Cloud Audit Best Practices: Big Data/Analytics (780+ pgs., 21 AWS Services)

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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.