Evaluate available configuration options for data store

Understanding and evaluating the various features and configuration options available for your data stores is crucial to optimizing storage space and performance. This allows workloads to leverage the strengths of different data types and access patterns effectively.

Best Practices

Utilize Purpose-Built Data Stores

• Select data storage solutions based on specific use cases: for example, use Amazon DynamoDB for key-value and document storage, Amazon S3 for object storage, and Amazon RDS for relational data. This ensures that each type of data is stored in the most efficient and effective manner.
• Evaluate access patterns to choose data stores that optimize performance. For instance, utilize caching mechanisms like Amazon ElastiCache to enhance read performance for frequently accessed data.
• Consider using tiered storage strategically based on frequency of access: hot storage for frequently accessed data and cold storage (e.g., Amazon S3 Glacier) for archival data. This can save costs while maintaining access efficiency when needed.
• Incorporate data lifecycle policies to automatically transition data between storage classes according to access patterns, ultimately optimizing storage costs and management effort.

Questions to ask your team

• What types of data do you handle, and how do they influence your choice of data store?
• Have you assessed the access patterns of your workload to determine the most suitable data management strategies?
• What are the throughput and latency requirements of your application, and how do your current data stores meet these needs?
• Are you utilizing purpose-built data stores for different data types (block, file, object) effectively?
• How often do you update your data, and does your current configuration support your update frequency?
• What strategies do you have in place for data availability and durability, and how do they align with your chosen configuration options?
• Have you conducted performance testing to evaluate the efficiency of your data store configurations under different scenarios?
• Are there any cost implications associated with the configuration options you are currently using, and how do they impact performance?

Who should be doing this?

Data Architect

• Design data storage solutions based on workload requirements.
• Evaluate different data stores suitable for various data types (block, file, object).
• Assess access patterns and determine the best configuration for optimized performance.
• Analyze throughput and frequency of access to select appropriate data management solutions.
• Ensure data management solutions meet availability and durability requirements.

Cloud Engineer

• Implement the selected data storage configuration in the cloud environment.
• Monitor the performance of data stores and make adjustments as necessary.
• Work with the Data Architect to test various configuration options and access patterns.
• Maintain documentation of data store configurations and performance metrics.

Business Analyst

• Gather workload requirements from stakeholders related to data management.
• Define the necessary access patterns and frequencies for various data use cases.
• Collaborate with technical teams to ensure the data solutions align with business needs.
• Identify key performance indicators (KPIs) to evaluate the effectiveness of data management solutions.

DevOps Engineer

• Automate deployment processes for data management solutions.
• Integrate monitoring tools to track data store performance and issues.
• Collaborate with Cloud Engineers to optimize costs and performance of data resources.
• Perform regular audits of data access and storage configurations to ensure compliance.

What evidence shows this is happening in your organization?

• Data Store Configuration Checklist: A comprehensive checklist to evaluate the configuration options of different data stores, focusing on optimizing storage space and performance. This document guides teams through assessing their current configurations and identifying areas for improvement.
• Performance Optimization Strategy Guide: A strategic guide outlining best practices for selecting and configuring data stores tailored to specific workload needs. It includes case studies and recommendations for performance evaluation based on data types, access patterns, and throughput requirements.
• Data Management Practices Report: An analytical report that documents the existing data management practices within the organization. It discusses the evaluation of various data stores, detailing performance metrics, access patterns, and configuration settings to inform future architectural decisions.
• Configuration Options Matrix: A matrix comparing various data stores based on configuration options such as durability, availability, read/write performance, and cost. This visual aid assists teams in making informed decisions on the most suitable data store for their workload.
• Performance Monitoring Dashboard: An interactive dashboard that provides real-time insights into the performance of data stores in use. It tracks metrics such as latency, throughput, and error rates, allowing teams to continually assess and optimize their configurations.

Cloud Services

AWS

• Amazon S3: An object storage service offering high scalability, data availability, and security features, suitable for storing vast amounts of unstructured data.
• Amazon EFS: A fully managed, scalable, elastic file system for use with Amazon EC2, providing file storage that can scale on demand while supporting NFS access.
• Amazon RDS: A managed relational database service that simplifies setup, operation, and scaling of a relational database in the cloud with multiple database engines to choose from.
• Amazon DynamoDB: A fully managed NoSQL database service that provides fast and predictable performance with seamless scalability, ideal for applications that require consistent, single-digit millisecond latency.

Azure

• Azure Blob Storage: A scalable object storage solution for unstructured data with capabilities for high availability and robust security features.
• Azure Files: Fully managed file shares that allow you to share files across your Windows, Linux, and macOS applications, usable via SMB (Server Message Block) protocol.
• Azure SQL Database: A fully managed relational database service operated by Microsoft Azure, providing built-in high availability, backup, and scaling capabilities.
• Azure Cosmos DB: A globally distributed, multi-model database service designed for high availability and horizontal scalability, suitable for mission-critical applications.

Google Cloud Platform

• Google Cloud Storage: A unified object storage service that allows for storage, management and retrieval of data through a web interface with high availability and redundancy.
• Google Cloud Filestore: A managed file storage service for applications that require a file system interface and a shared file system for data.
• Google Cloud SQL: A fully managed relational database service for MySQL, PostgreSQL, and SQL Server that makes it easy to set up, maintain, manage, and administer relational databases on Google Cloud.
• Google Firestore: A flexible, scalable NoSQL cloud database for storing and syncing data in real time, ideal for building serverless applications.

Question: How do you store, manage, and access data in your workload?
Pillar: Performance Efficiency (Code: PERF)