Appears on
Articles40
Typical Challenges of Building Your Data Layer
Every data project hits the same walls at the same stages. Knowing where you are helps you avoid the predictable mistakes.
Simple Time Series Prediction Modeling Using Tinybird
Simple time series prediction doesn't require a PhD. This approach gives you forecasts with SQL you already understand.
ClickHouse® Meetup Madrid videos
Last April we had the pleasure to host the ClickHouse® meetup in Madrid.
Try out Tinybird's closed beta
Tinybird started in closed beta with a handful of teams. Here's what we learned and how it shaped the product you use today.
Memory bandwith Napkin Math and more readings from our team members
Real-Time APIs and ETLs, how Github deals with database migrations, memory bandwidth math and more: most interesting articles coming from our flock
Facebook rewrites their messenger application using 20 year old techniques – What our team is reading
Kids learn to make good decisions by making decisions, not by following directions
A high production rate solves many ills – What our team is reading
If you have a high production rate, you have a high iteration rate. For pretty much any technology whatsoever, progress is a function of iteration.
The Guns of August – What our team is reading
Why the birds are the world's best engineers
The most sophisticated piece of software ever written – What our team is reading
What is the most sophisticated piece of software ever written?
New ideas often emerge or are developed in response to extreme needs arising during a social crisis – What our team is reading
World War II, for example, forced innovation or accelerated development and commercialization of the jet engine, pressurized aircraft cabins, helicopters, at...
Getting real – What our team is reading
The best software has a vision. The best software takes sides. When someone uses software, they’re not just looking for features.
The Fremen – What our team is reading
If you want to know how to work with new or limited resources, find a population that’s used to not having many alternatives.
How we processed 12 trillion rows during Black Friday
In this post we explain the data architecture, infrastructure and how we scale our real-time analytics service with ClickHouse®.
Product design: how our SaaS integrates with git
Analytics data projects are code and code should be in a repo.
ClickHouse® tips #1: calculating aggregations after a date
Tips and recipes to learn how to make the most of ClickHouse®, curated weekly by the Tinybird team. Part 1.
Publish SQL-based endpoints on NGINX log analysis
Building a highly scalable log analytics tool with Tinybird and exporting your queries as an API.
Clickhouse®, Open Source and Tinybird
Tinybird runs on ClickHouse but adds what's missing: APIs, auth, scaling, and deployment. Same engine, completely different experience.
ClickHouse® tips #3: the transform function
Using the transform function to join two tables when joinGet is not available. Part 3.
Starting with Kafka
Starting with Kafka feels overwhelming. This guide cuts through the complexity and gets you streaming data in hours, not weeks.
Spatial Indexing aids Finding which Polygons contain a Point
Speed up your queries by using a spatial index to select fewer polygons before testing if a point is inside a polygon.