Learn how to accelerate
realtime analytics.

Learn tips & tricks from industry experts, and take your real-time analytics APIs to the next level.

Our guides

We’ve carefully curated a set of short guides to help you get the most out of your Tinybird account. Take a look.

Getting started

First time using Tinybird? Here you'll learn all the basics you need to start ingesting, transforming and exposing data with Tinybird Analytics.

Ingesting data

Tinybird can ingest data from lots of different sources. Learn about them all with these guides.

Transforming data

Here you will find advanced ways in which you can use Tinybird to transform your data.

Exposing 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.

Intro to ingesting data with Tinybird

Easy

Tinybird can import huge amounts of data using our UI, our API and our CLI

Intro to transforming data with Tinybird

Easy

Learn how to use pipes to explore, combine and transform data from multiple data sources

Intro to exposing data with Tinybird

Easy

In this guide you'll learn how you can create API endpoints in Tinybird in a matter of minutes to let others consume it

Coming from Postgres? Read this

Intermediate

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.

Intro to the CLI and Docker

Easy

Learn how to setup a real data project using the CLI

Best practices for writing faster SQL queries

Easy

In this guide, you will learn the best practices you should apply to improve the performance of all your queries.

Replacing and deleting data in your Tinybird Data Sources

Intermediate

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

High-frequency Ingestion

Intermediate

Ingesting at thousands of requests per second to Tinybird using v0/events

Syncing data with cron jobs or GitHub actions

Intermediate

Using a cronjob to replace data on Tinybird periodically and keep it synced with data on its origin

Using Quarantine Data Sources

Easy

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

On-the-fly health checks

Intermediate

Learn how to create a pipe to monitor the quality of the data that gets periodically ingested to your datasources on Tinybird

How to ingest NDJSON data

Intermediate

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

Fine-tuning your CSV files for fast ingestion

Intermediate

Formatting your CSVs properly can lead to 5-10X ingestion speed improvements on Tinybird. Learn how to do it here.

Syncing data from S3 or GCS buckets

Intermediate

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.

Streaming via RudderStack

Intermediate

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.

Ingestion from a Notebook

Easy

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.

Calculating data on ingestion with Materialized Views

Easy

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

Improve Endpoint Performance with Materialized Views

Intermediate

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

Speeding up slow joins on ClickHouse with the Join engine

Intermediate

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.

Deduplication Strategies in ClickHouse

Intermediate

Deduplicating data on query time, doing upserts on ClickHouse and using Materialized views to do last-point analytics.

Handling data privacy in Serverless Analytics APIs

Intermediate

With Tinybird, you can avoid all the hassle behind securitizing and filtering your data in a multi-user application with no extra backend needed

Using dynamic parameters for changing aggregation types on-the-fly

Advanced

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

Versioning your pipes

Intermediate

This guide will show you how to version your pipes using Tinybird's CLI

Implementing test strategies

Intermediate

This guide will show you how to run automatic tests on your API endpoints as part of your continuous integration workflow

Sharing endpoint docs with development teams

Easy

In this guide you'll learn about the different possibilities for sharing your API endpoints documentation with other developers that tinybird brings to you

Advanced dynamic endpoints functions

Advanced

How to make the most of some of the least-known template functions to create complex queries with dynamic parameters on Tinybird

Consume APIs in a Notebook

Easy

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.

No guides found

Replacing and deleting data in your Tinybird Data Sources

Intermediate

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

High-frequency Ingestion

Intermediate

Ingesting at thousands of requests per second to Tinybird using v0/events

Syncing data with cron jobs or GitHub actions

Intermediate

Using a cronjob to replace data on Tinybird periodically and keep it synced with data on its origin

Using Quarantine Data Sources

Easy

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

On-the-fly health checks

Intermediate

Learn how to create a pipe to monitor the quality of the data that gets periodically ingested to your datasources on Tinybird

How to ingest NDJSON data

Intermediate

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

Fine-tuning your CSV files for fast ingestion

Intermediate

Formatting your CSVs properly can lead to 5-10X ingestion speed improvements on Tinybird. Learn how to do it here.

Syncing data from S3 or GCS buckets

Intermediate

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.

Streaming via RudderStack

Intermediate

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.

Ingestion from a Notebook

Easy

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.

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