The Tinybird blog
- · Databases, Product
Dynamic API reponses based on endpoint parameters
Make your Tinybird real-time API endpoints to return data at different resolutions depending on the selected temporal range.Read more... - · Product, Ux
Tinybird Changelog: sharing pipes
Sharing data is hard - here is our alternative to sending a CSV file by emailRead more... - · What-we-are-reading
The Guns of August – What our team is reading
“Why the birds are the world's best engineers"Read more... - · Databases, Product
Enriching Kafka streams for real-time queries
If you are using Kafka to capture large quantities of events or transactional data, you are probably also looking for ways to enrich that data in real-time. Here is how to do it with TinybirdRead more... - · What-we-are-reading
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, the progress is a function of how many iterations do you have, and how much progress do you make between each iterationRead more... - · Databases
Create a static application to analyze +50M github events with Zeit and Tinybird
Creating applications just using HTML, javascript and CSS is easier than ever with new frameworks and platforms like Zeit. Let see how to create a simple analytics application that queries 50M rows in real time without backendRead more... - · What-we-are-reading
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 directionsRead more... - · What-we-are-reading
Driving a porsche as far as the moon – What our team is reading
Hiring in start-ups, mental models for product managers, driving a porsche as far as the moon and more: most interesting articles coming from our flockRead more... - · What-we-are-reading
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 flockRead more... - · Changelog, Product
Update your analytical data selectively
Updating specific records in your analytical database couldn't be easier with Tinybird's new 'replace with condition' functionalityRead more... - · Tinybird
When things go wrong: how we handle technical incidents and service disruptions at Tinybird
Part of providing an outstanding level of service is to react quickly and professionally when things go wrong. This is an account of a recent service disruption and how we handled it.Read more... - · Changelog, Product, Ux
Tinybird Changelog: New User Experience for Data Exploration and API building
During the last few weeks we’ve been studying the best way of building and organizing large applications using Tinybird Analytics, and, in addition to a few improvements to our APIs, we’ve completely redesigned the User Interface of the Data Pipes screen.Read more... - · Product
The one cron job that will speed up your analytical queries in Postgres a hundred fold
What do you do when your transactions table in postgres has grown way too big to handle analytical queries? How do you answer business questions when it could take minutes to even get a `SELECT count(*) FROM transactions` going?Read more... - · Product
Real-time analytics API at scale with billions of rows
How to create an analytics API that deals with billions of rows in a matter of minutes.Read more... - · Conference
Tinybird at South Summit Madrid 2019
We will be demoing Tinybird Analytics thanks to Google for Startups at South Summit Madrid, one of the best places to connect with other technology startups, investors and corporates.Read more... - · Product, Ux
Tinybird Changelog: Improving support for replacing or appending new data to existing Data Sources
One of the foundational ideas of Tinybird Analytics is resiliency and consistency under high frequency or big data updates.Read more... - · Product
Our focus on Speed
There is no good way to uncover new insights underlying terabytes of data unless you make the process of working with it tremendously rewarding and fast.Read more... - · Product
Learnings and results of our first User Testing sessions
Read more...In Product Development, “User Testing” is often easier to talk about than to actually do: it is hard to find the time to reach out to users or volunteers, to schedule the interviews, to prepare scripts, to gather notes and to review the outcome of the sessions with the teams.
- · Product
Try out Tinybird's closed beta
Working with large amounts of data is challenging, but we believe it should not be complex. Everybody is waking up to data and its possibilities. We constantly hear from our customers and prospects how they are trying to “Democratize access to Data” across their organizations, how they want to enable their teams to make data driven decisions at will, to generate insights that will better inform their decisions.Read more... -
Tinybird Changelog: Faster csv import and Introducing Auth tokens with SQL filters
Read more...During the last couple of weeks, we’ve made major improvements to our csv import process, increasing its speed and reliability, getting almost a 2x performance. Tinybird Analytics is now able to ingest around 680,000 rows per second – in the smallest Tinybird paid account.
- · Data, Clickhouse
ClickHouse Meetup Madrid videos
Read more...Last April we had the pleasure to host the ClickHouse meetup in Madrid. Altinity’s team normally organize meetups in different cities where local developers talk about their experiences using the technology, to later have a session about the ClickHouse roadmap by Yandex and Altinity. As you might noted already, we are huge fans of ClickHouse (the core of Tinybird Analytics) and it’s awesome to be able to talk to the people behind the technology.
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Simple and effective time series prediction modeling using Tinybird Analytics
Read more...Time series predictions are one of the most common use cases you can find. Predicting the future, enables you to get ready for it (and act accordingly) so, as you would expect, it is something every company would love to do. Good news is that there are many methods to do it: from sophisticated Machine Learning algorithms or advanced forecasting libraries like prophet to simpler approaches based on simpler statistical foundations, like the one we describe below.
- · Data
Typical challenges of building your data layer. Things we've learnt from dozens of growing companies
Read more...When you start a digital product you usually put your data in a database. It does not matter if it is a simple text file, an excel spreadsheet or a managed Postgres instance on the cloud, your data always lives somewhere. Data has been a key component of any digital product for a long time now, but it hasn’t been until recently that we’ve started to devote significant resources to aggregating and making use of it. Even if you think you are devoting enough energy to data within your organization, most probably you aren’t.