Data & Analytics Skill
Airflow DAG Patterns
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and…
$15
Projection Patterns
Build read models and projections from event streams.
Build read models and materialized views from event streams for CQRS read sides, complete with checkpointing, idempotent handlers, and disposable rebuilds. It turns raw event history into fast, query-optimized tables, search indexes, and aggregates that survive crashes and can be reconstructed from scratch at any time. Every pattern ships with the transaction and monitoring discipline that keeps read models trustworthy under at-least-once delivery.
$15 one-time
Add to a kit → Prices include 20% VAT. · Forged on real agency work · one-time, no lock-in
- Type Skill
- Category Data & Analytics
- Delivery Email · instant
- License One-time
Inside the run · no black box
See the actual work before you buy it.
A read model you cannot rebuild is a liability. Every projection is disposable by design, updated exactly once per event, and watched for lag down to the second.
- One read model per query pattern: each UI or report need gets its own denormalized table (a flat order summary, a daily sales aggregate, a search index), instead of bending a single schema to serve every screen. New need means new projection, never a riskier rewrite of the existing one.
- The projector loop runs continuously: read events from checkpoint plus one in batches, dispatch each by event type to its handler, apply to the read table, save the new checkpoint position.
- Exactly-once where it matters: the read-model update and the checkpoint write share one database transaction. Where they cannot, handlers are made idempotent with event_id dedup checks and UPSERT, so a replayed event never double-increments a counter.
- Multi-table updates stay atomic: when one event touches both a summary table and a history table, both writes sit in the same transaction. Cross-store projections (database plus search engine) go through an outbox instead.
- Every projection is disposable by design: a rebuild path resets the checkpoint, truncates the read tables and replays the full stream, with a blue-green switch so the old data keeps serving queries during the rebuild.
- Lag is watched, not hoped about: store position minus checkpoint is emitted as a metric per projection, with thresholds at 1 second warn, 5 seconds critical and 30 seconds page.
One power source. 6 lines out.
projection-patterns · core
core active · 6 lines
- ✓ cqrs read sides and mate…
CQRS read sides and materialized views
- ✓ real-time dashboards from
Real-time dashboards from event data
- ✓ search indexes built from
Search indexes built from events
- ✓ daily and aggregate repo…
Daily and aggregate reporting tables
- ✓ customer activity rollups
Customer activity rollups across streams
- ✓ rebuilding read models a…
Rebuilding read models after schema changes
Yours to keep.
Drag time forward. Watch what stays.
Forever
That's what owning means.
The rented stack
ai writing tool: subscription
expired · access lostanalytics suite: subscription
expired · access lostdesign platform: subscription
expired · access lost(nothing left)
Your forge
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Query-optimized read tables that need no JOINs at runtime
license: perpetual -
Crash-safe recovery via persistent checkpoints
license: perpetual -
Zero-downtime read model rebuilds from event history
license: perpetual -
Duplicate events handled safely with idempotent handlers
license: perpetual
subscriptions expire · deeds don't
Everything in the box.
Pick a piece up. Watch it work.
Base Projection class plus a continuous Projector with batch processing
6 parts · one working system · ships instantly by email
This wasn't forged for everyone.
- Not for you if you'd rather rent a tool than own one.
- Not for you if you want someone else to run your stack.
- Not for you if you're happy guessing.
Backend engineers building event-sourced systems who need fast, rebuildable read models without sacrificing data consistency.
then this was forged for you.Works with
Universal by design: these run in any AI. Delivered in the open Agent Skills + MCP format (native in Claude); ChatGPT, Gemini, Cursor and Copilot adapt the same files their own way.
- Claude Native format
- ChatGPT Adapts via open standards
- Gemini Adapts via open standards
- Cursor Adapts via open standards
- Copilot Adapts via open standards
Catch what's on your mind.
the air is clear. nothing between you and the forge.
catch a spark: the forge will answer
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Do I need a full event-sourcing setup before this is useful?
Yes, the patterns assume your source of truth is an event stream, since read models are built and rebuilt from event history. If you run a plain CRUD database with no events, there is nothing for a projection to consume.
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How do the projections survive crashes and duplicate events?
A persistent checkpoint store records the last processed position so a restarted projector resumes instead of reprocessing everything, and idempotent handlers make duplicate events under at-least-once delivery safe. Multi-table updates happen inside one DB transaction to stay atomic.
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Does it manage the event store or message broker itself?
No. It gives you the Projection base class, the continuous Projector with batch processing, and lag monitoring with WARN/CRITICAL/PAGE thresholds, but the event store and delivery infrastructure are yours to run.
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How is it delivered?
By email right after purchase: ready to run, downloaded instantly, no setup wait.
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One-time or subscription?
A one-time purchase; no subscription or hidden fees. VAT (20%) is included.
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Can I get a refund?
As a digital product, it can’t be refunded once downloaded. That’s why we show exactly what’s inside and who it’s for, right here.