Cloud Audit 2-Pack - Cloud Audit: Essentials + Cloud Audit: Big Data/Analytics

1. Cloud Audit Best Practices: Big Data/Analytics...

This Cloud Best Practices Guide is designed to help you do many types of Cloud Audits for your apps and cloud infrastructure.

850+ pages of Cloud Best Practices checklists for 21 AWS Cloud Services and over 270+ checklists.

BONUSES!!!! (recent additions, in latest update)

• EC2 instance type use cases and EC2 Spot cost optimization
• Overall AWS IAM Security checklist (in addition to security checklists for each service)
• DynamoDB Data Modeling Best Practices
• General Compliance and Organization Requirements checklist.

Software Engineers: Make improvements and show clients/managers you have followed Cloud Professional Best Practices in your infrastructure. Catch problems BEFORE they occur.

Managers, Stakeholders: Demonstrate to leadership Cloud Best Practices have been implemented to improve performance, security, cost optimization, reliability, disaster recovery and more. Find gaps in your plans early and remediate in advance.

There are 5 main sections with a total of 21 AWS services.

• Security: IAM, Cloudtrail, KMS, Cognito, Guard Duty
• Networking/Content Delivery: VPC, Route53, CloudFront, ELB, API Gateway
• Compute: EC2, EC2 Autoscaling, Lambda, Fargate
• Storage: S3, EBS, EFS
• Database: RDS, DynamoDB, Aurora, ElastiCache

These services were selected because they are core AWS services which appear in most companies' AWS cloud stacks.

Each the 21 AWS services covered has checklists for these best practices:

• Strategies: how to get the most out of the AWS service.
• Settings: settings that should usually be checked before usage.
• Avoid Mistakes: a checklist to AVOID in implementation.
• Operations: best practices for operational excellence.
• Security: best practices for security.
• Reliability: best practices for reliability.
• Performance: best practices for performance efficiency.
• Cost Optimization: best practices for cost optimization.
• Compliance: best practices for general compliance and governance.
• Innovation: innovative ways to use the service
• Documentation: best practices for documentation and knowledge management.
• Use Cases: popular use cases with this AWS service.
• Consider alternatives if…: consider alternatives if you need these features.
• Solutions: problem-solution pairs using advanced features.

This guide was created to help you do Cloud Audits in your current role.

Sample excerpt from the Table of Contents

Recommendations how to use:

• Understand Your Environment: Review the guide's best practices in the context of your specific AWS environment. Make improvements and show clients/managers you have followed Cloud Professional Best Practices. Use as a foundation to further tailor the checklists to reflect the unique configurations and use cases of your tech stack.
• Incorporate Into Audits: Use the checklists as a reference when conducting cloud audits. Ensure that each checklist item aligns with your organization's compliance requirements and security policies.
• Train Your Team: Share the guide with your team members and provide training on how to apply the best practices. This will help ensure everyone is on the same page regarding cloud governance and operational excellence.
• Regular Reviews: Periodically revisit the guide to keep up with updates and changes. AWS services and best practices evolve, so it's important to keep the checklists current with the latest recommendations.
• Document Findings: When using the guide, document your findings and any gaps identified. This documentation will be valuable for creating audit reports and tracking improvements over time. Regular revisit and updated.
• Multi-Person Licenses: If you have a multi-person license, ensure that all designated users have access to the guide and understand how to utilize it. This promotes consistency and thoroughness in your cloud audits.

Cloud Audit examples (how to use this guide):

• AWS Security Review: Each service has a security review checklist. Use this to make sure you have implemented proper AuthZ, AuthN, roles, policies, encryption, and other security measures.
• Automation and DevOps: Apply checklists and convert to IaC for automation and CI/CD practices. Use this to assess effectiveness of deployment pipelines and automation scripts in monitoring Cloud Best Practices. Highlight areas for improvement to streamline workflows and enhance deployment efficiency.
• Cost Management: Use the cost optimization best practice checklists for each service to steps needed for review resource usage and cost reports. Highlight any areas where costs can be reduced and suggest actions for cost-saving.
• Performance Monitoring: Apply the performance best practices tips for each service to improve your current performance and monitoring setup. Include findings on metrics and alerts configurations, and recommend improvements based on the checklist.
• Compliance Checks: Compliance-related checklists for each service ensure that regulatory and policy requirements are factored into your infrastructure. Use suggestions to document compliance gaps and outline necessary steps for remediation.
• Disaster Recovery: Include a checklist on disaster recovery planning in your audit report. Assess the current backup and recovery processes, and suggest enhancements based on best practices.
• Resource Optimization: Review resource provisioning and scaling practices using the guide’s recommendations. Document any inefficiencies and propose optimizations to improve resource utilization and performance.
• Data Protection: Use the best practices for data protection to review encryption settings for data at rest and in transit. Document any issues and recommend actions for ensuring data is adequately protected.
• Incident Management: Include a checklist for incident management practices to evaluate your incident response processes. Document response times and incident handling procedures, and recommend enhancements for quicker resolution and better management.

2. Cloud Audit Best Practices: Big Data/Analytics...

780+ pages of Cloud Best Practices checklists for 21 Big Data and Analytics AWS Cloud Services and over 290+ checklists.

Cloud Audit Best Practices: Big Data/Analytics is designed to help you do many types of Cloud Audits for your apps and cloud infrastructure.

BONUSES!!!! (recent additions, in latest update)

• General Big Data/Analytics best practices (in addition to best practices for all services below):
  • Data Encryption, Data Lifecycle, Cost Monitoring, Performance Tuning, Scalability Planning, Automated Data Pipeline, Data Governance, Data Backup, Serverless, Data Quality
• DynamoDB Data Modeling Best Practices checklists including
  • Access Patterns
  • Efficient Use of Primary Key
  • Effective Use of Secondary Indexes
  • Single Table Design
  • Data Distribution and Avoiding Hot Partitions
  • Optimizing Read and Write Capacity
  • Data Management and Expiry
• General Compliance and Organization Requirements checklist.

Best Practices deep dives
in EACH of these 21 AWS Services:

⚡️AWS DATA PROCESSING: AWS Kinesis, AWS Athena, AWS Glue, AWS Glue Studio, AWS Lambda, AWS EMR, AWS Batch

⚡️AWS STORAGE/DATABASE: Amazon S3, AWS DynamoDB, Amazon RDS, AWS Aurora, AWS Redshift, AWS Data Exchange

⚡️AWS DATA ANALYTICS: AWS Data Pipeline, Amazon QuickSight, Amazon OpenSearch, Amazon Forecast

⚡️AWS DATA INTEGRATION: Amazon MSK, AWS Glue DataBrew, AWS Lake Formation, AWS Step Functions

Each AWS service has checklists covering these best practices categories:

⭐️ Strategies: how to get the most out of the AWS service.

⭐️ Settings: settings that should usually be checked before usage for that service.

⭐️ Avoid Mistakes: a checklist to AVOID in implementation of that service.

⭐️ Operations: best practices for operational excellence.

⭐️ Security: best practices for security for the service.

⭐️ Reliability: best practices for reliability for the service.

⭐️ Performance: best practices for performance efficiency for the service.

⭐️ Cost Optimization: best practices for cost optimization.

⭐️ Compliance: best practices for general compliance and governance.

⭐️ Innovation: innovative ways to use the service

⭐️ Documentation: best practices for documentation.

⭐️ Use Cases: popular use cases with this AWS service.

⭐️ Consider alternatives if…: consider alternatives if you need these features.

⭐️ Solutions: problem-solution pairs using service features.

AWS DATA PROCESSING

• AWS Kinesis: A service for real-time data streaming and analytics. It's important for big data analytics as it allows for real-time processing of streaming data at scale.
• AWS Athena: An interactive query service for analyzing data in Amazon S3 using standard SQL. It's valuable for quickly querying large datasets without needing complex ETL processes.
• AWS Glue: A fully managed ETL (extract, transform, load) service. It simplifies data preparation for analytics by automating data integration tasks, making it essential for building data pipelines.
• AWS Glue Studio: A visual interface for AWS Glue that allows users to create, run, and monitor ETL jobs. It's important for big data analytics as it simplifies the process of building and managing ETL workflows.
• AWS Lambda: A serverless compute service that runs code in response to events. It's useful for big data analytics for running data processing functions without managing servers.
• AWS EMR: A managed cluster platform that simplifies running big data frameworks like Apache Hadoop and Apache Spark. It's crucial for scalable data processing and analytics on large datasets.
• AWS Batch: A service for running batch computing workloads on AWS. It's important for big data analytics because it allows for the efficient processing of large-scale jobs in parallel.

AWS STORAGE/DATABASE

• Amazon S3: A scalable object storage service for storing large datasets. It is essential for big data analytics due to its cost-effective storage and ability to store vast amounts of unstructured data.
• AWS DynamoDB: A fully managed NoSQL database service. It's important for big data analytics for storing and retrieving any amount of data with high performance and low latency.
• Amazon RDS: A managed relational database service. It's crucial for big data analytics for running SQL queries and integrating with various analytics tools.
• AWS Aurora: A MySQL and PostgreSQL-compatible relational database built for the cloud. It provides high performance and availability, making it suitable for analytics workloads.
• AWS Redshift: A fully managed data warehouse service. It's vital for big data analytics as it enables fast querying and reporting across large datasets.
• AWS Data Exchange: A service for finding, subscribing to, and using third-party data in the cloud. It's important for big data analytics because it facilitates access to a wide variety of external datasets for enhanced insights.

AWS DATA ANALYTICS

• AWS Data Pipeline: A web service for automating data movement and transformation. It is important for big data analytics as it allows data workflows to be managed and scheduled efficiently.
• Amazon QuickSight: A business analytics service for building visualizations and performing ad hoc analysis. It's valuable for big data analytics for creating easy-to-understand dashboards and reports.
• Amazon OpenSearch: A search and analytics engine for log analytics, real-time application monitoring, and clickstream analytics. It's essential for analyzing and visualizing data in near real-time.
• Amazon Forecast: A fully managed service for generating accurate forecasts using machine learning. It's crucial for big data analytics as it enables predictive analytics on large datasets.

AWS DATA INTEGRATION

• Amazon MSK: A managed service for Apache Kafka that makes it easy to build and run applications that use Apache Kafka for streaming data. It's important for integrating streaming data into big data analytics workflows.
• AWS Glue DataBrew: A visual data preparation tool that helps clean and normalize data without writing code. It's valuable for big data analytics as it speeds up data preparation for analysis.
• AWS Lake Formation: A service to set up a secure data lake in days. It's crucial for big data analytics as it simplifies the process of setting up a data lake, making it easier to store, catalog, and analyze large datasets.
• AWS Step Functions: A serverless orchestration service that lets you build complex workflows. It's important for big data analytics as it manages the flow of data processing tasks, integrating various AWS services effectively.

note: Guides are published independently and not affiliated directly with Amazon and Amazon AWS.

Usage rights: By purchasing this guide you have permission to incorporate these best practices checklists in your cloud audit reports for your work, job or freelance projects as long as you authored it and the report is not for resale (all other rights are reserved). For those who have purchased a multi-person guide license then it is permitted for the number of users concurrently that you purchased for under those terms. All other rights reserved by the author/publisher.