<p>What the title says, thanks to the latest version (v0.5) of <a target="_blank" href="https://learn.microsoft.com/en-us/fabric/data-science/semantic-link-overview?wt.mc_id=MVP_335074">Semantic Link</a></p>
<pre><code class="lang-python">!pip install --upgrade semantic-link --q <span class="hljs-comment">#upgrade to semantic-link v0.5</span>

<span class="hljs-keyword">import</span> sempy.fabric <span class="hljs-keyword">as</span> fabric
<span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd 

df = pd.concat([fabric.list_items(workspace=ws) <span class="hljs-keyword">for</span> ws <span class="hljs-keyword">in</span> fabric.list_workspaces().query(<span class="hljs-string">'`Is On Dedicated Capacity` == True'</span>).Id], ignore_index=<span class="hljs-literal">True</span>)
df
</code></pre>
<p><img src="https://cdn.hashnode.com/res/hashnode/image/upload/v1705502685880/8c249494-de95-49bf-ad88-e3b9a20a0e49.png" alt class="image--center mx-auto" /></p>
<div data-node-type="callout">
<div data-node-type="callout-emoji">💡</div>
<div data-node-type="callout-text">If you have a large tenant with thousands of workspaces and items, this will be slow. In that case optimize the code to execute in parallel using <code>ThreadPoolExecutor</code></div>
</div>

What the title says, thanks to the latest version (v0.5) of [Semantic Link](https://learn.microsoft.com/en-us/fabric/data-science/semantic-link-overview?wt.mc_id=MVP_335074)

```python
!pip install --upgrade semantic-link --q #upgrade to semantic-link v0.5

import sempy.fabric as fabric
import pandas as pd 

df = pd.concat([fabric.list_items(workspace=ws) for ws in fabric.list_workspaces().query('`Is On Dedicated Capacity` == True').Id], ignore_index=True)
df
```

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1705502685880/8c249494-de95-49bf-ad88-e3b9a20a0e49.png align="center")

<div data-node-type="callout">
<div data-node-type="callout-emoji">💡</div>
<div data-node-type="callout-text">If you have a large tenant with thousands of workspaces and items, this will be slow. In that case optimize the code to execute in parallel using <code>ThreadPoolExecutor</code></div>
</div>

Efficiently fetch items from Fabric/Premium Workspaces using one-line code in v0.5 Semantic Link; optimize for large tenants

One Line Code To Get A List Of Items From All The Fabric/Premium Works

Using Semantic Link v0.5

One Line Code To Get A List Of Items From All The Fabric/Premium Workspaces

I am Sandeep Pawar, data analytics and data science professional.  I build data analytics & data science solutions using Microsoft Fabric and Power BI. I am a Microsoft Data Platform MVP.


Data analytics using Microsoft Fabric and Power BI. Enterprise scale end-to-end data analytics solutions using Microsoft Fabric. Tips and tricks on Microsoft Fabric

Sandeep Pawar | Microsoft Fabric

Sandeep Pawar | Microsoft Fabric

One Line Code To Get A List Of Items From All The Fabric/Premium Workspaces

Using Semantic Link v0.5