The Tinybird blog
- · ProductWhat 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...
- · ProductHow to create an analytics API that deals with billions of rows in a matter of minutes.Read more...
- · ConferenceWe 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, UxOne of the foundational ideas of Tinybird Analytics is resiliency and consistency under high frequency or big data updates.Read more...
- · ProductThere 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
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
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.
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
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.
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
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.