Advanced concepts in Cloud Platforms, page 2
Managed Graph Databases: When Relationships Are the Data
A managed graph database models data as nodes and edges, making relationship-heavy queries fast. Use it for social networks, fraud detection, or recommendations.
Managed Time Series Databases (TSDB)
A managed time series database is a hosted service optimized for data with a timestamp. It's used for application monitoring, IoT sensor data, and financial analytics. The main footgun is using a general-purpose database, which fails at high-volume writes.
Global Database: One Logical DB, Multiple Regions
A global database is a single logical database that spans multiple geographic regions, providing fast local reads and disaster recovery. It's used for apps with a worldwide user base that must survive regional outages.
AWS DMS: Automating Database Migrations
AWS DMS automates moving data between databases, not just as a simple data pump. Use it for one-time migrations to the cloud or for continuous replication. The footgun is assuming DMS also converts your schema; for different engines, you must use the Schema…
Immutable Deployments: Treat Servers Like Cattle, Not Pets
Treat servers like cattle, not pets. Instead of modifying live servers, you deploy updates by replacing entire server fleets with new, pre-baked images. This eliminates configuration drift and simplifies rollbacks.
.ebextensions: Infrastructure as Code for Elastic Beanstalk
.ebextensions are config files for customizing the AWS resources in your Elastic Beanstalk environment. Use them to install packages, run commands on instances, or provision related AWS resources.
Cloud Native Buildpacks: No More Dockerfiles
Cloud Native Buildpacks turn source code into container images without a Dockerfile. This lets platform teams enforce security and best practices, while app developers just push code. The footgun is assuming they're simple; they're a full build system.
Managed Kubernetes: Your Cloud's K8s Control Plane
A managed Kubernetes service (EKS, AKS, GKE) runs the complex K8s control plane for you, letting you focus on deploying apps, not managing infrastructure. Use it to run containers without the overhead of maintaining masters.
Horizontal Pod Autoscaler (HPA): Scale on Demand
A Horizontal Pod Autoscaler (HPA) is a thermostat for your app's capacity, adding or removing pods based on load. It's used to handle traffic spikes by watching metrics like CPU, but a common footgun is setting aggressive thresholds that cause flapping.
Serverless State Machines: The Conductor for Your Functions
A serverless state machine is the conductor for your microservices, telling each function when to run and how to handle errors. It's built for multi-step processes like ETL pipelines or coordinating parallel tasks. The footgun is creating a visual monolith.
The Saga Pattern: Transactions Across Microservices
The Saga pattern manages transactions across services by chaining local operations. If a step fails, compensating actions undo previous work. It's common in booking systems.
CQRS: Separate Models for Reading and Writing Data
CQRS splits your application into two parts: one for changing data (Commands) and one for reading it (Queries), often with separate data models. Use it in complex systems with different read/write patterns.
IaC State: The Map Between Your Code and the Cloud
IaC state is the source of truth mapping your code to real-world resources, acting as your tool's memory. Terraform uses a state file to plan updates, while other tools use a service backend. The footgun: never commit state files to Git; they lack locking and.
Policy as Code: Rules as Versioned, Testable Code
Policy as Code (PaC) treats rules like code: versioned, tested, and automated. Instead of manual UI clicks, you define guardrails in a declarative language. Use it in CI/CD to block bad deploys or in Kubernetes to enforce runtime rules.
Cost Anomaly Detection: Catching Runaway Cloud Bills
Think of cost anomaly detection as a fraud alert for your cloud bill. It uses machine learning to learn your normal spending patterns and automatically flags unexpected spikes before they become a crisis.
Cloud Unit Economics: Tying Spend to Value
Instead of just a total cloud bill, unit economics calculates cost per meaningful unit, like 'cost per customer.' This helps justify rising costs with business growth and lets product owners make data-driven pricing tradeoffs.
Apache Flink: Unifying Batch and Stream Processing
Apache Flink treats everything as a stream of data, even finite batches. This unified model lets you process real-time events and historical data with the same logic. Use it for live analytics or fraud detection.
Apache Beam: Write-Once, Run-Anywhere Data Pipelines
Apache Beam is a universal remote for big data engines. You write your pipeline logic once using its SDK, and it translates your code to run on different "runners" like Spark or Flink. The footgun is thinking Beam is an engine; it's an abstraction that.
Apache Iceberg: A Table Format for Huge Datasets
Apache Iceberg is an open table format for huge analytic datasets. It adds a metadata layer to files in object storage, enabling engines like Spark and Trino to work with transactional guarantees. The footgun: it's a format, not a query engine itself.
Data Mesh: From Central Data Lake to Distributed Ownership
Data Mesh decentralizes data ownership, moving it from a central team to the business domains that create it. This approach, like microservices for data, is for orgs where a monolithic data lake has become a bottleneck.
We are hiring for this. Every open role lists the topics its interview covers, so you can prepare for the real thing rather than guessing.
See open roles