Fiveonefour
@514hq
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Our mission is to bring incredible developer experiences to the data & analytics stack. Northern roots. Friendly vibe. Let's build together.
Joined December 2023
ORMs made transactional databases more dev friendly (types, IDE help, migrations). Analytical databases force you back into raw SQL and self-managed migrations. Inspired by @prisma & @drizzleorm, we asked: should analytics get a similar DX? We teamed up with @ClickHouse to
clickhouse.com
What if we could bring some of the benefits of ORMs to the world of OLAP? Should we just apply the ORMs we already have to analytics?
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Watch how we use the open-source Moose Stack to embed real-time analytics and AI chat into a Next.js app—powered by ClickHouse and Red Panda streaming. Here is the intro, but you can watch the full demo of the future of analytics in applications here: https://t.co/ybhFWpxTUO
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Tomorrow’s the day! Join Chris, ClickHouse and F45 Training as we dive into how real-time analytics transformed their member experience. 📅 November 20 | 9 AM PT | 12 PM ET 🔗
clickhouse.com
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🚨 5 days until we go live with ClickHouse and F45 Training! Real-time analytics, 1B+ data points, and a 70% boost in satisfaction — all in 30 minutes. Save your seat 👇 📅 November 20 | 9 AM PT | 12 PM ET 🔗
clickhouse.com
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The magic behind real-time analytics? Partnership. We helped F45 Training build a scalable data engine that delivers results in milliseconds, using ClickHouse and MooseStack. Learn how on Nov 20 👇 🔗
clickhouse.com
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What does 1 billion data points really mean for a fitness brand? For @F45Training, it’s real-time visibility into performance — across members, workouts, and studios. Join us on Nov 20 to see how real-time data transformed their member experience. 🔗
clickhouse.com
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Can AI be your next data engineer? We think yes—if you go dev-first. Watch the full conversation →
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MooseStack native APIs:
If you want "OLAP in your App", you need to expose your #OLAP data to your App, usually with APIS. In today's part one, #MooseStack's native APIs (coming up, bring your own API framework).
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Modern analytics shouldn’t leave you waiting: they should talk back. Join our own Chris Crane for a free session on integrating realtime + agentic analytics into your app with MooseStack. https://t.co/PVlXMSIEk6
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Customer result: sub-second analytics replace 20–30s waits. Recipe: AI + context (schema/queries) + guardrails (types/engines/sort-key policy). Case study: Mike Klein modeled 100+ views in ~4 hours. Link in reply. #Analytics #DataEngineering
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Moose + SQLModel = Python OLTP → OLAP, end to end. We rebuilt our TypeScript demo in Python to show what really happens when your ORM meets ClickHouse. Same schema, a few more manual seams than typescript. Still way cleaner than the usual ETL spaghetti. Read:
fiveonefour.com
Build Moose OLAP pipelines from Python’s SQLModel. This guide shows how to map OLTP schemas to ClickHouse-ready models with MooseStack.
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I built a code-first, local-first CDC pipeline to ClickHouse using MooseStack. - spins up Postgres, Debezium, Redpanda, ClickHouse in 1 cmd - consume Debezium topics in code - apply stateless transforms - manage ClickHouse tables in code as the sink Repo:
github.com
Easy-to-run demo of a CDC pipeline using Debezium (Kafka Connect), PostgreSQL, Redpanda, and ClickHouse. - 514-labs/debezium-cdc
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CDC from #OLTP to #OLAP, code‑first. A practical path to mirror #Postgres in @ClickHouseDB using @debezium, @redpandadata using #MooseStack. Architecture: Postgres → Debezium (WAL→CDC) → Redpanda topics → ClickHouse (ReplacingMergeTree). Using MooseStack as the dev layer
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OLAP doesn’t update, it merges. When streaming CDC from OLTP → @ClickHouseDB, your engine choice decides what “update” really means. We break down MergeTree, Replacing, Collapsing + and when to use each. https://t.co/izPBstcFtC
#OLAP #CDC #OLTP @debezium @Redpandadata
fiveonefour.com
In OLAP databases like ClickHouse, every transaction is an insert — not an update. This guide explains how CDC (Change Data Capture) streams from OLTP systems behave in ClickHouse and how choosing...
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🚀 We’re launching our “OLAP into your App” series. First up: Optimizing writes to OLAP using buffers. This is the first step in getting data into your OLAP system — from ingest to insight. Over the week, we’ll move from general inserts to Change Data Capture (CDC) and
fiveonefour.com
Understand OLTP vs OLAP insert patterns, how batching impacts ClickHouse performance, and how MooseStack simplifies streaming buffer setup for fast ingestion.
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New guide w/ @ClickHouseDB shows how to add ClickHouse to your @ReactJS app: ✅ CDC from OLTP → ClickHouse ✅ Type-safe, ORM-like TS queries w/ MooseStack ✅ Auto-gen APIs + OpenAPI ✅ Local-first dev env Build a production-ready analytics backend. Read:
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Stop running analytics on your transactional DB. Our latest post with the @ClickHouseDB team shows how to kill loading spinners in your @reactjs app by moving slow analytical queries from Postgres/MySQL/Mongo → ClickHouse: - Mirror data from Postgres/MySQL → ClickHouse w/
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The default data stack: - Analytics: @ClickHouseDB + Moose OLAP - Transactions: @supabase / Postgres - Web: @reactjs
Built analytics on Postgres? You know what happens next: works great initially, then queries slow down as you scale. https://t.co/AbvCWWcewL Here's how to add ClickHouse speed without a full rewrite: - Keep Postgres as source of truth - CDC sync to ClickHouse - TypeScript APIs
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Slow dashboards. Laggy AI chat. Painful data DX. We’re fixing it. MooseStack = OSS toolkit for AI-ready analytics on ClickHouse (TS/Python). Just raised $17M → more shipping, less waiting. ⭐️
github.com
The developer framework for building analytics into your app on top of ClickHouse, Redpanda and other high-performance analytical infrastructure - 514-labs/moosestack
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