Tinybird blog

Product

Changelog #20: Data Source descriptions and beta testing of Parquet ingestion

Document your Data Sources by adding descriptions just like you do for pipes and nodes

Company

The realtime data stack. What’s next for Tinybird.

Sign up and start turning data into realtime insights, actions and business value.

Product

Tinybird is out of beta and open to everyone

Sign up and start turning data into realtime insights, actions and business value.

Company

Welcoming new senior leaders to Tinybird

Tinybird announces four new senior leaders to help deliver even more value to customers at a key stage of growth.

Tinybird Examples

Visualizing your Twitter timeline sentiment with Tinybird

What if you could measure happiness and sadness, the peaks and troughs, just by analyzing the sentiment of your Twitter timeline?

Product

Changelog #19: Adding new columns in high-frequency ingestion and more

New columns in high-frequency ingestion, passing parameters between pipes and design improvements

Product

Tiny Updates: Drag and Drop to Rearrange Nodes in a Pipe

New feature: dragging and dropping to rearrange the nodes in a Pipe.

Product

Changelog #18: High-frequency ingestion, handling NDJSON files and more product enhancements

High-frequency ingestion, handling NDJSON files, cascading partial replaces and more product enhancements

Product

Add data from CSVs with different column orders

Add data to a Data Source from a CSV, even if the columns are in a different order to the one used when the Data Source was created.

Tech

Spatial Indexing aids Finding which Polygons contain a Point

Speed up your queries by using a spatial index to select fewer polygons before testing if a point is inside a polygon.

Tech

From Kafka streams to data products

Productize data ingested via Kafka using API endpoints with Tinybird

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.