What's Your Most Active Fabric Workspace?

Semantic Link Labs v0.8.3 has list_activities method to get the list of all activities in your Fabric tenant. It uses the same Power BI Admin - Get Activity Events API but this API now also includes Fabric activities. Note that this is an Admin API so you need to be a Fabric administrator. Check the API details.

To answer the above question, I will use the admin.list_activity_events , loop over the last 30 days and plot the results by Fabric item type in my personal tenant:

from datetime import datetime, timedelta
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import sempy_labs as labs
import numpy as np

#number fo days to scan
N=30

activities = []
for n in range(N):
    day = datetime.now() - timedelta(days=n)
    start_of_day = day.replace(hour=0, minute=0, second=0, microsecond=0).strftime('%Y-%m-%dT%H:%M:%S')
    end_of_day = day.replace(hour=23, minute=59, second=59, microsecond=999999).strftime('%Y-%m-%dT%H:%M:%S')

    df = labs.admin.list_activity_events(
        start_time=start_of_day, 
        end_time=end_of_day
    ).groupby(['Workspace Name', 'Object Type'])['Request Id'].count().reset_index().assign(day=start_of_day)
    activities.append(df)

df_cleaned = pd.concat(activities).reset_index(drop=True)
df_cleaned = df_cleaned[df_cleaned.astype(str).apply(lambda x: x.str.strip().astype(bool))].dropna()
df_cleaned = df_cleaned.replace([np.inf, -np.inf], np.nan).dropna()
df_cleaned = df_cleaned.rename(columns={"Request Id": "Activity Count"})

plt.style.use('seaborn-v0_8')
sns.set_style("white")
sns.set_context("notebook", font_scale=1.2)

fig, ax = plt.subplots(figsize=(15, 8))
sns.lineplot(data=df_cleaned, x="Workspace Name", y="Activity Count", 
             hue="Object Type", marker='o', linewidth=2, markersize=8, errorbar=None)

plt.title(f"Workspace Activity Distribution: Last {N} Days", pad=20, fontsize=16, fontweight='bold')
plt.xlabel("Workspace Name", fontsize=12)
plt.ylabel("Activity Count", fontsize=12)
plt.xticks(rotation=45, ha='right')
plt.grid(True, linestyle=':', alpha=0.3, color='gray')
plt.legend(title="Object Type", title_fontsize=12, bbox_to_anchor=(1.02, 1),
          loc='upper left', frameon=True, borderaxespad=0, fontsize=10)

sns.despine()
plt.tight_layout()
plt.show()

Unsurprisingly, I use lakehouse and notebooks the most 😁

Note this API has a limitation of 200 requests per hour. To limit the scope, specify activity_filter and user_id_filter in the arguments. The activities are super granular, e.g. GenerateScreenshot, GetCloudSupportedDatasources etc. and some could be system generated so be sure to include only relevant activities in your analysis. I am the only user in my personal tenant and use it may be for ~30 min every day and still generated thousands of activities. If you have a large tenant, I would highly recommend saving all the logs to an Eventhouse on a daily basis for analysis. You can use the Kusto SDK or the Kusto Spark Connector in a notebook to ingest the data in the KQL table.