Architecture Blueprints

3 real-world CDC patterns — Streaming Lakehouse, Batch Warehouse, DynamoDB Event-Driven

Brokers, Partitions, ISR, Consumer Groups, Offsets — complete internals for senior engineers

Bronze → Silver → Gold — Lakehouse data quality layers on AWS

Driver → Executors → Jobs → Stages → Tasks → Shuffle — how Spark runs your code

Batch + speed vs stream-only — two paradigms for data processing

DAGs, Operators, Sensors, XCom, MWAA — orchestrating data pipelines

MSK patterns — CDC, streaming ETL, event-driven, real-time analytics

AWS messaging: when to use which for data pipelines

Events as truth, separate read/write models — on AWS

Dimensional modeling — facts, dimensions, and when to flatten everything

Slowly Changing Dimensions — tracking historical changes in dimensions

Domain ownership, data products, federated governance — decentralized data at scale

S3 + Iceberg + Glue + Lake Formation — end-to-end lakehouse architecture

Leader node, compute nodes, slices, dist keys, sort keys, WLM, Spectrum

JobManager, TaskManagers, checkpoints, watermarks, exactly-once — true real-time

Open table format comparison — features, trade-offs, and when to use which

Modern ELT with dbt vs traditional ETL — when to transform where

Warehouse → operational systems. Push analytics back to tools your team uses.

Crawlers, ETL jobs, DPU tuning, bookmarks, Data Quality, Schema Registry

Serverless orchestration vs managed Airflow — when to use which on AWS

Master node types, instance fleets, EMRFS, and EMR Serverless for Spark/Hive at scale.

DQ gates, validation engines, quarantine patterns — catch bad data before it poisons downstream.

Lake Formation, catalog, lineage, PII detection, audit — govern your lake without killing velocity.

Freshness, volume, schema, distribution — the five pillars of knowing your data is healthy.

Three stream processing engines compared — architecture, state, windowing, and exactly-once.

Partitions, skew, joins, AQE, caching — the techniques that turn a 4-hour job into 20 minutes.

Evolve schemas without breaking consumers — backward, forward, full compatibility explained.

Defense in depth — network, encryption, access control, audit, and PII detection for your lake.

Not everything needs real-time. Choose the right processing model for latency, cost, and complexity.

Rate limits, pagination, retries, state management — the unglamorous art of pulling data from APIs.

Online + offline serving, point-in-time correctness, feature reuse — bridge the gap between DE and ML.

Model DAGs, materializations, testing, snapshots, incremental — the dbt patterns that actually matter.

Millions of tiny files kill query performance. Understand why, and the 5 ways to fix it.

Hive-style, Iceberg hidden, Redshift distribution — partition right and queries fly. Partition wrong and they crawl.

Run it twice, get the same result. MERGE, partition overwrite, dedup, checkpoints — bulletproof your ETL.

S3 tiering, Spot instances, query optimization, reserved capacity — stop lighting money on fire.

Testing pyramid, CDK deploy, blue-green releases, rollback — ship data pipelines like software.

Hub-Link-Satellite modeling for auditable, scalable enterprise data warehouses

Row vs column stores, indexing strategies, consistency models — the foundational split

Warehouse rigidity vs lake flexibility — and why lakehouse bridges the gap

Splitting data for scale — within a node (partitioning) and across nodes (sharding)

File formats, sizing, partitioning, lifecycle — the foundation of every data lake

Message delivery guarantees in distributed systems — the tradeoffs that matter

When producers outrun consumers — patterns for handling overload without data loss

EventBridge, SNS, SQS, Step Functions — loosely coupled, event-driven data pipelines

CloudWatch, alarms, dashboards, SLA tracking — know when your pipeline breaks before users do