# Data Engineering
All Data Engineering notes →1 — AWS Data Stack
The AWS data stack — S3, Glue, EMR, Athena, Redshift, Kinesis, and Lambda — and how they compose into an end-to-end pipeline.
2 — Azure Data Stack
The Azure data stack — ADLS, Synapse, Event Hub, Data Factory, Databricks, and Microsoft Fabric — and how they compose into an end-to-end pipeline.
3 — Google Cloud Data Stack
The Google Cloud data stack — BigQuery, Dataflow, Dataproc, Pub/Sub, Composer, and Cloud Storage — and how they compose into an end-to-end pipeline.
# Observability
All Observability notes →1 — AWS
Covers CloudWatch's metric/log/trace surfaces and where AWS-native telemetry needs augmenting with OTel for cross-account visibility.
2 — Azure
Covers Azure Monitor and Application Insights as the native telemetry surface, and their integration points with an OTel-based pipeline.
3 — Google Cloud
Covers Google Cloud's operations suite (Cloud Monitoring/Logging/Trace) and its native OTLP ingestion path.
4 — Hybrid Cloud
Covers unifying telemetry across on-prem and cloud environments where network topology and identity differ per environment.
5 — Multi Cloud
Covers the added complexity of a telemetry pipeline that must normalize signals from more than one cloud provider's native tooling.
# System Design
All System Design notes →Chapter 1 — Compute Platforms
VMs, Kubernetes, serverless, and containers compared on the operational responsibility each one leaves with your team.
Chapter 2 — Cloud Storage Services
Blob storage, object storage, and distributed file systems, and which durability/latency/cost point each is built around.
Chapter 3 — Multi-Cloud Architecture
Hybrid cloud, cloud migration, and the vendor lock-in trade-offs that make "just go multi-cloud" harder than it sounds.