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Multi Cloud Database Picker Guide for Real World Architectures
Govardhana M K
March 24, 2025
TechOps Examples
Hey — It's Govardhana MK 👋
Along with a use case deep dive, we identify the remote job opportunities, top news, tools, and articles in the TechOps industry.
👋 Before we begin... a big thank you to today's sponsor PERFECTSCALE
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IN TODAY'S EDITION
🧠 Use Case
Multi Cloud Database Picker Guide for Real World Architectures
🚀 Top News
👀 Remote Jobs
Nethermind is hiring a IAM Engineer
Remote Location: Worldwide
Superhuman is hiring a Senior Site Reliability Engineer
Remote Location: US, Canada & Latin America
📚️ Resources
📢 Reddit Threads
🛠️ TOOL OF THE DAY
Lazy Doc - A project for those who are lazy af to document their code.
It is designed to detect undocumented functions and pass the function to an AI provider to document it.
🧠 USE CASE
Multi Cloud Database Picker Guide for Real World Architectures
When people start cloud architecture, especially across AWS, Azure, and GCP, they often get stuck in the catalog. “What’s the GCP equivalent of DynamoDB?” “Is Cosmos DB the same as MongoDB?” This thinking leads you into a trap.
Choosing a database isn’t about ticking off boxes, it’s about knowing what you’re building and what it needs.
Start with Use Case, Not Features
Before scanning the long list of services, answer this:
What kind of data are you storing? (Structured? Events? Key-Value? Graph?)
What access patterns are common? (OLTP vs OLAP, random vs sequential reads, low latency reads?)
What scale are you expecting? (1000 rows or 10B?)
Is this write-heavy, read-heavy, or balanced?
I’ve made this cloud DB picker illustration for easy mapping.
Download a high resolution copy of this diagram here for future reference.
Database Types and Real World Choices
Type | ✅ Use When |
|---|---|
Relational DB | Standard apps needing ACID: SaaS platforms, ERP, CRM |
NoSQL (Key-Value / Doc) | Low latency, highly scalable, flexible schema: IoT, gaming, mobile apps |
In Memory DB | Session stores, leaderboards, caching: Think Redis |
Time Series DB | Metrics, telemetry: monitoring systems like Prometheus alternatives |
Graph DB | Social networks, fraud detection: when relationships matter more than rows |
Wide Column Stores | High write throughput, scalable: product catalogs, time series with sparse fields |
Search Engine | Log analysis, full text search: ELK stack replacement |
Data Warehouse | Analytics at scale: dashboards, BI tools, ad hoc queries |
Time Ordered DB | Event-driven apps, stream processing: for Kafka like pipelines |
But What Happens After Picking a DB?
Choosing the database type and cloud service is just step one. In real world architecture, your success depends on how you use that service, and how it integrates with the rest of your system.
My observations:
1. You rarely use just one database.
A SaaS app may use:
RDS (PostgreSQL) for user and billing data.
ElastiCache for session caching.
OpenSearch for product search.
Redshift for reporting and dashboards.
The best teams separate OLTP and OLAP early on, even if scale isn't a problem yet. Don’t make your relational DB do analytics.
2. Interoperability matters more than "feature matching".
A mistake many teams make: assuming Cosmos DB is a drop in for MongoDB. Technically, yes. Operationally, no.
Cosmos DB’s RU/s model works very differently from Mongo’s performance patterns.
Firestore vs DynamoDB? They’re both serverless NoSQL, but Firestore syncs beautifully with Firebase apps - Dynamo won’t give you that native mobile sync.
Always ask: how will this database behave under my app’s traffic patterns?
3. Cost explodes with wrong access patterns.
Seen this more than once:
Teams stream logs into BigQuery for real time dashboards.
Costs spiral because they’re querying hot data every 30s.
Better pattern?
Stream to Cloud Storage or S3, run daily ETLs, and query aggregated summaries from BigQuery/Redshift.
Start small, but design with cost controls baked in. A bad schema in a scalable DB doesn’t scale cheaply.
My 2 cents:
You don’t need all the boxes on Day 1. But the best cloud architectures:
Don’t overload their relational DB.
Pick the right DB per data type.
Embrace event streams where ordering matters.
Use in memory wisely (not as a DB substitute).
Choosing the right DB is less about comparing logos and more about understanding data shape + access pattern + scale + integration.
We partnered with PerfectScale to bring you a FREE Webinar.
Join us for an insightful panel discussion with Logz.io’s CTO and Co-Founder Asaf Yigal, PerfectScale by DoiT’s Chief Evangelist Anton Weiss, and DevOps legend John Willis, who will share critical strategies for navigating the future of AI.
You will learn the role of systems thinking in DevOps, DevSecOps, and SRE
Don’t miss out!