Learn tips & tricks from industry experts, and take your real-time analytics APIs to the next level.
We’ve carefully curated a set of short guides to help you get the most out of your Tinybird account. Take a look.
First time using Tinybird? Here you'll learn all the basics you need to start ingesting, transforming and exposing data with Tinybird Analytics.
Tinybird can ingest data from lots of different sources. Learn about them all with these guides.
Here you will find advanced ways in which you can use Tinybird to transform your data.
Tinybird also lets you create secure and robust API endpoints from views of your data in a matter of seconds. Learn how to do it here.
Tinybird enables using best software engineering practices when working with big amounts of data, like testing and version control. Here you'll learn how to integrate them into your data projects too.
We are used to saying ClickHouse is like a Formula 1, fast as hell but hard to drive. Sometimes a partition key, an index, or a simple change in a SQL query leads to 10x faster queries. Learn how to exploit all the power behind ClickHouse here
Tinybird can import huge amounts of data using our UI, our API and our CLI
Learn how to use pipes to explore, combine and transform data from multiple data sources
In this guide you'll learn how you can create API endpoints in Tinybird in a matter of minutes to let others consume it
A guide on how to rewrite common queries from Postgres on ClickHouse. A big part of the syntax is the same, and this guide will teach you the subtle differences.
Learn how to setup a real data project using the CLI
In this guide, you will learn the best practices you should apply to improve the performance of all your queries.
Ingesting at thousands of requests per second to Tinybird using v0/events
Data deletion operations are pretty common in transactional databases where your operational data lives. Often due to a data quality process in your operational database you will also need to update or delete your analytical data in Tinybird
Using a cronjob to replace data on Tinybird periodically and keep it synced with data on its origin
In this guide you'll learn how to use the quarantine table to detect and fix errors on any of your datasources, both in development and production environments
In this guide, you'll learn how to ingest semi-structured NDJSON data to Tinybird. A typical scenario consists of having a document-based database or generated events from your web application
Learn how to create a pipe to monitor the quality of the data that gets periodically ingested to your datasources on Tinybird
Formatting your CSVs properly can lead to 5-10X ingestion speed improvements on Tinybird. Learn how to do it here.
In this guide you'll learn how to automatically ingest and synchronize all the CSV files in an AWS S3 or Google Cloud Storage bucket to a Tinybird Data Source.
By using the events ingestion endpoint for high-frequency ingestion as a Webhook in RudderStack, you can stream customer data in real time to Data Sources.
Data can be ingested into Data Sources from notebooks in several different ways. Recent updates to Wikipedia are used as example data to show the range of ways to ingest data to Tinybird from a pandas DataFrame, with a bonus option of streaming events directly to Tinybird.
In this guide, you'll learn how to use materialized columns to easily calculate or transform data on the fly, as it is ingested, and avoid spending hours running costly ETL processes on your end
Learn the details of how Materialized Views work in Tinybird to become an MV master.
Learn how to persist the result of the transformation in materialized views to have different organized, pre-filtered or pre-aggregated views of your source data for different use cases
Deduplicating data on query time, doing upserts on ClickHouse and using Materialized views to do last-point analytics.
When you have hundreds of millions or billions of rows, joins get slow even on ClickHouse. Here we'll show you how to make joins on large amounts of data 100X faster using the JOIN Table Engine.
With Tinybird, you can avoid all the hassle behind securitizing and filtering your data in a multi-user application with no extra backend needed
In this guide, you'll learn how to define API endpoints Tinybird that accept dynamic parameters that will be passed to the underlying queries so that your frontend is performant, independently of the parameters that you pass to the endpoints
This guide will show you how to version your pipes using Tinybird's CLI
This guide will show you how to run automatic tests on your API endpoints as part of your continuous integration workflow
In this guide you'll learn about the different possibilities for sharing your API endpoints documentation with other developers that tinybird brings to you
How to make the most of some of the least-known template functions to create complex queries with dynamic parameters on Tinybird
Notebooks are a great resource for exploring data and generating plots. In a Colab notebook, we read from a Data Source of updates to Wikipedia to explore consuming data from queries using the Query API and from API endpoints using the Pipes API.
Learn how to use pipe_stats and pipe_stats_rt to monitor endpoint performance - and find opportunities to optimize them.
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