Cloud Architects and Lead Developers
Selecting a database architecture for a new cloud-native application
Cas d’usage
À propos
The Data Processing & Compute Provisioning mind map template provides a technical architecture overview for engineers and architects managing cloud infrastructure. This 44-node Data Processing & Compute Provisioning cheat sheet covers five critical storage paradigms: Relational Database, Data Warehouse, No-SQL Database, Document Database, and Global Relational Database. It serves as a decision-making framework for selecting the right compute provisioning strategy based on performance needs and consistency requirements. The template details specific operational benefits such as 'Automated backups, maintenance, and replication' for relational systems and the use of 'atomic clocks' for global consistency. By mapping out these distinct technologies, users can evaluate trade-offs between schema flexibility and query performance across petabytes of data.
Conditions d'utilisationQuand utiliser ce modèle
Engineering Managers and Technical Trainers
Onboarding junior engineers to data storage and compute concepts
Students and Software Engineers
Preparing for cloud certification exams or technical system design interviews
Comment utiliser ce modèle
Étape 1
Import the Xmind file
Download and open the .xmind file in the Xmind desktop or web application to view the full hierarchy.
Étape 2
Map your infrastructure
Replace the generic category nodes with your specific database instances and current compute provisioning settings.
Étape 3
Annotate with requirements
Use the floating note feature to add specific latency or throughput requirements to each database type.
Questions fréquentes
According to the Data Warehouse branch, partitioning divides a large table into smaller logical segments to improve performance, whereas table sharding divides separate tables by date. The template explicitly recommends partitioning over sharding for better query performance and cost reduction.
The template specifies that Global Relational Databases achieve strong consistency across distributed nodes by using atomic clocks to synchronize transactions globally, allowing users to specify node locations and counts.
In the No-SQL Database branch, it is noted that indexes are often not supported because they slow down writes and consume significant space. This architecture is instead optimized for extremely fast writes and key-based retrieval.
Yes, this Xmind template is fully editable. You can add specific cloud provider names (like AWS RDS or Google Cloud Spanner) to the existing branches to tailor the map to your specific tech stack.
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