> ## Documentation Index
> Fetch the complete documentation index at: https://upstash-qstashminimalrightbar.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Tinybird

> This tutorial shows how to set up a pipeline to stream traffic events to Upstash Kafka and analyse with Tinybird

In this tutorial series, we will show how to build an end to end real time
analytics system. We will stream the traffic (click) events from our web
application to Upstash Kafka then we will analyse it on real time. We will
implement one simply query with different stream processing tools:

```sql
SELECT city, count() FROM kafka_topic_page_views where  timestamp > now() - INTERVAL 15 MINUTE group by city
```

Namely, we will query the number of page views from different cities in last 15
minutes. We keep the query and scenario intentionally simple to make the series
easy to understand. But you can easily extend the model for your more complex
realtime analytics scenarios.

If you do not have already set up Kafka pipeline, see
[the first part of series](./cloudflare_workers) where we
did the set up our pipeline including Upstash Kafka and Cloudflare Workers (or
Vercel).

In this part of the series, we will showcase how to use Tinybird to run a query
on a Kafka topic.

### Tinybird Setup

Create a [Tinybird](https://tinybird.co) account and select a region. Create an
empty workspace. On the wizard screen click `Add Data` button.

![tiny1.png](https://mintlify.s3-us-west-1.amazonaws.com/upstash-qstashminimalrightbar/img/vercel-edge-tinybird/tiny1.png)

In the next screen click on Kafka tab and fill the fields with the credentials
copied from Upstash Kafka console. Key is `username` , secret is `password` .
Select `SCRAM-SHA-256`. Then click on `Connect` button.

![tiny2.png](https://mintlify.s3-us-west-1.amazonaws.com/upstash-qstashminimalrightbar/img/vercel-edge-tinybird/tiny2.png)

If the connection is successful, then you should see the topic you have just
created. Select it and click `Continue` .

![tiny3.png](https://mintlify.s3-us-west-1.amazonaws.com/upstash-qstashminimalrightbar/img/vercel-edge-tinybird/tiny3.png)

In the next screen, you should see data is populated from your Kafka topic. It
should look like the below. Select `Latest` and click `Create Data Source`

![tiny4.png](https://mintlify.s3-us-west-1.amazonaws.com/upstash-qstashminimalrightbar/img/vercel-edge-tinybird/tiny4.png)

Click `Create Pipe` on the next screen.

![tiny5.png](https://mintlify.s3-us-west-1.amazonaws.com/upstash-qstashminimalrightbar/img/vercel-edge-tinybird/tiny5.png)

In the next page, you will see the query editor where you can execute queries on
your data pipe. You can rename the views. Update the query as (replace the
datasource):

```sql
SELECT city, count() FROM  kafka_ds_534681 where  timestamp > now() - INTERVAL 15 MINUTE group by city
```

![tiny6.png](https://mintlify.s3-us-west-1.amazonaws.com/upstash-qstashminimalrightbar/img/vercel-edge-tinybird/tiny6.png)

You should see the number of page view from cities in last 15 minutes. The good
thing with TinyBird is you can chain queries (new transformation node) also you
can add multiple data sources (e.g. Kafka topics) and join them in a single
query.

If you are happy with your query, click on `Create API Endpoint` at top right.
It will create an API endpoint which returns the result for your query.

![tiny7.png](https://mintlify.s3-us-west-1.amazonaws.com/upstash-qstashminimalrightbar/img/vercel-edge-tinybird/tiny7.png)

Copy the curl command and try, you should see the result like below:

```bash
curl --compressed -H 'Authorization: Bearer p.eyJ1IjogIjMyMDM1YzdjLTRkOGYtNDA3CJpZCI6ICJlNTY4ZjVjYS1hNjNiLTRlZTItYTFhMi03MjRiNzhiNmE5MWEifQ.3KgyhWoohcr_0HCt6J7y-kt71ZmWOlrjhYyFa7TaUlA'  https://api.us-east.tinybird.co/v0/pipes/kafka_ds_534681_pipe_2444.json
{
	"meta":
	[
		{
			"name": "city",
			"type": "String"
		},
		{
			"name": "count()",
			"type": "UInt64"
		}
	],

	"data":
	[
		{
			"city": "San Jose",
			"count()": 8
		}
	],

	"rows": 1,

	"statistics":
	{
		"elapsed": 0.000562736,
		"rows_read": 8,
		"bytes_read": 168
	}
}
```

## Conclusion

We have built a simple data pipeline which collect data from edge to Kafka then
create real time reports using SQL. Thanks to serverless technologies (Vercel,
Upstash, Tinybird), we achieved this without dealing any server or
infrastructural configurations. You can easily extend and adapt this example for
much more complex architectures and queries.