architecture lakehouse warehouse

Lakehouse Tables vs Warehouse Tables: It’s About Guarantees

Pick the engine based on constraints and workload, not hype.

azure cdc eventhubs

Change Data Capture on Azure: Event Hubs, Debezium, and Reality

Azure can do CDC fine—if you respect throughput units and partition keys.

backfill orchestration sre

The Practical Guide to Backfills Without Setting the Warehouse on Fire

Backfills are load tests you run in production. Plan accordingly.

catalog metadata hive

Indexing Isn’t Just for OLTP: Metastore Design Matters

If your metastore is slow, everything is slow. Welcome.

cdc deletes iceberg

The One True Pattern for ‘Delete’ Events in Append-Only Lakes

Soft deletes are easy; correct deletes are architecture.

cdc scd2 delta data-quality

Your CDC Pipeline Is Lying (and Your SCD2 Table Knows It)

Designing CDC with exactly-once semantics is less about magic and more about being painfully explicit.

contracts governance quality

Data Contracts: Because ‘It Worked Yesterday’ Isn’t a Spec

Define expectations at the boundary and enforce them automatically.

cost finops optimization

Cost Engineering for Data: Because Finance Eventually Finds You

Treat cost as a first-class metric, or it will treat you as a line item.

data-mesh architecture governance

Data Mesh: The Part Where You Rebuild Central Governance Anyway

Decentralize ownership; centralize standards. Yes, both.

dbt snapshots cdc

dbt Snapshots: Helpful, Until You Pretend They’re CDC

Snapshots are a modeling tool, not a streaming system.

dedup spark batch

Dedup at Scale: Hashes, Windows, and the Art of Not Double-Charging

Dedup is easy until you need correctness and speed at the same time.

delta performance cost

Delta Lake OPTIMIZE Isn’t a Spell. It’s a Bill.

Compaction improves reads; it also writes a lot. Surprise.

eventing architecture queues

Fan-out Pipelines: Congratulations, You Built a DoS Machine

A little buffering and quotas save a lot of pain.

flink streaming watermarks

Event Time vs Processing Time: Choose Wrong, Debug Forever

Late data is inevitable; your design should be, too.

flink watermarks

Watermark Strategy: You Can’t Outrun Late Data

Pick a lateness budget; enforce it; move on.

iceberg delta retention

Time Travel Isn’t Backup: Snapshots, Retention, and Regret

Retention policies are not optional; your lawyers will agree.

iceberg schema catalog

Schema Evolution with Iceberg: The Grown-Up Way to Break Things

Evolving schemas safely requires contracts, not vibes.

immutability architecture

The Case for Immutable Data: Mutable Data Is How You Get Ghost Bugs

Immutability makes pipelines debuggable and reproducible.

json schema performance

JSON in the Lake: Sure, If You Hate Yourself

Store JSON raw, but normalize early if you like money.

kafka streaming compaction

Kafka Compaction: The Feature You Ignore Until It Eats Your Cluster

Compaction is incredible—unless you mis-key and turn history into confetti.

kafka transactions exactly-once

Exactly-Once: The Marketing Term You Can Actually Implement

Exactly-once is achievable in narrow pipelines if you respect the rules.

kubernetes batch ops

Kubernetes for Data Jobs: The Part Where YAML Becomes a Lifestyle

It’s great until you run 5000 pods and discover quotas.

lakehouse architecture governance

Lakehouse Medallion Layers: Bronze/Silver/Gold… or Just Bronze Forever

Layers only help if you enforce invariants at each boundary.

lakehouse contracts

Medallion Layering with Real Contracts (Not Just Folder Names)

A folder is not a boundary. A validated schema is.

lakehouse partitioning spark

Partitioning: Stop Worshipping the Date Column

Partitioning is an I/O strategy, not a religious practice.

lambda batch streaming

Lambda Architecture Without the Trauma

Hybrid batch+streaming can work—if you pick a single source of truth and stop duplicating business logic.

lineage observability metadata

OpenLineage + DataHub: Observability for People Who Like Sleep

Lineage is a debugging primitive, not just a compliance checkbox.

llm embeddings architecture

Vector Search Pipelines: Embeddings Are Data Engineering Too

Embeddings drift; treat them like any other dataset.

metadata orchestration

Metadata-Driven Pipelines: Dynamic Doesn’t Mean Uncontrolled

Drive config from metadata, but validate like a paranoid adult.

ml feature-store

Feature Stores: Centralize Reuse, Decentralize Blame

A feature store is a contract system with extra steps.

multi-tenant security k8s

Multi-tenant Data Platforms: Isolation or Incident

Noisy neighbors aren’t cute when they burn your CPU budget.

observability tracing

Observability: Trace IDs for Data Pipelines (Yes, It Works)

Correlate events across ingest → transform → serve. Debugging gets boring.

orchestration idempotency airflow

Idempotency: Because Retries Are Not a Personality Trait

If your pipeline breaks on retry, it’s not ‘flaky’—it’s wrong.

parquet compression cost

Compression Codecs: ZSTD Is Great Until Your CPU Says No

Compression trades bytes for cycles; measure both.

parquet performance

Column Pruning and Predicate Pushdown: The Two Free Lunches

If you’re scanning all columns, you’re doing it wrong. Yes, you.

quality lakehouse

Bronze Table Quality Gates: Yes, Even Bronze

If you ingest garbage, you’ll analyze garbage. That’s not ‘agile’.

reliability replay backfill

Survivable Pipelines: Replay, Reprocess, Reconcile

If you can’t replay, you can’t recover.

s3 adls consistency

Object Storage Consistency: Read-After-Write Is Not a Love Language

Assume eventual consistency somewhere; design around it.

scd warehouse modeling

Slowly Changing Dimensions Without Slowly Changing Sanity

SCD patterns are deterministic—your tooling should be too.

scheduling time

Batch Windows: Stop Using ‘Daily’ as a Data Type

Time zones and DST are not edge cases; they’re the default.

security rls governance

Row-Level Security: The Part Where Governance Meets Reality

Security models must be enforceable at query time—period.

serverless architecture

The Annoying Truth About ‘Serverless’ Data

Serverless mostly means ‘someone else runs the servers’. You still pay.

serving performance

Serving Layers: Materialized Views, Caches, and the Myth of ‘Realtime’

Realtime is a budget decision.

spark performance shuffle

Spark Shuffle Tuning: Because Default Settings Love Chaos

Tune shuffles with evidence, not folklore.

spark-structured-streaming streaming

Micro-batch vs True Streaming: The Cost of Pretending

Micro-batch is great—until your latency SLO is not.

sre observability metrics

SLA vs SLO vs ‘We Promise’—Define It Like You Mean It

If it’s not measured, it’s not real. If it’s not alerting, it’s a chart.