Intermediate everything in DevOps & Cloud, page 22
Linux Namespaces: A Virtual Slice of the OS
Linux namespaces give a process a virtualized slice of OS resources, like its own process tree or network stack. This is the core magic behind containers, providing the illusion of a separate machine without the overhead of a full virtual machine.
Cloud Disaster Recovery: Planning for Failure
Cloud DR isn't about backups; it's a business continuity plan defining acceptable downtime (RTO) and data loss (RPO). It's for when a whole region fails or a bad deploy corrupts data. The footgun is having a plan but never testing it, creating false security.
Cloud Operating Model: Your Org's Playbook for the Cloud
A Cloud Operating Model is your org's playbook for balancing new cloud capabilities with existing systems. It's used to manage the transition to the cloud with minimal disruption. The footgun is assuming your old processes will work unmodified.
ML Model Registry: A Version Control System for Models
A model registry is like Git for machine learning models: a central hub to version, store, and manage them. It's crucial for MLOps, letting teams track which version is in production, compare performance, and roll back. The footgun is using it as just storage.
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 Alerting: Your System's Automated Smoke Detector
Cloud alerting is your system's smoke detector, watching key metrics and screaming when something's wrong before it becomes a fire. It's used to flag high CPU, failing health checks, or security anomalies.
Cloud Monitoring: Metrics, Time Series, and Resources
Cloud monitoring metrics are numerical measurements of a resource over time. They are used to build dashboards, trigger alerts when a threshold is crossed, and analyze performance for services like VMs or databases.
Automate Storage Costs with Lifecycle Policies
Object storage lifecycle policies are automated rules that move or delete data as it ages to save money. They're ideal for logs or backups, transitioning them to cheaper "cold" storage over time.
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.
What is Cloud Native Architecture?
Cloud Native refers to a collection of practices and open-source projects, like Kubernetes, governed by the CNCF. It's used by enterprises to build modern products and services.
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.
Cloud Center of Excellence (CCoE): Your Internal Cloud Guides
A Cloud Center of Excellence (CCoE) is your company's internal cloud consulting team, centralizing expertise to guide adoption. It creates reusable patterns, manages costs, and ensures security, helping large orgs scale.
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.
Hyperparameter Tuning for LLM Inference
Control an LLM's creativity versus predictability by tweaking its inference parameters. This is crucial for tasks like generating structured JSON versus creative text. The footgun is changing parameters without a clear goal, leading to chaotic output.
Feature Store: The Single Source of Truth for ML
A feature store is the single source of truth for ML models, acting as a central kitchen for prepped ingredients (features). It's used to ensure the same feature logic is applied in both training and real-time inference, preventing model drift.
MLOps: Applying DevOps to Machine Learning
MLOps applies DevOps principles to automate the machine learning lifecycle, creating an assembly line for models. It's for moving from notebooks to production systems that retrain automatically.
OLAP Cube: Pre-Aggregating Data for Fast Analysis
An OLAP cube is like a Rubik's Cube for your data, pre-calculating answers to complex business questions. It powers BI tools, letting you 'slice and dice' sales data by region and time for fast reports. The footgun: data is typically stale, not real-time.
Lambda Architecture: Batch and Stream for Big Data
Lambda Architecture splits data into two paths: a slow, comprehensive batch layer and a fast, real-time stream layer. It's used in big data systems needing both historical accuracy and live views.
Apache Airflow: Code-Defined Data Pipelines
Airflow lets you define, schedule, and monitor complex data workflows as code, replacing brittle cron jobs. It's used for ETL jobs or ML training pipelines. The footgun is treating it as a data processing engine; it's an orchestrator, not the worker.
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.
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