# View Kafka & Iceberg together
In order to follow this page, it's advised that you [run the demo](/get-started/run-the-demo.md) first.
Streambased surfaces your Iceberg and Kafka data together. Let's see this in action by first finding our Kafka and Iceberg data within their respective systems.
### The Kafka data
You can see your available Kafka data within [AKHQ](http://localhost:9090/ui/docker-kafka-server/topic) ([a dashboard for Kafka](https://akhq.io)).
Your topics are being populated in real time by [Shadowtraffic](https://shadowtraffic.io), and when your 'customers' and 'transactions' topics reach predetermined limits (1,000,000, and 500,000 respectively) they are moved from Kafka to Iceberg.
Once your dashboard looks like this ⬇️
* 1,000,000 'customers' messages now live in Iceberg.
* 10,000 'accounts' messages live in Kafka exclusively (no more data inbound).
* Messages within 'transactions' are being updated in real time (you'll see this if you refresh the page).
### The Iceberg data
Our Iceberg data lives within MinIO (at [http://localhost:9001/browser/warehouse/](http://35.212.149.246:9001/browser/warehouse/coldset%2F); **username**: admin; **password**: password)
The structure of our Iceberg data is such that:
* The `transactions` folder maps onto the Kafka topic of the same name, and it contains historic Kafka data migrated to Iceberg by [I.S.K.](/deploy-and-operate/streambased-platform/iceberg-service-for-kafka-i.s.k..md).
* So too, `customers` maps onto the Kafka topic of the same name, but its data lives exclusively in Iceberg.
* `Branches` does not map onto any Kafka topic; it is exclusively an Iceberg table.
### View the data together
Now let's head to a Jupyter notebook at [this address](http://localhost:8888/notebooks/notebooks/demo_script.ipynb) and see both sets.
Before getting started, make sure to run cells 1–6 below. After running those cells, you should see similar outputs to these for cells 2 and 6:
After this, run the following SQL query:
```python
spark.sql("SHOW DATABASES").show()
```
At which point you will see these namespaces:
```
+---------+
|namespace|
+---------+
| hotset|
| coldset|
| merged|
+---------+
```
A namespace in Iceberg can be understood as a database, and a database in Streambased is a **logical view** of the data. The database is not a copy of your data — the Kafka data and the Iceberg data stay where they are. The `hotset` is the view of the data as found in Kafka, the `coldset` is the view of the data as found in Iceberg, and `merged` is the view of all the data from both Kafka and Iceberg (including their exclusive topics).
{% hint style="info" %}
"Hotset" and "coldset" are key terminology in Streambased's products. You can expect to regularly see both terms.
{% endhint %}
As you can see, Kafka data, Iceberg data, and Kafka & Iceberg data all exist as databases to be queried. Next, let's query each of those databases in turn using PySpark.
---
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