diff --git a/f/examples/parquet_etl.flow/do_analytics_and_build_plot.inline_script.py b/f/examples/parquet_etl.flow/do_analytics_and_build_plot.inline_script.py
deleted file mode 100644
index 8bd457c..0000000
--- a/f/examples/parquet_etl.flow/do_analytics_and_build_plot.inline_script.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import matplotlib.pyplot as plt
-import pandas as pd
-import fastparquet
-import base64
-
-def main(path: str):
-  fig, ax = plt.subplots(figsize=(15, 6), dpi=200)
-
-  # Colors
-  normal_color = '#cf1111'
-  anomaly_color = '#ffe4ad'
-
-  # Subset our data for plotting, and separate into normal and anomalous
-  to_plot = pd.read_parquet(
-      path,
-      columns=['new_cases', 'new_cases_ma7', 'anomaly']).loc['2020-03':]
-  normal = to_plot.query('not anomaly')
-  anomalous = to_plot.query('anomaly')
-
-  # Add new cases rolling average and annotation
-  to_plot['new_cases_ma7'].plot(color=normal_color, ax=ax)
-
-  label_date = '2020-07-26'
-  ax.annotate('7-day\naverage',
-              xy=(label_date, to_plot['new_cases_ma7'].loc[label_date]),
-              xytext=(0, 16), textcoords='offset points',
-              horizontalalignment='center',
-              arrowprops=dict(arrowstyle="-"),
-              color=normal_color)
-
-  # Add new cases bar plot and annotation
-  ax.bar(normal.index, normal['new_cases'], width=0.6, color=normal_color,
-        alpha=0.3)
-  ax.bar(anomalous.index, anomalous['new_cases'], width=0.6, color=anomaly_color)
-
-  label_date = normal.index[normal.new_cases.argmax()]
-  ax.annotate('New\ncases',
-              xy=(label_date, to_plot['new_cases'].loc[label_date]),
-              xytext=(-20, 0), textcoords='offset points',
-              horizontalalignment='right',
-              verticalalignment='center',
-              arrowprops=dict(arrowstyle="-"),
-              color=normal_color)
-
-  # Add and annotate horizontal grid lines
-  ax.hlines(100_000, to_plot.index[0], to_plot.index[-1], color='lightgray',
-            linestyle='--', linewidth=1)
-  ax.hlines(200_000, to_plot.index[0], to_plot.index[-1], color='lightgray',
-            linestyle='--', linewidth=1)
-
-  ax.annotate('200,000 cases',
-              xy=('2020-03-01', 200_000),
-              xytext=(0, 5), textcoords='offset points',
-              fontsize='large', color='gray')
-  ax.annotate('100,000',
-              xy=('2020-03-01', 100_000),
-              xytext=(0, 5), textcoords='offset points',
-              fontsize='large', color='gray')
-
-  # Styling
-  [ax.spines[spine].set_visible(False)
-  for spine in ['top', 'right', 'bottom', 'left']]
-  ax.yaxis.set_ticks([])
-  ax.minorticks_off()
-  ax.set_xlabel('');
-  plt.savefig('out.png')
-  with open('out.png', "rb") as image_file:
-    encoded_string = base64.b64encode(image_file.read()).decode('ascii')
-
-  return {"png": encoded_string}
diff --git a/f/examples/parquet_etl.flow/flow.yaml b/f/examples/parquet_etl.flow/flow.yaml
deleted file mode 100644
index 250052c..0000000
--- a/f/examples/parquet_etl.flow/flow.yaml
+++ /dev/null
@@ -1,63 +0,0 @@
-summary: >-
-  Small ETL using same_worker to share parquet across steps and do data
-  analytics
-description: >-
-  This flow uses the same_worker setting to share data in the '/shared' folder. 
-
-  The flow consists in getting covid-19 data from a csv, store it in parquet and
-  then build a plot out of it
-value:
-  modules:
-    - id: a
-      summary: Get the covid-19 data and store it as parquet
-      value:
-        type: rawscript
-        content: '!inline get_the_covid-19_data_and_store_it_as_parquet.inline_script.py'
-        input_transforms: {}
-        lock: |-
-          cramjam==2.8.0
-          fastparquet==2023.10.1
-          fsspec==2023.12.2
-          numpy==1.26.3
-          packaging==23.2
-          pandas==2.2.0
-          python-dateutil==2.8.2
-          pytz==2023.3.post1
-          six==1.16.0
-          tzdata==2023.4
-        language: python3
-    - id: b
-      summary: Do analytics and build plot
-      value:
-        type: rawscript
-        content: '!inline do_analytics_and_build_plot.inline_script.py'
-        input_transforms:
-          path:
-            type: javascript
-            expr: results.a
-        lock: |-
-          contourpy==1.2.0
-          cramjam==2.8.0
-          cycler==0.12.1
-          fastparquet==2023.10.1
-          fonttools==4.47.2
-          fsspec==2023.12.2
-          kiwisolver==1.4.5
-          matplotlib==3.8.2
-          numpy==1.26.3
-          packaging==23.2
-          pandas==2.2.0
-          pillow==10.2.0
-          pyparsing==3.1.1
-          python-dateutil==2.8.2
-          pytz==2023.3.post1
-          six==1.16.0
-          tzdata==2023.4
-        language: python3
-  same_worker: true
-schema:
-  $schema: 'https://json-schema.org/draft/2020-12/schema'
-  type: object
-  properties: {}
-  required: []
-ws_error_handler_muted: false
diff --git a/f/examples/parquet_etl.flow/get_the_covid-19_data_and_store_it_as_parquet.inline_script.py b/f/examples/parquet_etl.flow/get_the_covid-19_data_and_store_it_as_parquet.inline_script.py
deleted file mode 100644
index 5ff572a..0000000
--- a/f/examples/parquet_etl.flow/get_the_covid-19_data_and_store_it_as_parquet.inline_script.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import pandas as pd
-import pathlib
-import fastparquet
-
-def main():
-  nytimes = pd.read_csv(
-      'https://raw.githubusercontent.com/nytimes/covid-19-data/master/us.csv',
-      index_col=['date'], parse_dates=['date']).asfreq(freq='D')
-
-  # Transform: add new columns with useful transformations
-  nytimes['new_cases'] = nytimes['cases'].diff()
-  nytimes['new_cases_ma7'] = nytimes['new_cases'].rolling(7).mean()
-
-  anomalous_dates = ['2020-09-21', '2020-11-26', '2020-12-11', '2020-12-25']
-  nytimes['anomaly'] = False
-  nytimes.loc[anomalous_dates, 'anomaly'] = True
-
-  nytimes_path = pathlib.Path('./shared/nytimes')
-  nytimes_path.mkdir(parents=True, exist_ok=True)
-  path = nytimes_path / 'us.parquet'
-  nytimes.to_parquet(path)
-  return path
\ No newline at end of file