diff --git a/apps/wizard/app_pages/producer_analytics.py b/apps/wizard/app_pages/producer_analytics.py
new file mode 100644
index 00000000000..3be62be9165
--- /dev/null
+++ b/apps/wizard/app_pages/producer_analytics.py
@@ -0,0 +1,564 @@
+from datetime import datetime, timedelta
+from typing import Optional, cast
+
+import owid.catalog.processing as pr
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+from pandas_gbq import read_gbq
+from st_aggrid import AgGrid, GridUpdateMode, JsCode
+from st_aggrid.grid_options_builder import GridOptionsBuilder
+from structlog import get_logger
+
+from apps.wizard.utils.components import st_horizontal
+from etl.snapshot import Snapshot
+from etl.version_tracker import VersionTracker
+
+# Initialize log.
+log = get_logger()
+
+# Define constants.
+TODAY = datetime.today()
+# Date when the new views metric started to be recorded.
+MIN_DATE = datetime.strptime("2024-11-01", "%Y-%m-%d")
+GRAPHERS_BASE_URL = "https://ourworldindata.org/grapher/"
+# List of auxiliary steps to be (optionally) excluded from the DAG.
+# It may be convenient to ignore these steps because the analytics are heavily affected by a few producers (e.g. those that are involved in the population and income groups datasets).
+AUXILIARY_STEPS = [
+    "data://garden/demography/.*/population",
+    # Primary energy consumption is loaded by GCB.
+    "data://garden/energy/.*/primary_energy_consumption",
+    "data://garden/ggdc/.*/maddison_project_database",
+    "data://garden/wb/.*/income_groups",
+]
+
+# PAGE CONFIG
+st.set_page_config(
+    page_title="Wizard: Producer analytics",
+    layout="wide",
+    page_icon="🪄",
+)
+
+
+########################################################################################################################
+# FUNCTIONS & GLOBAL VARS
+########################################################################################################################
+def columns_producer(min_date, max_date):
+    # Define columns to be shown.
+    cols_prod = {
+        "producer": {
+            "headerName": "Producer",
+            "headerTooltip": "Name of the producer. This is NOT the name of the dataset.",
+        },
+        "n_charts": {
+            "headerName": "Charts",
+            "headerTooltip": "Number of charts using data from a producer.",
+        },
+        "renders_custom": {
+            "headerName": "Views in custom range",
+            "headerTooltip": f"Number of renders between {min_date} and {max_date}.",
+        },
+        "renders_365d": {
+            "headerName": "Views 365 days",
+            "headerTooltip": "Number of renders in the last 365 days.",
+        },
+        "renders_30d": {
+            "headerName": "Views 30 days",
+            "headerTooltip": "Number of renders in the last 30 days.",
+        },
+    }
+    return cols_prod
+
+
+@st.cache_data(show_spinner=False)
+def get_grapher_views(
+    date_start: str = MIN_DATE.strftime("%Y-%m-%d"),
+    date_end: str = TODAY.strftime("%Y-%m-%d"),
+    groupby: Optional[list[str]] = None,
+    grapher_urls: Optional[list[str]] = None,
+) -> pd.DataFrame:
+    grapher_filter = ""
+    if grapher_urls:
+        # If a list of grapher URLs is given, consider only those.
+        grapher_urls_formatted = ", ".join(f"'{url}'" for url in grapher_urls)
+        grapher_filter = f"AND grapher IN ({grapher_urls_formatted})"
+    else:
+        # If no list is given, consider all grapher URLs.
+        grapher_filter = f"AND grapher LIKE '{GRAPHERS_BASE_URL}%'"
+
+    if not groupby:
+        # If a groupby list is not given, assume the simplest case, which gives total views for each grapher.
+        groupby = ["grapher"]
+
+    # Prepare the query.
+    groupby_clause = ", ".join(groupby)
+    select_clause = f"{groupby_clause}, SUM(events) AS renders"
+    query = f"""
+        SELECT
+            {select_clause}
+        FROM prod_google_analytics4.grapher_views_by_day_page_grapher_device_country_iframe
+        WHERE
+            day >= '{date_start}'
+            AND day <= '{date_end}'
+            {grapher_filter}
+        GROUP BY {groupby_clause}
+        ORDER BY {groupby_clause}
+    """
+
+    # Execute the query.
+    df_views = read_gbq(query, project_id="owid-analytics")
+
+    return cast(pd.DataFrame, df_views)
+
+
+@st.cache_data(show_spinner=False)
+def get_chart_renders(min_date: str, max_date: str) -> pd.DataFrame:
+    # List ranges of dates to fetch views.
+    date_ranges = {
+        "renders_365d": ((TODAY - timedelta(days=365)).strftime("%Y-%m-%d"), TODAY.strftime("%Y-%m-%d")),
+        "renders_30d": ((TODAY - timedelta(days=30)).strftime("%Y-%m-%d"), TODAY.strftime("%Y-%m-%d")),
+        "renders_custom": (min_date, max_date),  # Use user-defined date range.
+    }
+
+    # Get analytics for those ranges, for all grapher URLs.
+    list_renders = [
+        get_grapher_views(date_start=date_start, date_end=date_end, grapher_urls=None, groupby=["grapher"]).rename(
+            columns={"renders": column_name}
+        )
+        for column_name, (date_start, date_end) in date_ranges.items()
+    ]
+
+    # Merge all dataframes.
+    df_renders = pr.multi_merge(list_renders, on="grapher", how="outer")  # type: ignore
+
+    return df_renders
+
+
+@st.cache_data(show_spinner=False)
+def load_steps_df(excluded_steps) -> pd.DataFrame:
+    # Load steps dataframe.
+    steps_df = VersionTracker(exclude_steps=excluded_steps).steps_df
+
+    return steps_df
+
+
+@st.cache_data(show_spinner=False)
+def load_steps_df_with_producer_data(excluded_steps) -> pd.DataFrame:
+    # Load steps dataframe.
+    # st.toast("⌛ Loading data from VersionTracker...")
+    steps_df = load_steps_df(excluded_steps=excluded_steps)
+
+    # st.toast("⌛ Processing VersionTracker data...")
+    # Select only active snapshots.
+    df = steps_df[(steps_df["channel"] == "snapshot") & (steps_df["state"] == "active")].reset_index(drop=True)
+
+    # Select only relevant columns.
+    df = df[["step", "all_chart_slugs"]]
+
+    # Add a column of producer to steps df (where possible).
+    for i, row in df.iterrows():
+        snap_uri = row["step"].split("snapshot://" if "snapshot://" in row["step"] else "snapshot-private://")[1]
+        snap = Snapshot(snap_uri)
+        origin = snap.metadata.origin
+        if (origin is not None) and (snap.metadata.namespace not in ["dummy"]):
+            producer = snap.metadata.origin.producer  # type: ignore
+            df.loc[i, "producer"] = producer
+
+    # Select only relevant columns.
+    df = df[["producer", "all_chart_slugs"]]
+
+    # Remove rows with no producer.
+    df = df.dropna(subset=["producer"]).reset_index(drop=True)
+
+    # Ignore the chart id, and keep only the slug.
+    df["all_chart_slugs"] = [sorted(set([slug for _, slug in id_slug])) for id_slug in df["all_chart_slugs"]]
+
+    # Create a row for each producer-slug pair. Fill with "" (in cases where the producer has no charts).
+    df_expanded = df.explode("all_chart_slugs")
+
+    # Remove duplicates.
+    # NOTE: This happens because df contains one row per snapshot. Some grapher datasets come from a combination of multiple snapshots (often from the same producer). We want to count producer-chart pairs only once.
+    df_expanded = df_expanded.drop_duplicates(subset=["producer", "all_chart_slugs"]).reset_index(drop=True)
+
+    # Add a column for grapher URL.
+    df_expanded["grapher"] = GRAPHERS_BASE_URL + df_expanded["all_chart_slugs"]
+
+    return df_expanded
+
+
+@st.cache_data(show_spinner=False)
+def get_producer_charts_analytics(min_date, max_date, excluded_steps):
+    # Get chart renders using user-defined date range for "renders_custom".
+    # st.toast("⌛ Getting analytics on chart renders...")
+    df_renders = get_chart_renders(min_date=min_date, max_date=max_date)
+
+    # Load the steps dataframe with producer data.
+    df_expanded = load_steps_df_with_producer_data(excluded_steps=excluded_steps)
+
+    # Add columns with the numbers of chart renders.
+    df_expanded = df_expanded.merge(df_renders, on="grapher", how="left").drop(columns=["all_chart_slugs"])
+
+    return df_expanded
+
+
+@st.cache_data(show_spinner=False)
+def get_producer_analytics_per_chart(min_date, max_date, excluded_steps):
+    # Load the steps dataframe with producer data and analytics.
+    df_expanded = get_producer_charts_analytics(min_date=min_date, max_date=max_date, excluded_steps=excluded_steps)
+
+    # Create an expanded table with number of views per chart.
+    df_renders_per_chart = df_expanded.dropna(subset=["grapher"]).fillna(0).reset_index(drop=True)
+    df_renders_per_chart = df_renders_per_chart.sort_values("renders_custom", ascending=False).reset_index(drop=True)
+
+    return df_renders_per_chart
+
+
+@st.cache_data(show_spinner=False)
+def get_producer_analytics_per_producer(min_date, max_date, excluded_steps):
+    # Load the steps dataframe with producer data and analytics.
+    df_expanded = get_producer_charts_analytics(min_date=min_date, max_date=max_date, excluded_steps=excluded_steps)
+
+    # st.toast("⌛ Adapting the data for presentation...")
+    # Group by producer and get the full list of chart slugs for each producer.
+    df_grouped = df_expanded.groupby("producer", observed=True, as_index=False).agg(
+        {
+            "grapher": lambda x: [item for item in x if pd.notna(item)],  # Filter out NaN values
+            "renders_365d": "sum",
+            "renders_30d": "sum",
+            "renders_custom": "sum",
+        }
+    )
+    df_grouped["n_charts"] = df_grouped["grapher"].apply(len)
+
+    # Check if lists are unique. If not, make them unique in the previous line.
+    error = "Duplicated chart slugs found for a given producer."
+    assert df_grouped["grapher"].apply(lambda x: len(x) == len(set(x))).all(), error
+
+    # Drop unnecessary columns.
+    df_grouped = df_grouped.drop(columns=["grapher"])
+
+    # Sort conveniently.
+    df_grouped = df_grouped.sort_values(["renders_custom"], ascending=False).reset_index(drop=True)
+
+    return df_grouped
+
+
+def show_producers_grid(df_producers, min_date, max_date):
+    """Show table with producers analytics."""
+    gb = GridOptionsBuilder.from_dataframe(df_producers)
+    gb.configure_grid_options(domLayout="autoHeight", enableCellTextSelection=True)
+    gb.configure_selection(
+        selection_mode="multiple",
+        use_checkbox=True,
+        rowMultiSelectWithClick=True,
+        suppressRowDeselection=False,
+        groupSelectsChildren=True,
+        groupSelectsFiltered=True,
+    )
+    gb.configure_default_column(editable=False, groupable=True, sortable=True, filterable=True, resizable=True)
+
+    # Enable column auto-sizing for the grid.
+    gb.configure_grid_options(suppressSizeToFit=False)  # Allows dynamic resizing to fit.
+    gb.configure_default_column(autoSizeColumns=True)  # Ensures all columns can auto-size.
+
+    # Configure individual columns with specific settings.
+    COLUMNS_PRODUCERS = columns_producer(min_date, max_date)
+    for column in COLUMNS_PRODUCERS:
+        gb.configure_column(column, **COLUMNS_PRODUCERS[column])
+    # Configure pagination with dynamic page size.
+    gb.configure_pagination(paginationAutoPageSize=False, paginationPageSize=20)
+    # Build the grid options.
+    grid_options = gb.build()
+    # Custom CSS to ensure the table stretches across the page.
+    custom_css = {
+        ".ag-theme-streamlit": {
+            "max-width": "100% !important",
+            "width": "100% !important",
+            "margin": "0 auto !important",  # Centers the grid horizontally.
+        },
+    }
+    # Display the grid table with the updated grid options.
+    grid_response = AgGrid(
+        data=df_producers,
+        gridOptions=grid_options,
+        height=1000,
+        width="100%",
+        update_mode=GridUpdateMode.MODEL_CHANGED,
+        fit_columns_on_grid_load=True,  # Automatically adjust columns when the grid loads.
+        allow_unsafe_jscode=True,
+        theme="streamlit",
+        custom_css=custom_css,
+        # excel_export_mode=ExcelExportMode.MANUAL,  # Doesn't work?
+    )
+
+    # Get the selected producers from the first table.
+    producers_selected = [row["producer"] for row in grid_response["selected_rows"]]
+
+    return producers_selected
+
+
+def plot_chart_analytics(df):
+    """Show chart with analytics on producer's charts."""
+    # Get total daily views of selected producers.
+    grapher_urls_selected = df["grapher"].unique().tolist()  # type: ignore
+    df_total_daily_views = get_grapher_views(
+        date_start=min_date, date_end=max_date, groupby=["day"], grapher_urls=grapher_urls_selected
+    )
+
+    # Get daily views of the top 10 charts.
+    grapher_urls_top_10 = (
+        df.sort_values("renders_custom", ascending=False)["grapher"].unique().tolist()[0:10]  # type: ignore
+    )
+    df_top_10_daily_views = get_grapher_views(
+        date_start=min_date, date_end=max_date, groupby=["day", "grapher"], grapher_urls=grapher_urls_top_10
+    )
+
+    # Get total number of views and average daily views.
+    total_views = df_total_daily_views["renders"].sum()
+    average_daily_views = df_total_daily_views["renders"].mean()
+    # Get total views of the top 10 charts in the selected date range.
+    df_top_10_total_views = df_top_10_daily_views.groupby("grapher", as_index=False).agg({"renders": "sum"})
+
+    # Create a line chart.
+    df_plot = pd.concat([df_total_daily_views.assign(**{"grapher": "Total"}), df_top_10_daily_views]).rename(
+        columns={"grapher": "Chart slug"}
+    )
+    df_plot["Chart slug"] = df_plot["Chart slug"].apply(lambda x: x.split("/")[-1])
+    df_plot["day"] = pd.to_datetime(df_plot["day"]).dt.strftime("%a. %Y-%m-%d")
+    fig = px.line(
+        df_plot,
+        x="day",
+        y="renders",
+        color="Chart slug",
+        title="Total daily views and views of top 10 charts",
+    ).update_layout(xaxis_title=None, yaxis_title=None)
+
+    # Display the chart.
+    st.plotly_chart(fig, use_container_width=True)
+
+    return total_views, average_daily_views, df_top_10_total_views
+
+
+def show_producer_charts_grid(df):
+    """Show table with analytics on producer's charts."""
+    # Configure and display the second table.
+    gb2 = GridOptionsBuilder.from_dataframe(df)
+    gb2.configure_grid_options(domLayout="autoHeight", enableCellTextSelection=True)
+    gb2.configure_default_column(editable=False, groupable=True, sortable=True, filterable=True, resizable=True)
+
+    # Create a JavaScript renderer for clickable slugs.
+    grapher_slug_jscode = JsCode(
+        r"""
+        class UrlCellRenderer {
+        init(params) {
+            this.eGui = document.createElement('a');
+            if (params.value) {
+                // Extract the slug from the full URL.
+                const url = new URL(params.value);
+                const slug = url.pathname.split('/').pop();  // Get the last part of the path as the slug.
+                this.eGui.innerText = slug;
+                this.eGui.setAttribute('href', params.value);
+            } else {
+                this.eGui.innerText = '';
+            }
+            this.eGui.setAttribute('style', "text-decoration:none; color:blue");
+            this.eGui.setAttribute('target', "_blank");
+        }
+        getGui() {
+            return this.eGui;
+        }
+        }
+        """
+    )
+
+    # Define columns to be shown, including the cell renderer for "grapher".
+    COLUMNS_PRODUCERS = columns_producer(min_date, max_date)
+    COLUMNS_PRODUCER_CHARTS = {
+        column: (
+            {
+                "headerName": "Chart URL",
+                "headerTooltip": "URL of the chart in the grapher.",
+                "cellRenderer": grapher_slug_jscode,
+            }
+            if column == "grapher"
+            else COLUMNS_PRODUCERS[column]
+        )
+        for column in ["producer", "renders_custom", "renders_365d", "renders_30d", "grapher"]
+    }
+    # Configure and display the second table.
+    gb2 = GridOptionsBuilder.from_dataframe(df)
+    gb2.configure_grid_options(domLayout="autoHeight", enableCellTextSelection=True)
+    gb2.configure_default_column(editable=False, groupable=True, sortable=True, filterable=True, resizable=True)
+
+    # Apply column configurations directly from the dictionary.
+    for column, config in COLUMNS_PRODUCER_CHARTS.items():
+        gb2.configure_column(column, **config)
+
+    # Configure pagination with dynamic page size.
+    gb2.configure_pagination(paginationAutoPageSize=False, paginationPageSize=20)
+    grid_options2 = gb2.build()
+
+    # Display the grid.
+    AgGrid(
+        data=df,
+        gridOptions=grid_options2,
+        height=500,
+        width="100%",
+        fit_columns_on_grid_load=True,
+        allow_unsafe_jscode=True,
+        theme="streamlit",
+        # excel_export_mode=ExcelExportMode.MANUAL,  # Doesn't work?
+    )
+
+
+def prepare_summary(
+    df_top_10_total_views, producers_selected, total_views, average_daily_views, min_date, max_date
+) -> str:
+    """Prepare summary at the end of the app."""
+    # Prepare the total number of views.
+    total_views_str = f"{total_views:9,}"
+    # Prepare the average daily views.
+    average_views_str = f"{round(average_daily_views):9,}"
+    # Prepare a summary of the top 10 charts to be copy-pasted.
+    if len(producers_selected) == 0:
+        producers_selected_str = "all producers"
+    elif len(producers_selected) == 1:
+        producers_selected_str = producers_selected[0]
+    else:
+        producers_selected_str = ", ".join(producers_selected[:-1]) + " and " + producers_selected[-1]
+    # NOTE: I tried .to_string() and .to_markdown() and couldn't find a way to keep a meaningful format.
+    df_summary_str = ""
+    for _, row in df_top_10_total_views.sort_values("renders", ascending=False).iterrows():
+        df_summary_str += f"{row['renders']:9,}" + " - " + row["grapher"] + "\n"
+
+    # Define the content to copy.
+    summary = f"""\
+Analytics of charts using data by {producers_selected_str} between {min_date} and {max_date}:
+- Total number of chart views: {total_views_str}
+- Average daily chart views: {average_views_str}
+- Views of top performing charts:
+{df_summary_str}
+
+    """
+    return summary
+
+
+########################################################################################################################
+# RENDER
+########################################################################################################################
+
+# Streamlit app layout.
+st.title(":material/bar_chart: Producer analytics")
+st.markdown("Explore analytics of data producers.")
+
+# SEARCH BOX
+with st.container(border=True):
+    st.markdown(
+        f"Select a custom date range (note that this metric started to be recorded on {MIN_DATE.strftime('%Y-%m-%d')})."
+    )
+
+    with st_horizontal(vertical_alignment="center"):
+        # Create input fields for minimum and maximum dates.
+        min_date = st.date_input(
+            "Select minimum date",
+            value=MIN_DATE,
+            key="min_date",
+            format="YYYY-MM-DD",
+        ).strftime(  # type: ignore
+            "%Y-%m-%d"
+        )
+        max_date = st.date_input(
+            "Select maximum date",
+            value=TODAY,
+            key="max_date",
+            format="YYYY-MM-DD",
+        ).strftime(  # type: ignore
+            "%Y-%m-%d"
+        )
+        exclude_auxiliary_steps = st.checkbox(
+            "Exclude auxiliary steps (e.g. population)",
+            False,
+            help="Exclude steps that are commonly used as auxiliary data, so they do not skew the analytics in favor of a few producers. But note that this will exclude all uses of these steps, even when they are the main datasets (not auxiliary). Auxiliary steps are:\n- "
+            + "\n- ".join(sorted(f"`{s}`" for s in AUXILIARY_STEPS)),
+        )
+
+if exclude_auxiliary_steps:
+    # If the user wants to exclude auxiliary steps, take the default list of excluded steps.
+    excluded_steps = AUXILIARY_STEPS
+else:
+    # Otherwise, do not exclude any steps.
+    excluded_steps = []
+
+########################################################################################################################
+# 1/ PRODUCER ANALYTICS: Display main table, with analytics per producer.
+# Allow the user to select a subset of producers.
+########################################################################################################################
+st.header("Analytics by producer")
+st.markdown(
+    "Total number of charts and chart views for each producer. Producers selected in this table will be used to filter the producer-charts table below."
+)
+
+# Load table content and select only columns to be shown.
+with st.spinner("Loading producer data. We are accessing various databases. This can take few seconds..."):
+    df_producers = get_producer_analytics_per_producer(
+        min_date=min_date, max_date=max_date, excluded_steps=excluded_steps
+    )
+
+# Prepare and display the grid table with producer analytics.
+producers_selected = show_producers_grid(
+    df_producers=df_producers,
+    min_date=min_date,
+    max_date=max_date,
+)
+
+########################################################################################################################
+# 2/ CHART ANALYTICS: Display a chart with the total number of daily views, and the daily views of the top performing charts.
+########################################################################################################################
+st.header("Analytics by chart")
+st.markdown("Number of views for each chart that uses data by the selected producers.")
+
+# Load detailed analytics per producer-chart.
+with st.spinner("Loading chart data. This can take few seconds..."):
+    df_producer_charts = get_producer_analytics_per_chart(
+        min_date=min_date, max_date=max_date, excluded_steps=excluded_steps
+    )
+
+# Get the selected producers from the first table.
+if len(producers_selected) == 0:
+    # If no producers are selected, show all producer-charts.
+    df_producer_charts_filtered = df_producer_charts
+else:
+    # Filter producer-charts by selected producers.
+    df_producer_charts_filtered = df_producer_charts[df_producer_charts["producer"].isin(producers_selected)]
+
+# Show chart with chart analytics, and get some summary data.
+total_views, average_daily_views, df_top_10_total_views = plot_chart_analytics(df_producer_charts_filtered)
+
+# Show table
+show_producer_charts_grid(df_producer_charts_filtered)
+
+########################################################################################################################
+# 3/ SUMMARY: Display a summary to be shared with the data producer.
+########################################################################################################################
+
+# Prepare the summary to be copy-pasted.
+summary = prepare_summary(
+    df_top_10_total_views=df_top_10_total_views,
+    producers_selected=producers_selected,
+    total_views=total_views,
+    average_daily_views=average_daily_views,
+    min_date=min_date,
+    max_date=max_date,
+)
+
+# Display the content.
+st.markdown(
+    """## Summary for data producers
+
+For now, to share analytics with a data producer you can so any of the following:
+- **Table export**: Right-click on a cell in the above's table and export as a CSV or Excel file.
+- **Chart export**: Click on the camera icon on the top right of the chart to download the chart as a PNG.
+- **Copy summary**: Click on the upper right corner of the box below to copy the summary to the clipboard.
+"""
+)
+st.code(summary, language="text")
diff --git a/apps/wizard/config/config.yml b/apps/wizard/config/config.yml
index f00f6090fc0..ca80284a32b 100644
--- a/apps/wizard/config/config.yml
+++ b/apps/wizard/config/config.yml
@@ -151,6 +151,13 @@ sections:
         entrypoint: app_pages/dataset_explorer.py
         icon: ":material/search:"
         image_url: "https://upload.wikimedia.org/wikipedia/commons/c/c3/NGC_4414_%28NASA-med%29.jpg"
+      - title: "Producer analytics"
+        alias: producer-analytics
+        description: "Extract analytics to share with data producers"
+        maintainer: "@pablo"
+        entrypoint: app_pages/producer_analytics.py
+        icon: ":material/developer_board:"
+        image_url: "https://upload.wikimedia.org/wikipedia/commons/thumb/c/ce/Wikimedia_Product_Analytics_team_logo.svg/512px-Wikimedia_Product_Analytics_team_logo.svg.png"
 
   - title: "Explorers"
     description: |-
diff --git a/apps/wizard/utils/components.py b/apps/wizard/utils/components.py
index c2934548512..c82b4d84cc4 100644
--- a/apps/wizard/utils/components.py
+++ b/apps/wizard/utils/components.py
@@ -13,42 +13,43 @@
 
 HORIZONTAL_STYLE = """<style class="hide-element">
     /* Hides the style container and removes the extra spacing */
-    .element-container:has(.hide-element) {
+    .element-container:has(.hide-element) {{
         display: none;
-    }
+    }}
     /*
         The selector for >.element-container is necessary to avoid selecting the whole
         body of the streamlit app, which is also a stVerticalBlock.
     */
-    div[data-testid="stVerticalBlock"]:has(> .element-container .horizontal-marker) {
+    div[data-testid="stVerticalBlock"]:has(> .element-container .horizontal-marker) {{
         display: flex;
         flex-direction: row !important;
         flex-wrap: wrap;
         gap: 1rem;
-        align-items: baseline;
-    }
+        align-items: {vertical_alignment};
+    }}
     /* Override the default width of selectboxes in horizontal layout */
-    div[data-testid="stVerticalBlock"]:has(> .element-container .horizontal-marker) select {
+    div[data-testid="stVerticalBlock"]:has(> .element-container .horizontal-marker) select {{
         min-width: 200px;  /* Set a minimum width for selectboxes */
         max-width: 400px;  /* Optional: Set a max-width to avoid overly wide selectboxes */
-    }
+    }}
     /* Buttons and their parent container all have a width of 704px, which we need to override */
-    div[data-testid="stVerticalBlock"]:has(> .element-container .horizontal-marker) div {
+    div[data-testid="stVerticalBlock"]:has(> .element-container .horizontal-marker) div {{
         width: max-content !important;
-    }
+    }}
     /* Just an example of how you would style buttons, if desired */
     /*
-    div[data-testid="stVerticalBlock"]:has(> .element-container .horizontal-marker) button {
+    div[data-testid="stVerticalBlock"]:has(> .element-container .horizontal-marker) button {{
         border-color: red;
-    }
+    }}
     */
 </style>
 """
 
 
 @contextmanager
-def st_horizontal():
-    st.markdown(HORIZONTAL_STYLE, unsafe_allow_html=True)
+def st_horizontal(vertical_alignment="baseline"):
+    h_style = HORIZONTAL_STYLE.format(vertical_alignment=vertical_alignment)
+    st.markdown(h_style, unsafe_allow_html=True)
     with st.container():
         st.markdown('<span class="hide-element horizontal-marker"></span>', unsafe_allow_html=True)
         yield
diff --git a/etl/version_tracker.py b/etl/version_tracker.py
index f75f9f8bbc0..20a78df7c75 100644
--- a/etl/version_tracker.py
+++ b/etl/version_tracker.py
@@ -1,3 +1,4 @@
+import re
 from datetime import datetime
 from enum import Enum
 from pathlib import Path
@@ -259,6 +260,31 @@ def _recursive_get_all_step_dependencies_ndim(
     return dependencies, memo
 
 
+def remove_steps_from_dag(dag: dict, exclude: list[str]) -> dict:
+    """
+    Remove specific steps (either active steps or dependencies) from the DAG.
+
+    This can be useful to ignore auxiliary datasets. The excluded steps can have wildcards:
+    exclude = [
+            "data://garden/demography/.*/population",
+            "data://garden/wb/.*/income_groups",
+        ]
+    """
+
+    # Check if a step matches any exclude pattern.
+    def is_excluded(step: str) -> bool:
+        return any(pattern.match(step) for pattern in [re.compile(pattern) for pattern in exclude])
+
+    # Filter out steps and dependencies that match any element from the excluded list.
+    dag_filtered = {
+        step: {dep for dep in dependencies if not is_excluded(dep)}
+        for step, dependencies in dag.items()
+        if not is_excluded(step)
+    }
+
+    return dag_filtered
+
+
 class VersionTracker:
     """Helper object that loads the dag, provides useful functions to check for versions and dataset dependencies, and
     checks for inconsistencies.
@@ -299,6 +325,7 @@ def __init__(
         warn_on_archivable: bool = True,
         warn_on_unused: bool = True,
         ignore_archive: bool = False,
+        exclude_steps: Optional[list[str]] = None,
     ):
         # Load dag of active steps (a dictionary step: set of dependencies).
         self.dag_active = load_dag(paths.DAG_FILE)
@@ -308,6 +335,13 @@ def __init__(
         else:
             # Load dag of active and archive steps.
             self.dag_all = load_dag(paths.DAG_ARCHIVE_FILE)
+
+        # Optionally exclude certain steps and dependencies.
+        self.exclude_steps = exclude_steps
+        if self.exclude_steps:
+            self.dag_active = remove_steps_from_dag(self.dag_active, self.exclude_steps)
+            self.dag_all = remove_steps_from_dag(self.dag_all, self.exclude_steps)
+
         # Create a reverse dag (a dictionary where each item is step: set of usages).
         self.dag_all_reverse = reverse_graph(graph=self.dag_all)
         # Create a reverse dag (a dictionary where each item is step: set of usages) of active steps.
@@ -621,14 +655,14 @@ def _days_since_step_creation(version):
         ] = UpdateState.ARCHIVABLE.value
 
         # There are special steps that, even though they are archivable or unused, we want to keep in the active dag.
-        steps_active_df.loc[steps_active_df["step"].isin(self.ARCHIVABLE_STEPS_TO_KEEP), "update_state"] = (
-            UpdateState.UP_TO_DATE.value
-        )
+        steps_active_df.loc[
+            steps_active_df["step"].isin(self.ARCHIVABLE_STEPS_TO_KEEP), "update_state"
+        ] = UpdateState.UP_TO_DATE.value
 
         # All explorers and external steps should be considered up to date.
-        steps_active_df.loc[steps_active_df["channel"].isin(["explorers", "external"]), "update_state"] = (
-            UpdateState.UP_TO_DATE.value
-        )
+        steps_active_df.loc[
+            steps_active_df["channel"].isin(["explorers", "external"]), "update_state"
+        ] = UpdateState.UP_TO_DATE.value
 
         # Add update state to archived steps.
         steps_inactive_df["update_state"] = UpdateState.ARCHIVED.value
diff --git a/pyproject.toml b/pyproject.toml
index 3e524a1b5ca..ca6c8ecde91 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -66,7 +66,6 @@ dependencies = [
     "scikit-learn>=1.5.2",
     "geopy>=2.4.1",
     "py7zr>=0.22.0",
-    "pyreadr>=0.5.2",
 ]
 
 [tool.uv.sources]
@@ -107,7 +106,7 @@ dev-dependencies = [
     # unpinning those would introduce tons of type errors
     "pyright==1.1.373",
     "pandas-stubs==1.2.0.62",
-    "ruff>=0.8.2",
+    "ruff==0.1.6",
     "ipdb>=0.13.13",
 ]
 
@@ -135,6 +134,7 @@ wizard = [
     "torch<2.3.0",
     "sentence-transformers>=2.2.2",
     "moviepy>=2.1.1",
+    "pandas-gbq>=0.25.0",
 ]
 
 [project.scripts]
@@ -147,17 +147,16 @@ compare = 'etl.compare:cli'
 backport = 'apps.backport.backport:backport_cli'
 
 [tool.ruff]
-lint.extend-select = [
+extend-select = [
     # isort
     "I"
 ]
-lint.ignore = ["E501"]
+ignore = ["E501"]
 line-length = 120
 target-version = "py310"
 extend-exclude = [
     ".ipynb_checkpoints",
     "*cookiecutter",
-    "*.ipynb",
 ]
 
 [build-system]
diff --git a/uv.lock b/uv.lock
index ced25ca0fd1..2f4ecd7dbac 100644
--- a/uv.lock
+++ b/uv.lock
@@ -796,6 +796,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c3/be/d0d44e092656fe7a06b55e6103cbce807cdbdee17884a5367c68c9860853/dataclasses_json-0.6.7-py3-none-any.whl", hash = "sha256:0dbf33f26c8d5305befd61b39d2b3414e8a407bedc2834dea9b8d642666fb40a", size = 28686 },
 ]
 
+[[package]]
+name = "db-dtypes"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pandas" },
+    { name = "pyarrow" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e9/80/00d501391fc5450c37b5fe0c25596e629a9fa2868fe4f194d69c5622210e/db_dtypes-1.3.1.tar.gz", hash = "sha256:a058f05dab100891f3e76a7a3db9ad0f107f18dd3d1bdd13680749a2f07eae77", size = 32492 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a2/e0/2eeacbe14b5a3a3b4ad5fd2c1e7959bd2201e4daeba851579fa78e5eccb1/db_dtypes-1.3.1-py2.py3-none-any.whl", hash = "sha256:fbc9d1740d94aaf2b5ae24601cfc875a69b4635bb9d049e3c3036e9f10203af8", size = 18742 },
+]
+
 [[package]]
 name = "debugpy"
 version = "1.8.5"
@@ -938,7 +953,6 @@ dependencies = [
     { name = "pyhumps" },
     { name = "pymysql" },
     { name = "pypdf2" },
-    { name = "pyreadr" },
     { name = "python-docx" },
     { name = "python-dotenv" },
     { name = "pyyaml" },
@@ -979,6 +993,7 @@ api = [
 wizard = [
     { name = "geographiclib" },
     { name = "moviepy" },
+    { name = "pandas-gbq" },
     { name = "plotly" },
     { name = "pyproj" },
     { name = "sentence-transformers" },
@@ -1053,6 +1068,7 @@ requires-dist = [
     { name = "owid-datautils", editable = "lib/datautils" },
     { name = "owid-repack", editable = "lib/repack" },
     { name = "pandas", specifier = "==2.2.3" },
+    { name = "pandas-gbq", marker = "extra == 'wizard'", specifier = ">=0.25.0" },
     { name = "papermill", specifier = ">=2.3.3" },
     { name = "pdfplumber", specifier = ">=0.9.0" },
     { name = "plotly", marker = "extra == 'wizard'", specifier = ">=5.23.0" },
@@ -1063,7 +1079,6 @@ requires-dist = [
     { name = "pymysql", specifier = ">=1.1.1" },
     { name = "pypdf2", specifier = ">=2.11.1" },
     { name = "pyproj", marker = "extra == 'wizard'", specifier = ">=3.6.1" },
-    { name = "pyreadr", specifier = ">=0.5.2" },
     { name = "python-docx", specifier = ">=1.1.2" },
     { name = "python-dotenv", specifier = ">=0.19.0" },
     { name = "pyyaml", specifier = ">=6.0.1" },
@@ -1132,7 +1147,7 @@ dev = [
     { name = "pyright", specifier = "==1.1.373" },
     { name = "pytest", specifier = ">=8.3.2" },
     { name = "pyyaml", specifier = ">=6.0.2" },
-    { name = "ruff", specifier = ">=0.8.2" },
+    { name = "ruff", specifier = "==0.1.6" },
     { name = "sqlacodegen", git = "https://github.com/agronholm/sqlacodegen.git" },
     { name = "types-pyyaml", specifier = ">=6.0.12.20240808" },
     { name = "watchdog", specifier = ">=4.0.1" },
@@ -1496,6 +1511,12 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/79/53/2e340a6ed897fa2bdd6c1bf166b98c047fbb648463dfd2b209ca7d501984/google_api_core-2.19.2-py3-none-any.whl", hash = "sha256:53ec0258f2837dd53bbd3d3df50f5359281b3cc13f800c941dd15a9b5a415af4", size = 139427 },
 ]
 
+[package.optional-dependencies]
+grpc = [
+    { name = "grpcio" },
+    { name = "grpcio-status" },
+]
+
 [[package]]
 name = "google-api-python-client"
 version = "2.145.0"
@@ -1552,6 +1573,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/1a/8e/22a28dfbd218033e4eeaf3a0533b2b54852b6530da0c0fe934f0cc494b29/google_auth_oauthlib-1.2.1-py2.py3-none-any.whl", hash = "sha256:2d58a27262d55aa1b87678c3ba7142a080098cbc2024f903c62355deb235d91f", size = 24930 },
 ]
 
+[[package]]
+name = "google-cloud-bigquery"
+version = "3.27.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "google-api-core", extra = ["grpc"] },
+    { name = "google-auth" },
+    { name = "google-cloud-core" },
+    { name = "google-resumable-media" },
+    { name = "packaging" },
+    { name = "python-dateutil" },
+    { name = "requests" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c0/05/633ce6686b1fed2cd364fa4698bfa6d586263cd4795d012584f8097061e1/google_cloud_bigquery-3.27.0.tar.gz", hash = "sha256:379c524054d7b090fa56d0c22662cc6e6458a6229b6754c0e7177e3a73421d2c", size = 456964 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f5/40/4b11a4a8839de8ce802a3ccd60b34e70ce10d13d434a560534ba98f0ea3f/google_cloud_bigquery-3.27.0-py2.py3-none-any.whl", hash = "sha256:b53b0431e5ba362976a4cd8acce72194b4116cdf8115030c7b339b884603fcc3", size = 240100 },
+]
+
 [[package]]
 name = "google-cloud-core"
 version = "2.4.1"
@@ -1667,6 +1706,55 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0d/20/89674b7d62a19138b3352f6080f2ff3e1ee4a298b29bb793746423d0b908/greenlet-3.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:28fe80a3eb673b2d5cc3b12eea468a5e5f4603c26aa34d88bf61bba82ceb2f9b", size = 294647 },
 ]
 
+[[package]]
+name = "grpcio"
+version = "1.68.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/91/ec/b76ff6d86bdfd1737a5ec889394b54c18b1ec3832d91041e25023fbcb67d/grpcio-1.68.1.tar.gz", hash = "sha256:44a8502dd5de653ae6a73e2de50a401d84184f0331d0ac3daeb044e66d5c5054", size = 12694654 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f5/88/d1ac9676a0809e3efec154d45246474ec12a4941686da71ffb3d34190294/grpcio-1.68.1-cp310-cp310-linux_armv7l.whl", hash = "sha256:d35740e3f45f60f3c37b1e6f2f4702c23867b9ce21c6410254c9c682237da68d", size = 5171054 },
+    { url = "https://files.pythonhosted.org/packages/ec/cb/94ca41e100201fee8876a4b44d64e43ac7405929909afe1fa943d65b25ef/grpcio-1.68.1-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:d99abcd61760ebb34bdff37e5a3ba333c5cc09feda8c1ad42547bea0416ada78", size = 11078566 },
+    { url = "https://files.pythonhosted.org/packages/d5/b0/ad4c66f2e3181b4eab99885686c960c403ae2300bacfe427526282facc07/grpcio-1.68.1-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:f8261fa2a5f679abeb2a0a93ad056d765cdca1c47745eda3f2d87f874ff4b8c9", size = 5690039 },
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