How does shared responsibility shift between IaaS and SaaS?
Tests your understanding of security ownership across cloud stacks. Strong answer: in IaaS you own OS, apps, and network controls; in SaaS you only own data, identities, endpoints, and accounts while the provider manages the rest.
AWS Cost and Usage Report (CUR)
Think of CUR as your AWS itemized receipt, delivered daily to S3. It breaks down charges by hour, product, resource, and tag for spreadsheets or Athena queries. Mid-month numbers are estimates, so do not lock budgets until the report finalizes after invoicing.
Data Swamp: When a Data Lake Becomes Unusable
A data swamp is a data lake turned digital landfill, so disorganized that finding useful information is nearly impossible. This happens when data is dumped without metadata or quality checks, making it a costly, insecure liability instead of a valuable asset.
Cloud Governance: Rules for Your Cloud Kingdom
Cloud governance is like city planning for your cloud, setting automated rules to prevent chaos. It's used to control costs by blocking expensive VMs and enforce security with required settings.
The Ambassador Pattern: Your App's Diplomatic Sidecar
The Ambassador pattern places a proxy next to your application to handle its network communication, like a diplomat. This adds modern features like monitoring, security, and retries to legacy apps or across languages without changing app code.
The Sidecar Pattern: Offload and Isolate Application Logic
The Sidecar Pattern attaches a helper container to your main application, like a sidecar on a motorcycle. It offloads tasks like logging or proxying, letting you add features without changing the main app's code. The footgun is over-engineering a solution.
Cloud Landing Zone: A Blueprint for Cloud Environments
A Cloud Landing Zone is a pre-configured, secure foundation for your cloud applications, like a city grid with utilities ready for new buildings. It provides shared services like networking and identity, ensuring consistency for large organizations.
AWS Well-Architected Framework: A Blueprint for Cloud Health
Think of it as a pre-flight checklist for your cloud architecture. It provides a consistent way to evaluate your systems against six pillars—like security and cost optimization—to ensure they are sound.
The 6 R's: Your Playbook for Cloud Migration
The 6 R's are a strategic menu for migrating apps to the cloud. When planning a move, you use it to decide whether to simply 'Rehost' an app, 'Refactor' it for performance, or even 'Retire' it.
Model Drift: When Good Models Go Bad
A model is a snapshot of the world; model drift is the alarm that fires when the world changes but your snapshot has not. It detects when production data no longer statistically matches the training data, a common issue for models predicting user behavior.
Distributed Model Training: Splitting the Workload
Don't wait for one GPU to finish; use many. Distributed training splits a model's workload across multiple processors to finish faster. It's essential for massive deep learning models.
ML Inference Endpoint: The API for Your Model
An ML inference endpoint is the stable API URL your application calls to get predictions. It separates the public URL from the underlying model, letting you swap models without changing client code.
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.
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.
Dimensional Modeling: Facts vs. Dimensions
Dimensional modeling organizes data like a story: 'facts' are what happened (sales numbers) and 'dimensions' are the who, what, and where (customer, product). It's the foundation for data warehouses, turning raw data into analyzable BI reports.
Batch vs. Stream Processing: When to Process Data
Batch processing is like a nightly report, crunching a full day's data at once. Stream processing is a live feed, handling events as they arrive. Use batch for ETL jobs and stream for real-time fraud detection.
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.
Showback vs. Chargeback: Who Pays the Cloud Bill?
Showback shows teams their cloud costs for visibility; Chargeback makes them pay for it by moving costs to their budget. This helps control cloud spend by making engineers cost-aware. The footgun is treating Chargeback as inherently more mature than Showback.
FinOps Framework: Aligning Cloud Cost with Business Value
FinOps treats cloud spend as a business metric, not just an IT cost. It provides a shared framework for engineering, finance, and business to collaborate on data-driven spending decisions.
Cloud Rightsizing: Stop Overpaying for VMs
Rightsizing stops you from overpaying for idle cloud capacity. It involves analyzing CPU and memory usage to shrink over-provisioned VMs. Always collaborate with application owners before making changes.