Tinybird blog

Tinybird Examples

Operational Analytics in Real Time with Tinybird and Retool

Three steps to create a Retool app or dashboard in minutes to display your operational analytics in real time.

Our Beliefs

The era of JSON data analytics

JSON is the de facto standard for data communication in the web and that's why we are supporting it natively: from a Kafka stream or from local or remote NDJSON files (and very soon in other flavours)

Tech

Splitting CSV files at 3GB/s

Splitting CSV files is a must when dealing with large, potentially larger than RAM, files. But how fast can it be?

Product

Changelog #17: Guided tour, Kafka ingestion improvements and more

Lots has been happening at Tinybird. The team is growing fast and everybody is working to improve the developer experience. Here are some recent features and updates that you may have missed.

Tech

Performance and Kafka compression

The **unmodified** support message we sent to one of our clients outlining potential performance gains through Kafka compression

Data 101

SQL and Python: alerts from predictions

Combine Tinybird with pre-coded models to make predictions, compare data in real time to the predictions and alert.

ClickHouse

ClickHouse Tips #12: Apply Functions to Columns with a Single Call

Clickhouse 21 allows some fancy operations packed into multiple columns with SELECT modifiers.

ClickHouse

ClickHouse tips #11: Best way to get query types

A fast and simple solution to know the types returned by a query

Product

New feature: sharing Data Sources across workspaces

From now on, Data Sources can be shared in a read-only way across multiple workspaces. Maintaining only one ingestion process, data can be made available for...

Product

Changelog #16: Improved Workspace selector, better autocomplete and more

Now you can see how many Workspaces you own, the autocomplete is smarter, the CLI and the Kafka connector are better than ever and more.

ClickHouse

ClickHouse tips #10: Null behavior with LowCardinality columns

Does it work? What's actually inserted?

ClickHouse

Experimental ClickHouse: Projections

ClickHouse tags a major release around once a month, which is an order of magnitude more often than similar projects, and it does it while maintaining speed and stability.

ClickHouse

ClickHouse tips #9: Filling gaps in time-series on ClickHouse

This simple trick will teach you how to fill date and datetime gaps in time-series on ClickHouse

Product

Changelog #15: improved data flow graph, anomaly detection and more

Better visualization of dependencies, Kafka improvements, how to ingest JSON data, CLI enhacements and more.

Data 101

Starting with Kafka

I just want to share my thoughts on Kafka after using it for a few months, always from a practical point of view. I don’t know anything more than the basics ...

Tinybird Examples

Simple statistics for anomaly detection on time-series data

Anomaly detection is a type of data analytics whose goal is detecting outliers or unusual patterns in a dataset.

ClickHouse

ClickHouse tips #8: Generating time-series on ClickHouse

ClickHouse doesn't have a generate_series function yet, but you can achieve the same with other functions. Learn how here.

Product

A big performance boost, adding columns and more

The last weeks have been intense at Tinybird, with lots of new features and product improvements. Let’s explore some of them!

Tinybird Examples

Analyzing artists performance in real-time with Tinybird

It's possible to ingest millions of events per minute to Tinybird via Kafka, and exploit them through real-time data products. Here is a good example of a simple data product that analyzes millions of music streams across different platforms.

Product

New feature: add column to a Data Source

From now on, it's possible to add new columns to existing Data Sources on Tinybird. This is how.

ClickHouse

ClickHouse tips #7: Forward and backfilling null values

Making use of array functions to do it.

Tinybird Examples

Analyzing our own Nginx logs with ClickHouse on Tinybird

How we analyze our own logs in real-time using our own product.

ClickHouse

ClickHouse tips #6: Filtering data in subqueries to avoid joins

Sometimes you can replace joins on ClickHouse using where clauses, having the same performance as with Join engines. Learn how here.

Product

Advanced endpoint functions and more

Advanced dynamic endpoint functions and lot of bug fixes

Product

Changelog: autocomplete uses the full ClickHouse documentation. CLI on MacOS and more

A new Data Source to see your endpoint errors. Better UX for the Data lineage graph. Autocomplete uses the ClickHouse SQL reference. CLI on MacOS working.