defining and conducting experiments to optimize long-term free cash flow, building growth forecasting models, and collaborating with data engineers and software developers to deploy models into production. These scientists must effectively translate ambiguous business questions into clear problems and deliver insights that balance scientific validity and business practicality.

  • 5-4. Amazon Advertising and its Impact on Merchant Success

  • Amazon Advertising is one of the company's fastest-growing and most profitable divisions. It helps merchants, retail vendors, and brand owners achieve success through native advertising, which drives the incremental sales of their products on Amazon. Applied Scientists in this division develop and optimize machine learning models to enhance traffic monetization and merchandise sales, perform A/B testing, and conduct large-scale data analysis. Their work supports creating scalable, efficient data analysis processes and continuously innovating to meet advertisers' business objectives while preserving the shopper experience. Key responsibilities include developing insights from data sets, conducting end-to-end machine learning projects, and establishing automated processes for model development and validation.

  • 5-5. Deep Learning and Large Language Models in Amazon's AGI Team

  • The Artificial General Intelligence (AGI) team at Amazon focuses on pushing the boundaries of AI with Large Language Models (LLMs) and multimodal systems. Senior Applied Scientists in this team develop novel algorithms and modeling techniques to advance the state of the art in LLMs, particularly in the audio domain. Their work leverages Amazon's diverse data sources and large-scale computing resources to create impactful products and services that utilize speech and language technology. The AGI team aims to create the best possible customer experience through continuous innovation in Generative AI, collaborating with other AI/ML scientists and engineers globally.

6. Comparative Analysis of Cloud Service Providers

  • 6-1. AWS vs. Azure vs. Google Cloud services

  • AWS, Azure, and Google Cloud offer a wide array of cloud services, each with distinct features and benefits. AWS is prominent for its extensive service range including advanced tools such as AWS Lambda for serverless computing, and Amazon SageMaker for machine learning. Azure is renowned for strong PaaS offerings and enterprise integrations, particularly through Azure Machine Learning and Azure Functions. Google Cloud, though with fewer regions, is noted for its simplicity and powerful integrations with Google services, including Google Cloud Functions and TensorFlow for machine learning.

  • 6-2. Functionality and integration differences

  • Functionality and integration differ across the platforms. AWS provides a high degree of service diversity and robust ecosystem support but can be complex to navigate. Azure excels in hybrid cloud scenarios and has seamless integration with Microsoft products like Office 365 and Dynamics. Google Cloud is known for facilitating AI and machine learning projects with its straightforward, developer-friendly interfaces. Each platform offers a digital marketplace for third-party applications: AWS Marketplace, Azure Marketplace, and Google Cloud Marketplace.

  • 6-3. Key services like AWS SageMaker, Azure Synapse, and Google BigQuery

  • Key services from each provider demonstrate their strategic focus. AWS SageMaker allows easy building and deploying of machine learning models. Azure Synapse Analytics combines enterprise data warehousing with big data analytics. Google BigQuery offers a RESTful web service that enables interactive analysis of large datasets. These services highlight the providers' commitment to advancing analytics and machine learning capabilities.

  • 6-4. Understanding provider-specific advantages and focus areas

  • Each provider has distinct advantages and focal points. AWS is highly versatile with extensive global infrastructure and a broad service portfolio, making it suitable for a wide range of use cases. Azure's strength lies in enterprise and hybrid solutions, providing robust security and compliance features. Google Cloud's strengths are in data analytics and machine learning, benefiting from Google's innovation and simplicity. These differences reflect the providers' strategies in catering to diverse business needs.

7. Database Management Systems and Cloud-Based Services

  • 7-1. Comparison of CouchDB, OpenSearch, and PostgreSQL

  • CouchDB is a native JSON document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones. OpenSearch is a distributed, RESTful search and analytics engine forked from Elasticsearch and based on Apache Lucene. PostgreSQL is a widely used open source RDBMS developed initially as an object-oriented DBMS (Postgres) and later enhanced with standards like SQL. All three database systems have different primary database models: relational DBMS with object-oriented extensions for PostgreSQL, schema-free document store for CouchDB, and a flexible type definitions and search engine for OpenSearch.

  • 7-2. Features, Managed Services, and Recent News

  • PostgreSQL, CouchDB, and OpenSearch all support various features and can be accessed through managed services. Aiven offers fully managed services for both OpenSearch and PostgreSQL. STACKIT provides enterprise-grade, 100% GDPR-compliant managed versions of OpenSearch and PostgreSQL. Recent news includes Amazon OpenSearch Service updates like Zstandard compression and zero-ETL integration with Amazon S3, PostgreSQL Flex managed instances by STACKIT, and various CouchDB citations detailing its setup and usage. Important mentions include PostgreSQL updates like rightsizing recommendations in AWS Compute Optimizer and PostgreSQL 16 Security Technical Implementation Guide.

  • 7-3. Cloud-based Services like Aiven for OpenSearch and PostgreSQL

  • Aiven for OpenSearch offers a fully managed open source search and analytics suite, providing out-of-the-box integrations for quick setup. STACKIT OpenSearch is another cloud-based service offering a managed version of OpenSearch, tailored for various applications and compliant with GDPR. For PostgreSQL, Aiven provides fully managed services with over 70 extensions and flexible orchestration tools, while STACKIT PostgreSQL Flex offers enterprise-grade managed instances with adjustable resources and several extensions.

  • 7-4. Optimizing Storage and Query Management with Amazon OpenSearch

  • Amazon OpenSearch Service provides various methods to optimize storage and query management. Zstandard compression is utilized to reduce storage costs. Query management can be simplified using search templates, and zero-ETL integration with Amazon S3 helps modernize data observability. Other optimizations include methods for performing reindexing using Amazon OpenSearch Ingestion and building multimodal search capabilities.

8. Guides and Practical Insights for AWS Services

  • 8-1. Connecting Amazon S3 to data warehouses

  • This guide demonstrates how to use Census to connect your S3 account to your data warehouse. First, you need a Census account and an S3 bucket ready for use with credentials for an AWS user that can access the bucket. Authentication details needed include Access Key ID, Secret Key, Bucket Name, and AWS Region. There is an option to use role-based permissions instead of keys, which involves creating a role in AWS IAM and assigning the required permissions. The setup process includes steps both inside Census and the AWS console. Variables for file paths allow for the creation of new CSV files in the S3 bucket reflecting the date and time of the sync. By default, files are written as CSV but can be configured to use other formats like TSV, JSON, NDJSON, or Parquet.

  • 8-2. AWS Solutions Architect certification overview

  • The AWS Solutions Architect certification is designed to validate expertise in designing and deploying scalable systems on the AWS platform. The syllabus covers core concepts of AWS, an overview of AWS infrastructure, and core services like EC2, S3, and RDS. It also includes AWS account setup, IAM, compute features, storage services, VPC networking, content delivery, databases including RDS and DynamoDB, and security practices such as IAM policies and roles, encryption, and monitoring using CloudWatch. The certification ensures preparedness to create secure, efficient cloud solutions and is essential for those looking to advance in the field of cloud computing.

  • 8-3. Steps to secure and manage AWS S3 buckets

  • When managing AWS S3 buckets, security and proper management are critical. The configuration process includes setting up the bucket with a globally unique name and configuring security settings, including IAM policies and roles to control access. Default server-side encryption is recommended to secure data within the bucket. Special permissions might be required for operations such as multipart uploads for large files exceeding 5GB. AWS S3 supports various sync behaviors, including lifecycle rules for managing data retention and ensuring cost efficiency through automatic data archiving and deletion.

  • 8-4. Utilizing GitHub Actions with Amazon CloudWatch

  • GitHub Actions is used for continuous integration and deployment, and it can be integrated with Amazon CloudWatch to monitor and manage the infrastructure. Best practices for deploying GitHub self-hosted runners in AWS include using short-lived AWS credentials, ephemeral runners for better security, and setting up runner groups for isolation based on security requirements. Optimizing runner start-up time with Amazon EC2 warm pools, leveraging spot instances for cost savings, and recording runner metrics using CloudWatch for observability are crucial practices. This setup ensures scalable, secure, and efficient CI/CD pipelines on AWS infrastructure.

9. Trends and Transformations in the Software Industry

  • 9-1. Impact of Open Source Software, Cloud Computing, and SaaS

  • The software industry has seen significant changes driven by the adoption of open source software (OSS), cloud computing, and software-as-a-service (SaaS). Open source software offers free alternatives where anyone can view and modify the code, posing a direct challenge to traditional software business models. OSS allows smaller firms and startups to access sophisticated computing resources, leveling the playing field with larger corporations. Cloud computing enables firms to run software on third-party hardware, alleviating the need to manage their own hardware, while SaaS allows users to access software via the internet without installation. The shift to these technologies is altering how software is accessed, utilized, and monetized.

  • 9-2. Challenges Posed by Low Marginal Costs and Competition

  • The traditional software business model is under strain due to its near-zero marginal production costs and the competitive advantage provided by network effects and switching costs. For instance, producing an additional copy of software incurs almost no cost, making the economics of a leading software offering highly profitable. However, OSS disrupts this model by providing comparable products at no cost, forcing existing firms to reconsider their revenue strategies. This challenge is intensified by the competitive pressure from cloud-based services and SaaS, where firms no longer need to invest heavily in their own IT infrastructure.

  • 9-3. Opportunities for Startups and Smaller Firms

  • Emerging technologies such as OSS and cloud computing offer substantial benefits for startups and smaller firms. By leveraging these technologies, smaller companies can quickly scale their operations and reduce initial investment costs, which traditionally served as a barrier to entry. These tech advancements provide access to powerful computing resources formerly reserved for industry giants, enabling startups to innovate and compete effectively. For example, many open source projects receive contributions from commercial entities, ensuring high-quality and up-to-date solutions that smaller firms can exploit for their growth.

  • 9-4. Shifts in How Software is Accessed and Used

  • The paradigm for software access and utilization has shifted dramatically. Traditional software installations on personal and corporate hardware are increasingly replaced by SaaS models, where software is accessed through web browsers. Organizations are moving their software operations into the cloud, benefiting from reduced costs and increased efficiency. This shift is facilitated by technologies like virtualization, which allow computers to operate as multiple machines, optimizing resource allocation and scalability. These trends indicate a broader transition away from owning and maintaining physical software toward a service-oriented model, impacting long-term software adoption and management strategies across industries.

10. Conclusion

  • The comprehensive exploration of AWS and cloud computing in this report reveals the vital role these technologies play in modern IT and business operations. The key findings highlight not only the best practices in managing AWS IAM using Terraform but also underscore the advancements and updates in AWS services, such as AWS Lambda and Amazon ECS, which drive operational efficiency and innovation. The inclusion of practical insights into securing and managing Amazon S3, along with the significance of AWS Solutions Architect certification, underscores the importance of security and expertise in cloud management. Additionally, the comparative analysis of cloud service providers like AWS, Azure, and Google Cloud illuminates their unique advantages and strategic focuses, aiding informed decision-making for technology investments. Despite the technological strides, the report acknowledges the limitations of current cloud services and suggests a continuous evaluation of emerging technologies like Machine Learning and Open Source Software to stay ahead in the evolving tech landscape. Future prospects include further leveraging AI and advanced data analytics to optimize operations and customer experiences further. These insights are practically applicable in real-world scenarios, providing concrete steps for enhancing cloud infrastructure, security, and operational strategy.

11. Glossary

  • 11-1. AWS IAM [Technology]

  • AWS Identity and Access Management (IAM) is a web service that helps securely control access to AWS resources. This document highlights best practices for managing IAM using Terraform, including creating admin groups, enforcing MFA, and centralizing IAM for multiple accounts.

  • 11-2. Amazon Web Services (AWS) [Company]

  • Amazon Web Services is a subsidiary of Amazon providing on-demand cloud computing platforms and APIs. It leads the cloud market, accounting for one-third of the market share, and its evolution and technological innovations are critical to Amazon's success.

  • 11-3. Terraform [Technology]

  • Terraform is an open-source infrastructure as code software tool created by HashiCorp. It is used to automate the provisioning of infrastructure in the cloud. The report covers best practices for managing AWS IAM with Terraform.

  • 11-4. AWS Lambda [Technology]

  • AWS Lambda is a serverless compute service that allows users to run code without provisioning or managing servers. The report details updates and enhancements in AWS Lambda, including its faster scaling capabilities in AWS GovCloud (US) Regions.

  • 11-5. Amazon ECS [Technology]

  • Amazon Elastic Container Service (ECS) is a fully managed container orchestration service. The document discusses updates related to Amazon ECS and AWS Fargate.

  • 11-6. Amazon Bedrock [Technology]

  • Amazon Bedrock is a platform for building and deploying artificial intelligence models on Amazon Web Services. It is part of Amazon's continuous innovation in AI and cloud services.

  • 11-7. Amazon S3 [Technology]

  • Amazon Simple Storage Service (S3) is an object storage service offering scalability, data availability, security, and performance. The report includes guides on connecting S3 to data warehouses and securing S3 buckets.

  • 11-8. AWS Solutions Architect [Certification]

  • AWS Solutions Architect certification validates expertise in designing and deploying scalable systems on AWS. The document provides an overview of the certification syllabus and recommended training resources.

  • 11-9. Machine Learning [Technology]

  • Machine learning is a subset of artificial intelligence involving the use of data and algorithms to mimic human learning processes. It plays a significant role at Amazon, optimizing inventory, supply chains, and advertising campaigns.

  • 11-10. Open Source Software [Technology]

  • Open source software is software with source code that anyone can inspect, modify, and enhance. The report discusses its impact on the software industry along with cloud computing and SaaS models.

12. Source Documents