diff --git a/python/MANIFEST.in b/python/MANIFEST.in
index f883d48c1..e04dbff56 100644
--- a/python/MANIFEST.in
+++ b/python/MANIFEST.in
@@ -7,3 +7,4 @@ recursive-exclude * __pycache__
 recursive-exclude * *.pyc
 include README.md
 include LICENSE
+include graphframes/tutorials/data/.exists
diff --git a/python/graphframes/console.py b/python/graphframes/console.py
new file mode 100644
index 000000000..d2b38d28b
--- /dev/null
+++ b/python/graphframes/console.py
@@ -0,0 +1,19 @@
+import click
+from graphframes.tutorials import download
+
+
+@click.group()
+def cli():
+    """GraphFrames CLI: a collection of commands for graphframes."""
+    pass
+
+
+cli.add_command(download.stackexchange)
+
+
+def main():
+    cli()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/python/graphframes/tutorials/download.py b/python/graphframes/tutorials/download.py
new file mode 100755
index 000000000..4eadfa647
--- /dev/null
+++ b/python/graphframes/tutorials/download.py
@@ -0,0 +1,88 @@
+#!/usr/bin/env python
+
+"""Download and decompress the Stack Exchange data dump from the Internet Archive."""
+
+import os
+
+import click
+import py7zr
+import requests  # type: ignore
+
+
+@click.command()
+@click.argument("subdomain")
+@click.option(
+    "--data-dir",
+    default="python/graphframes/tutorials/data",
+    help="Directory to store downloaded files",
+)
+@click.option(
+    "--extract/--no-extract", default=True, help="Whether to extract the archive after download"
+)
+def stackexchange(subdomain: str, data_dir: str, extract: bool) -> None:
+    """Download Stack Exchange archive for a given SUBDOMAIN.
+
+    Example: python/graphframes/tutorials/download.py stats.meta
+
+    Note: This won't work for stackoverflow.com archives due to size.
+    """
+    # Create data directory if it doesn't exist
+    os.makedirs(data_dir, exist_ok=True)
+
+    # Construct archive URL and filename
+    archive_url = f"https://archive.org/download/stackexchange/{subdomain}.stackexchange.com.7z"
+    archive_path = os.path.join(data_dir, f"{subdomain}.stackexchange.com.7z")
+
+    click.echo(f"Downloading archive from {archive_url}")
+
+    try:
+        # Download the file with retries
+        max_retries = 3
+        retry_count = 0
+
+        while retry_count < max_retries:
+            try:
+                response = requests.get(archive_url, stream=True)
+                response.raise_for_status()  # Raise exception for bad status codes
+                break
+            except (
+                requests.exceptions.RequestException,
+                requests.exceptions.ConnectionError,
+                requests.exceptions.HTTPError,
+                requests.exceptions.Timeout,
+            ) as e:
+                retry_count += 1
+                if retry_count == max_retries:
+                    click.echo(f"Failed to download after {max_retries} attempts: {e}", err=True)
+                    raise click.Abort()
+                click.echo(f"Download attempt {retry_count} failed, retrying...")
+
+        total_size = int(response.headers.get("content-length", 0))
+
+        with click.progressbar(length=total_size, label="Downloading") as bar:  # type: ignore
+            with open(archive_path, "wb") as f:
+                for chunk in response.iter_content(chunk_size=8192):
+                    if chunk:
+                        f.write(chunk)
+                        bar.update(len(chunk))
+
+        click.echo(f"Download complete: {archive_path}")
+
+        # Extract if requested
+        if extract:
+            click.echo("Extracting archive...")
+            output_dir = f"{subdomain}.stackexchange.com"
+            with py7zr.SevenZipFile(archive_path, mode="r") as z:
+                z.extractall(path=os.path.join(data_dir, output_dir))
+            click.echo(f"Extraction complete: {output_dir}")
+
+    except requests.exceptions.RequestException as e:
+        click.echo(f"Error downloading archive: {e}", err=True)
+        raise click.Abort()
+    except py7zr.Bad7zFile as e:
+        click.echo(f"Error extracting archive: {e}", err=True)
+        raise click.Abort()
+
+
+if __name__ == "__main__":
+    stackexchange()
diff --git a/python/graphframes/tutorials/motif.py b/python/graphframes/tutorials/motif.py
new file mode 100644
index 000000000..59691946a
--- /dev/null
+++ b/python/graphframes/tutorials/motif.py
@@ -0,0 +1,203 @@
+"""Demonstrate GraphFrames network motif finding capabilities. Code from the Network Motif Finding Tutorial."""
+
+#
+# Interactive Usage: pyspark --packages graphframes:graphframes:0.8.4-spark3.5-s_2.12
+#
+# Batch Usage: spark-submit --packages graphframes:graphframes:0.8.4-spark3.5-s_2.12 python/graphframes/tutorials/motif.py
+#
+
+import click
+import pyspark.sql.functions as F
+from pyspark.sql import DataFrame, SparkSession
+
+from graphframes import GraphFrame
+
+# Initialize a SparkSession
+spark: SparkSession = SparkSession.builder.appName("Stack Overflow Motif Analysis").getOrCreate()
+spark.sparkContext.setCheckpointDir("/tmp/graphframes-checkpoints/motif")
+
+# Change me if you download a different stackexchange site
+STACKEXCHANGE_SITE = "stats.meta.stackexchange.com"
+BASE_PATH = f"python/graphframes/tutorials/data/{STACKEXCHANGE_SITE}"
+
+
+#
+# Load the nodes and edges from disk, repartition, checkpoint [plan got long for some reason] and cache.
+#
+
+# We created these in stackexchange.py from Stack Exchange data dump XML files
+NODES_PATH: str = f"{BASE_PATH}/Nodes.parquet"
+nodes_df: DataFrame = spark.read.parquet(NODES_PATH)
+
+# Repartition the nodes to give our motif searches parallelism
+nodes_df = nodes_df.repartition(50).checkpoint().cache()
+
+# We created these in stackexchange.py from Stack Exchange data dump XML files
+EDGES_PATH: str = f"{BASE_PATH}/Edges.parquet"
+edges_df: DataFrame = spark.read.parquet(EDGES_PATH)
+
+# Repartition the edges to give our motif searches parallelism
+edges_df = edges_df.repartition(50).checkpoint().cache()
+
+# What kind of nodes we do we have to work with?
+node_counts = (
+    nodes_df.select("id", F.col("Type").alias("Node Type"))
+    .groupBy("Node Type")
+    .count()
+    .orderBy(F.col("count").desc())
+    # Add a comma formatted column for display
+    .withColumn("count", F.format_number(F.col("count"), 0))
+)
+node_counts.show()
+
+# What kind of edges do we have to work with?
+edge_counts = (
+    edges_df.select("src", "dst", F.col("relationship").alias("Edge Type"))
+    .groupBy("Edge Type")
+    .count()
+    .orderBy(F.col("count").desc())
+    # Add a comma formatted column for display
+    .withColumn("count", F.format_number(F.col("count"), 0))
+)
+edge_counts.show()
+
+g = GraphFrame(nodes_df, edges_df)
+
+g.vertices.show(10)
+click.echo(f"Node columns: {g.vertices.columns}")
+
+g.edges.sample(0.0001).show(10)
+
+# Sanity test that all edges have valid ids
+edge_count = g.edges.count()
+valid_edge_count = (
+    g.edges.join(g.vertices, on=g.edges.src == g.vertices.id)
+    .select("src", "dst", "relationship")
+    .join(g.vertices, on=g.edges.dst == g.vertices.id)
+    .count()
+)
+
+# Just up and die if we have edges that point to non-existent nodes
+assert (
+    edge_count == valid_edge_count
+), f"Edge count {edge_count} != valid edge count {valid_edge_count}"
+click.echo(f"Edge count: {edge_count:,} == Valid edge count: {valid_edge_count:,}")
+
+# G4: Continuous Triangles
+paths = g.find("(a)-[e1]->(b); (b)-[e2]->(c); (c)-[e3]->(a)")
+
+# Show the first path
+paths.show(3)
+
+graphlet_type_df = paths.select(
+    F.col("a.Type").alias("A_Type"),
+    F.col("e1.relationship").alias("(a)-[e1]->(b)"),
+    F.col("b.Type").alias("B_Type"),
+    F.col("e2.relationship").alias("(b)-[e2]->(c)"),
+    F.col("c.Type").alias("C_Type"),
+    F.col("e3.relationship").alias("(c)-[e3]->(a)"),
+)
+
+graphlet_count_df = (
+    graphlet_type_df.groupby(
+        "A_Type", "(a)-[e1]->(b)", "B_Type", "(b)-[e2]->(c)", "C_Type", "(c)-[e3]->(a)"
+    )
+    .count()
+    .orderBy(F.col("count").desc())
+    # Add a comma formatted column for display
+    .withColumn("count", F.format_number(F.col("count"), 0))
+)
+graphlet_count_df.show()
+
+# G5: Divergent Triangles
+paths = g.find("(a)-[e1]->(b); (a)-[e2]->(c); (c)-[e3]->(b)")
+
+graphlet_type_df = paths.select(
+    F.col("a.Type").alias("A_Type"),
+    F.col("e1.relationship").alias("(a)-[e1]->(b)"),
+    F.col("b.Type").alias("B_Type"),
+    F.col("e2.relationship").alias("(a)-[e2]->(c)"),
+    F.col("c.Type").alias("C_Type"),
+    F.col("e3.relationship").alias("(c)-[e3]->(b)"),
+)
+
+graphlet_count_df = (
+    graphlet_type_df.groupby(
+        "A_Type", "(a)-[e1]->(b)", "B_Type", "(a)-[e2]->(c)", "C_Type", "(c)-[e3]->(b)"
+    )
+    .count()
+    .orderBy(F.col("count").desc())
+    # Add a comma formatted column for display
+    .withColumn("count", F.format_number(F.col("count"), 0))
+)
+graphlet_count_df.show()
+
+# G17: A directed 3-path is a surprisingly diverse graphlet
+paths = g.find("(a)-[e1]->(b); (b)-[e2]->(c); (d)-[e3]->(c)")
+
+# Visualize the four-path by counting instances of paths by node / edge type
+graphlet_type_df = paths.select(
+    F.col("a.Type").alias("A_Type"),
+    F.col("e1.relationship").alias("(a)-[e1]->(b)"),
+    F.col("b.Type").alias("B_Type"),
+    F.col("e2.relationship").alias("(b)-[e2]->(c)"),
+    F.col("c.Type").alias("C_Type"),
+    F.col("e3.relationship").alias("(d)-[e3]->(c)"),
+    F.col("d.Type").alias("D_Type"),
+)
+graphlet_count_df = (
+    graphlet_type_df.groupby(
+        "A_Type",
+        "(a)-[e1]->(b)",
+        "B_Type",
+        "(b)-[e2]->(c)",
+        "C_Type",
+        "(d)-[e3]->(c)",
+        "D_Type",
+    )
+    .count()
+    .orderBy(F.col("count").desc())
+    # Add a comma formatted column for display
+    .withColumn("count", F.format_number(F.col("count"), 0))
+)
+graphlet_count_df.show()
+
+graphlet_count_df.orderBy(
+    [
+        "A_Type",
+        "(a)-[e1]->(b)",
+        "B_Type",
+        "(b)-[e2]->(c)",
+        "C_Type",
+        "(d)-[e3]->(c)",
+        "D_Type",
+    ],
+    ascending=False,
+).show(104)
+
+# A user answers an answer that answers a question that links to an answer.
+linked_vote_paths = paths.filter(
+    (F.col("a.Type") == "Vote")
+    & (F.col("e1.relationship") == "CastFor")
+    & (F.col("b.Type") == "Question")
+    & (F.col("e2.relationship") == "Links")
+    & (F.col("c.Type") == "Question")
+    & (F.col("e3.relationship") == "CastFor")
+    & (F.col("d.Type") == "Vote")
+)
+
+# Sanity check the count - it should match the table above
+linked_vote_paths.count()
+
+b_vote_counts = linked_vote_paths.select("a", "b").distinct().groupBy("b").count()
+c_vote_counts = linked_vote_paths.select("c", "d").distinct().groupBy("c").count()
+
+linked_vote_counts = (
+    linked_vote_paths.filter((F.col("a.VoteTypeId") == 2) & (F.col("d.VoteTypeId") == 2))
+    .select("b", "c")
+    .join(b_vote_counts, on="b", how="inner")
+    .withColumnRenamed("count", "b_count")
+    .join(c_vote_counts, on="c", how="inner")
+    .withColumnRenamed("count", "c_count")
+)
+linked_vote_counts.stat.corr("b_count", "c_count")
diff --git a/python/graphframes/tutorials/stackexchange.py b/python/graphframes/tutorials/stackexchange.py
new file mode 100644
index 000000000..02ebb2bb5
--- /dev/null
+++ b/python/graphframes/tutorials/stackexchange.py
@@ -0,0 +1,597 @@
+"""Build a Graph out of the Stack Exchange Data Dump XML files."""
+
+#
+# Interactive Usage: pyspark --packages com.databricks:spark-xml_2.12:0.18.0
+#
+# Batch Usage: spark-submit --packages com.databricks:spark-xml_2.12:0.18.0 python/graphframes/tutorials/stackexchange.py
+#
+from __future__ import annotations
+
+import re
+
+import click
+import pyspark.sql.functions as F
+import pyspark.sql.types as T
+from pyspark.sql import DataFrame, SparkSession
+
+# Change me if you download a different stackexchange site
+STACKEXCHANGE_SITE = "stats.meta.stackexchange.com"
+BASE_PATH = f"python/graphframes/tutorials/data/{STACKEXCHANGE_SITE}"
+
+
+#
+# Some utility functions
+#
+
+
+def remove_prefix(df: DataFrame) -> DataFrame:
+    """Remove the _ prefix present in the fields of the DataFrame"""
+    field_names = [x.name for x in df.schema]
+    new_field_names = [x[1:] for x in field_names]
+    s = []
+
+    # Substitute the old name for the new one
+    for old, new in zip(field_names, new_field_names):
+        s.append(F.col(old).alias(new))
+    return df.select(s)
+
+
+@F.udf(returnType=T.ArrayType(T.StringType()))
+def split_tags(tags: str) -> list[str]:
+    if not tags:
+        return []
+    # Remove < and > and split into array
+    return re.findall(r"<([^>]+)>", tags)
+
+
+#
+# Initialize a SparkSession with case sensitivity
+#
+
+spark: SparkSession = SparkSession.builder.appName("Stack Exchange Graph Builder").getOrCreate()
+spark.sparkContext.setCheckpointDir("/tmp/graphframes-checkpoints/stackexchange")
+
+click.echo("Loading data for stats.meta.stackexchange.com ...")
+
+
+#
+# Load the Posts...
+#
+posts_df: DataFrame = (
+    spark.read.format("xml")
+    .options(rowTag="row")
+    .options(rootTag="posts")
+    .load(f"{BASE_PATH}/Posts.xml")
+)
+click.echo(f"\nTotal Posts:       {posts_df.count():,}")
+
+# Remove the _ prefix from field names
+posts_df = remove_prefix(posts_df)
+
+# Create a list of tags
+posts_df = (
+    posts_df.withColumn(
+        "ParsedTags", F.split(F.regexp_replace(F.col("Tags"), "^\\||\\|$", ""), "\\|")
+    )
+    .drop("Tags")
+    .withColumnRenamed("ParsedTags", "Tags")
+)
+
+
+#
+# Building blocks: separate the questions and answers
+#
+
+# Do the questions look ok? Questions have NO parent ID and DO have a Title
+questions_df: DataFrame = posts_df.filter(posts_df.ParentId.isNull())
+questions_df = questions_df.withColumn("Type", F.lit("Question")).cache()
+click.echo(f"\nTotal questions: {questions_df.count():,}\n")
+
+questions_df.select("ParentId", "Title", "Body").show(10)
+
+# Answers DO have a ParentId parent post and no Title
+answers_df: DataFrame = posts_df.filter(posts_df.ParentId.isNotNull())
+answers_df = answers_df.withColumn("Type", F.lit("Answer")).cache()
+click.echo(f"\nTotal answers: {answers_df.count():,}\n")
+
+answers_df.select("ParentId", "Title", "Body").show(10)
+
+
+#
+# Load the PostLinks...
+#
+
+post_links_df = (
+    spark.read.format("xml")
+    .options(rowTag="row")
+    .options(rootTag="postlinks")
+    .load(f"{BASE_PATH}/PostLinks.xml")
+)
+click.echo(f"Total PostLinks:   {post_links_df.count():,}")
+
+# Remove the _ prefix from field names
+post_links_df = (
+    remove_prefix(post_links_df)
+    .withColumn(
+        "LinkType",
+        F.when(F.col("LinkTypeId") == 1, "Linked")
+        .when(F.col("LinkTypeId") == 3, "Duplicate")
+        .otherwise("Unknown"),
+    )
+    .withColumn("Type", F.lit("PostLinks"))
+)
+
+
+#
+# Load the PostHistory...
+#
+
+post_history_df = (
+    spark.read.format("xml")
+    .options(rowTag="row")
+    .options(rootTag="posthistory")
+    .load(f"{BASE_PATH}/PostHistory.xml")
+)
+click.echo(f"Total PostHistory: {post_history_df.count():,}")
+
+# Remove the _ prefix from field names
+post_history_df = remove_prefix(post_history_df).withColumn("Type", F.lit("PostHistory"))
+
+
+#
+# Load the Comments...
+#
+
+comments_df = (
+    spark.read.format("xml")
+    .options(rowTag="row")
+    .options(rootTag="comments")
+    .load(f"{BASE_PATH}/Comments.xml")
+)
+click.echo(f"Total Comments:    {comments_df.count():,}")
+
+# Remove the _ prefix from field names
+comments_df = remove_prefix(comments_df).withColumn("Type", F.lit("Comment"))
+
+
+#
+# Load the Users...
+#
+
+users_df = (
+    spark.read.format("xml")
+    .options(rowTag="row")
+    .options(rootTag="users")
+    .load(f"{BASE_PATH}/Users.xml")
+)
+click.echo(f"Total Users:       {users_df.count():,}")
+
+# Remove the _ prefix from field names
+users_df = remove_prefix(users_df).withColumn("Type", F.lit("User"))
+
+
+#
+# Load the Votes...
+#
+
+votes_df = (
+    spark.read.format("xml")
+    .options(rowTag="row")
+    .options(rootTag="votes")
+    .load(f"{BASE_PATH}/Votes.xml")
+)
+click.echo(f"Total Votes:       {votes_df.count():,}")
+
+# Remove the _ prefix from field names
+votes_df = remove_prefix(votes_df).withColumn("Type", F.lit("Vote"))
+
+# Add a VoteType column
+votes_df = votes_df.withColumn(
+    "VoteType",
+    F.when(F.col("VoteTypeId") == 2, "UpVote")
+    .when(F.col("VoteTypeId") == 3, "DownVote")
+    .when(F.col("VoteTypeId") == 4, "Favorite")
+    .when(F.col("VoteTypeId") == 5, "Close")
+    .when(F.col("VoteTypeId") == 6, "Reopen")
+    .when(F.col("VoteTypeId") == 7, "BountyStart")
+    .when(F.col("VoteTypeId") == 8, "BountyClose")
+    .when(F.col("VoteTypeId") == 9, "Deletion")
+    .when(F.col("VoteTypeId") == 10, "Undeletion")
+    .when(F.col("VoteTypeId") == 11, "Spam")
+    .when(F.col("VoteTypeId") == 12, "InformModerator")
+    .otherwise("Unknown"),
+)
+
+
+#
+# Load the Tags...
+#
+
+tags_df = (
+    spark.read.format("xml")
+    .options(rowTag="row")
+    .options(rootTag="tags")
+    .load(f"{BASE_PATH}/Tags.xml")
+)
+click.echo(f"Total Tags:        {tags_df.count():,}")
+
+# Remove the _ prefix from field names
+tags_df = remove_prefix(tags_df).withColumn("Type", F.lit("Tag"))
+
+
+#
+# Load the Badges...
+#
+
+badges_df = (
+    spark.read.format("xml")
+    .options(rowTag="row")
+    .options(rootTag="badges")
+    .load(f"{BASE_PATH}/Badges.xml")
+)
+click.echo(f"Total Badges:      {badges_df.count():,}\n")
+
+# Remove the _ prefix from field names
+badges_df = remove_prefix(badges_df).withColumn("Type", F.lit("Badge"))
+
+
+#
+# Form the nodes from the UNION of posts, users, votes and their combined schemas
+#
+
+all_cols: list[tuple[str, T.StructField]] = list(
+    set(
+        list(zip(answers_df.columns, answers_df.schema))
+        + list(zip(questions_df.columns, questions_df.schema))
+        + list(zip(post_links_df.columns, post_links_df.schema))
+        + list(zip(comments_df.columns, comments_df.schema))
+        + list(zip(users_df.columns, users_df.schema))
+        + list(zip(votes_df.columns, votes_df.schema))
+        + list(zip(tags_df.columns, tags_df.schema))
+        + list(zip(badges_df.columns, badges_df.schema))
+    )
+)
+all_column_names: list[str] = sorted([x[0] for x in all_cols])
+
+
+def add_missing_columns(df: DataFrame, all_cols: list[tuple[str, T.StructField]]) -> DataFrame:
+    """Add any missing columns from any DataFrame among several we want to merge."""
+    for col_name, schema_field in all_cols:
+        if col_name not in df.columns:
+            df = df.withColumn(col_name, F.lit(None).cast(schema_field.dataType))
+    return df
+
+
+# Now apply this function to each of your DataFrames to get a consistent schema
+# posts_df = add_missing_columns(posts_df, all_cols).select(all_column_names)
+questions_df = add_missing_columns(questions_df, all_cols).select(all_column_names)
+answers_df = add_missing_columns(answers_df, all_cols).select(all_column_names)
+post_links_df = add_missing_columns(post_links_df, all_cols).select(all_column_names)
+users_df = add_missing_columns(users_df, all_cols).select(all_column_names)
+votes_df = add_missing_columns(votes_df, all_cols).select(all_column_names)
+tags_df = add_missing_columns(tags_df, all_cols).select(all_column_names)
+badges_df = add_missing_columns(badges_df, all_cols).select(all_column_names)
+assert (
+    set(questions_df.columns)
+    == set(answers_df.columns)
+    == set(post_links_df.columns)
+    == set(users_df.columns)
+    == set(votes_df.columns)
+    == set(all_column_names)
+    == set(tags_df.columns)
+    == set(badges_df.columns)
+)
+
+# Now union them together and remove duplicates
+nodes_df: DataFrame = (
+    questions_df.unionByName(answers_df)
+    .unionByName(post_links_df)
+    .unionByName(users_df)
+    .unionByName(votes_df)
+    .unionByName(tags_df)
+    .unionByName(badges_df)
+    .distinct()
+)
+click.echo(f"Total distinct nodes: {nodes_df.count():,}")
+
+# Now add a unique lowercase 'id' field - standard for GraphFrames - moving the original...
+# Stack Exchange Id to StackId
+nodes_df = nodes_df.withColumnRenamed("Id", "StackId").drop("Id")
+
+# Update the column list...
+if "Id" in all_column_names:
+    all_column_names.remove("Id")
+all_column_names += ["StackId"]
+all_column_names = sorted(all_column_names)
+
+# Add the UUID 'id' field for GraphFrames. It will go in edges as 'src' and 'dst'
+nodes_df = nodes_df.withColumn("id", F.expr("uuid()")).select("id", *all_column_names)
+
+# Now create posts - combined questions and answers for things that can apply to them both
+posts_df = questions_df.unionByName(answers_df).cache()
+
+
+#
+# Store the nodes to disk, reload and cache
+#
+
+NODES_PATH: str = f"{BASE_PATH}/Nodes.parquet"
+
+# Write to disk and load back again
+nodes_df.write.mode("overwrite").parquet(NODES_PATH)
+nodes_df = spark.read.parquet(NODES_PATH)
+
+nodes_df.select("id", "Type").groupBy("Type").count().orderBy(F.col("count").desc()).show()
+
+# +---------+------+
+# |     Type| count|
+# +---------+------+
+# |    Badge|43,029|
+# |     Vote|42,593|
+# |     User|37,709|
+# |   Answer| 2,978|
+# | Question| 2,025|
+# |PostLinks| 1,274|
+# |      Tag|   143|
+# +---------+------+
+
+# Helps performance of GraphFrames' algorithms
+nodes_df = nodes_df.cache()
+
+# Make sure we have the right columns and cached data
+posts_df = nodes_df.filter(nodes_df.Type.isin("Question", "Answer")).cache()
+questions_df = nodes_df.filter(nodes_df.Type == "Question").cache()
+answers_df = nodes_df.filter(nodes_df.Type == "Answer").cache()
+post_links_df = nodes_df.filter(nodes_df.Type == "PostLinks").cache()
+users_df = nodes_df.filter(nodes_df.Type == "User").cache()
+votes_df = nodes_df.filter(nodes_df.Type == "Vote").cache()
+tags_df = nodes_df.filter(nodes_df.Type == "Tag").cache()
+badges_df = nodes_df.filter(nodes_df.Type == "Badge").cache()
+
+
+#
+# Build the edges DataFrame:
+#
+# * [Vote]--CastFor-->[Post]
+# * [User]--Asks-->[Question]
+# * [User]--Posts-->[Answer]
+# * [Post]--Answers-->[Question]
+# * [Tag]--Tags-->[Post]
+# * [User]--Earns-->[Badge]
+# * [Post]--Links-->[Post]
+#
+# Remember: 'src', 'dst' and 'relationship' are standard edge fields in GraphFrames
+# Remember: we must produce src/dst based on lowercase 'id' UUID, not 'Id' which is Stack Overflow's integer.
+#
+
+#
+# Create a [Vote]--CastFor-->[Post] edge... remember a Post is a Question or Answer
+#
+
+src_vote_df: DataFrame = votes_df.select(
+    F.col("id").alias("src"),
+    F.col("StackId").alias("VoteId"),
+    # Everything has all the fields - should build from base records but need UUIDs
+    F.col("PostId").alias("VotePostId"),
+)
+cast_for_edge_df: DataFrame = src_vote_df.join(
+    posts_df, on=src_vote_df.VotePostId == posts_df.StackId, how="inner"
+).select(
+    # 'src' comes from the votes' 'id'
+    "src",
+    # 'dst' comes from the posts' 'id'
+    F.col("id").alias("dst"),
+    # All edges have a 'relationship' field
+    F.lit("CastFor").alias("relationship"),
+)
+click.echo(f"Total CastFor edges: {cast_for_edge_df.count():,}")
+click.echo(f"Percentage of linked votes: {cast_for_edge_df.count() / votes_df.count():.2%}\n")
+
+
+#
+# Create a [User]--Asks-->[Question] edge
+#
+
+questions_asked_df: DataFrame = questions_df.select(
+    F.col("OwnerUserId").alias("QuestionUserId"),
+    F.col("id").alias("dst"),
+    F.lit("Asks").alias("relationship"),
+)
+user_asks_edges_df: DataFrame = questions_asked_df.join(
+    users_df, on=questions_asked_df.QuestionUserId == users_df.StackId, how="inner"
+).select(
+    # 'src' comes from the users' 'id'
+    F.col("id").alias("src"),
+    # 'dst' comes from the posts' 'id'
+    "dst",
+    # All edges have a 'relationship' field
+    "relationship",
+)
+click.echo(f"Total Asks edges: {user_asks_edges_df.count():,}")
+click.echo(
+    f"Percentage of asked questions linked to users: {user_asks_edges_df.count() / questions_df.count():.2%}\n"
+)
+
+
+#
+# Create a [User]--Posts-->[Answer] edge.
+#
+
+user_answers_df: DataFrame = answers_df.select(
+    F.col("OwnerUserId").alias("AnswerUserId"),
+    F.col("id").alias("dst"),
+    F.lit("Posts").alias("relationship"),
+)
+user_answers_edges_df = user_answers_df.join(
+    users_df, on=user_answers_df.AnswerUserId == users_df.StackId, how="inner"
+).select(
+    # 'src' comes from the users' 'id'
+    F.col("id").alias("src"),
+    # 'dst' comes from the posts' 'id'
+    "dst",
+    # All edges have a 'relationship' field
+    "relationship",
+)
+click.echo(f"Total User Answers edges: {user_answers_edges_df.count():,}")
+click.echo(
+    f"Percentage of answers linked to users: {user_answers_edges_df.count() / answers_df.count():.2%}\n"
+)
+
+
+#
+# Create a [Answer]--Answers-->[Question] edge
+#
+
+src_answers_df: DataFrame = answers_df.select(
+    F.col("id").alias("src"),
+    F.col("StackId").alias("AnswerId"),
+    F.col("ParentId").alias("AnswerParentId"),
+)
+question_answers_edges_df: DataFrame = src_answers_df.join(
+    posts_df, on=src_answers_df.AnswerParentId == questions_df.StackId, how="inner"
+).select(
+    # 'src' comes from the answers' 'id'
+    "src",
+    # 'dst' comes from the posts' 'id'
+    F.col("id").alias("dst"),
+    # All edges have a 'relationship' field
+    F.lit("Answers").alias("relationship"),
+)
+click.echo(f"Total Posts Answers edges: {question_answers_edges_df.count():,}")
+click.echo(
+    f"Percentage of linked answers: {question_answers_edges_df.count() / answers_df.count():.2%}\n"
+)
+
+
+#
+# Create a [Tag]--Tags-->[Post] edge... remember a Post is a Question or Answer
+#
+
+src_tags_df: DataFrame = posts_df.select(
+    F.col("id").alias("dst"),
+    # First remove leading/trailing < and >, then split on "><"
+    F.explode("Tags").alias("Tag"),
+)
+tags_edge_df: DataFrame = src_tags_df.join(
+    tags_df, on=src_tags_df.Tag == tags_df.TagName, how="inner"
+).select(
+    # 'src' comes from the posts' 'id'
+    F.col("id").alias("src"),
+    # 'dst' comes from the tags' 'id'
+    "dst",
+    # All edges have a 'relationship' field
+    F.lit("Tags").alias("relationship"),
+)
+click.echo(f"Total Tags edges: {tags_edge_df.count():,}")
+click.echo(f"Percentage of linked tags: {tags_edge_df.count() / posts_df.count():.2%}\n")
+
+
+#
+# Create a [User]--Earns-->[Badge] edge
+#
+
+earns_edges_df: DataFrame = badges_df.select(
+    F.col("UserId").alias("BadgeUserId"),
+    F.col("id").alias("dst"),
+    F.lit("Earns").alias("relationship"),
+)
+earns_edges_df = earns_edges_df.join(
+    users_df, on=earns_edges_df.BadgeUserId == users_df.StackId, how="inner"
+).select(
+    # 'src' comes from the users' 'id'
+    F.col("id").alias("src"),
+    # 'dst' comes from the badges' 'id'
+    "dst",
+    # All edges have a 'relationship' field
+    "relationship",
+)
+click.echo(f"Total Earns edges: {earns_edges_df.count():,}")
+click.echo(f"Percentage of earned badges: {earns_edges_df.count() / badges_df.count():.2%}\n")
+
+
+#
+# Create a [Post]--Links-->[Post] edge... remember a Post is a Question or Answer
+# Also a   [Post]--Duplicates-->[Post] edge... remember a Post is a Question or Answer
+#
+
+trim_links_df: DataFrame = post_links_df.select(
+    F.col("PostId").alias("SrcPostId"),
+    F.col("RelatedPostId").alias("DstPostId"),
+    "LinkType",
+)
+links_src_edge_df: DataFrame = trim_links_df.join(
+    posts_df.drop("LinkType"), on=trim_links_df.SrcPostId == posts_df.StackId, how="inner"
+).select(
+    # 'dst' comes from the posts' 'id'
+    F.col("id").alias("src"),
+    "DstPostId",
+    "LinkType",
+)
+raw_links_edge_df = links_src_edge_df.join(
+    posts_df.drop("LinkType"), on=links_src_edge_df.DstPostId == posts_df.StackId, how="inner"
+).select(
+    "src",
+    # 'src' comes from the posts' 'id'
+    F.col("id").alias("dst"),
+    # All edges have a 'relationship' field
+    F.lit("Links").alias("relationship"),
+    "LinkType",
+)
+
+duplicates_edge_df: DataFrame = (
+    raw_links_edge_df.filter(F.col("LinkType") == "Duplicate")
+    .withColumn("relationship", F.lit("Duplicates"))
+    .select("src", "dst", "relationship")
+)
+click.echo(f"Total Duplicates edges: {duplicates_edge_df.count():,}")
+click.echo(
+    f"Percentage of duplicate posts: {duplicates_edge_df.count() / post_links_df.count():.2%}\n"
+)
+
+linked_edge_df = (
+    raw_links_edge_df.filter(F.col("LinkType") == "Linked")
+    .withColumn("relationship", F.lit("Links"))
+    .select("src", "dst", "relationship")
+)
+click.echo(f"Total Links edges: {linked_edge_df.count():,}")
+click.echo(f"Percentage of linked posts: {linked_edge_df.count() / post_links_df.count():.2%}\n")
+
+
+#
+# Combine all the edges together into one relationships DataFrame
+#
+
+relationships_df: DataFrame = (
+    cast_for_edge_df.unionByName(user_asks_edges_df)
+    .unionByName(user_answers_edges_df)
+    .unionByName(question_answers_edges_df)
+    .unionByName(tags_edge_df)
+    .unionByName(earns_edges_df)
+    .unionByName(duplicates_edge_df)
+    .unionByName(linked_edge_df)
+)
+relationships_df.groupBy("relationship").count().orderBy(F.col("count").desc()).withColumn(
+    "count", F.format_number(F.col("count"), 0)
+).show()
+
+
+# +------------+------+
+# |relationship| count|
+# +------------+------+
+# |       Earns|43,029|
+# |     CastFor|40,701|
+# |        Tags| 4,427|
+# |     Answers| 2,978|
+# |       Posts| 2,767|
+# |        Asks| 1,934|
+# |       Links| 1,180|
+# |  Duplicates|    88|
+# +------------+------+
+
+EDGES_PATH: str = f"{BASE_PATH}/Edges.parquet"
+
+# Write to disk and back again
+relationships_df.write.mode("overwrite").parquet(EDGES_PATH)
+
+spark.stop()
+click.echo("Spark stopped.")
diff --git a/python/graphframes/tutorials/utils.py b/python/graphframes/tutorials/utils.py
new file mode 100644
index 000000000..46db14d96
--- /dev/null
+++ b/python/graphframes/tutorials/utils.py
@@ -0,0 +1,125 @@
+"""Utilities for Network Moitif Finding Tutorial"""
+
+from pyspark.sql import DataFrame
+from pyspark.sql import functions as F
+
+from graphframes import GraphFrame
+
+
+def three_edge_count(paths: DataFrame) -> DataFrame:
+    """three_edge_count View the counts of the different types of 3-node graphlets in the graph.
+
+    Parameters
+    ----------
+    paths : pyspark.sql.DataFrame
+        A DataFrame of 3-paths in the graph.
+
+    Returns
+    -------
+    DataFrame
+        A DataFrame of the counts of the different types of 3-node graphlets in the graph.
+    """
+    graphlet_type_df = paths.select(
+        F.col("a.Type").alias("A_Type"),
+        F.col("e1.relationship").alias("E_relationship"),
+        F.col("b.Type").alias("B_Type"),
+        F.col("e2.relationship").alias("E2_relationship"),
+        F.col("c.Type").alias("C_Type"),
+        F.col("e3.relationship").alias("E3_relationship"),
+        F.when(F.col("d").isNotNull(), F.col("d.Type")).alias("D_Type"),
+    )
+    graphlet_count_df = (
+        graphlet_type_df.groupby(
+            "A_Type", "E_relationship", "B_Type", "E2_relationship", "C_Type", "E3_relationship"
+        )
+        .count()
+        .orderBy(F.col("count").desc())
+        # Add a comma formatted column for display
+        .withColumn("count", F.format_number(F.col("count"), 0))
+    )
+    return graphlet_count_df
+
+
+def four_edge_count(paths: DataFrame) -> DataFrame:
+    """four_edge_count View the counts of the different types of 4-node graphlets in the graph.
+
+    Parameters
+    ----------
+    paths : DataFrame
+        A DataFrame of 4-paths in the graph.
+
+    Returns
+    -------
+    DataFrame
+        A DataFrame of the counts of the different types of 4-node graphlets in the graph.
+    """
+
+    graphlet_type_df = paths.select(
+        F.col("a.Type").alias("A_Type"),
+        F.col("e1.relationship").alias("E_relationship"),
+        F.col("b.Type").alias("B_Type"),
+        F.col("e2.relationship").alias("E2_relationship"),
+        F.col("c.Type").alias("C_Type"),
+        F.col("e3.relationship").alias("E3_relationship"),
+        F.col("d.Type").alias("D_Type"),
+        F.col("e4.relationship").alias("E4_relationship"),
+        F.when(F.col("e").isNotNull(), F.col("e.Type")).alias("E_Type"),
+    )
+    graphlet_count_df = (
+        graphlet_type_df.groupby(
+            "A_Type",
+            "E_relationship",
+            "B_Type",
+            "E2_relationship",
+            "C_Type",
+            "E3_relationship",
+            "D_Type",
+            "E4_relationship",
+        )
+        .count()
+        .orderBy(F.col("count").desc())
+        # Add a comma formatted column for display
+        .withColumn("count", F.format_number(F.col("count"), 0))
+    )
+    return graphlet_count_df
+
+
+def add_degree(g: GraphFrame) -> GraphFrame:
+    """add_degree compute the degree, adding it as a property of the nodes in the GraphFrame.
+
+    Parameters
+    ----------
+    g : GraphFrame
+        Any valid GraphFrame
+
+    Returns
+    -------
+    GraphFrame
+        Same GraphFrame with a 'degree' property added
+    """
+    degree_vertices: DataFrame = g.vertices.join(g.degrees, on="id")
+    return GraphFrame(degree_vertices, g.edges)
+
+
+def add_type_degree(g: GraphFrame) -> DataFrame:
+    """add_type_degree add a map property to the vertices with the degree by each type of relationship.
+
+    Parameters
+    ----------
+    g : GraphFrame
+        Any valid GraphFrame
+
+    Returns
+    -------
+    DataFrame - I am broke, next line is wrong
+        A GraphFrame with a map[type:degree] 'type_degree' field added to the vertices
+    """
+    type_degree: DataFrame = (
+        g.edges.select(F.col("src").alias("id"), "relationship")
+        .filter(F.col("id").isNotNull())
+        .groupby("id", "relationship")
+        .count()
+    )
+    type_degree = type_degree.withColumn("type_degree", F.create_map(type_degree.columns))
+    type_degree = type_degree.select("src", "type_degree")
+    return g.vertices.join(type_degree, on="src")
diff --git a/python/poetry.lock b/python/poetry.lock
index 0fb5fb139..fa319d849 100644
--- a/python/poetry.lock
+++ b/python/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
 
 [[package]]
 name = "black"
@@ -47,13 +47,385 @@ d = ["aiohttp (>=3.10)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
+[[package]]
+name = "brotli"
+version = "1.1.0"
+description = "Python bindings for the Brotli compression library"
+optional = false
+python-versions = "*"
+groups = ["tutorials"]
+markers = "platform_python_implementation == \"CPython\""
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+]
+
 [[package]]
 name = "click"
 version = "8.1.8"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.7"
-groups = ["dev"]
+groups = ["dev", "tutorials"]
 files = [
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@@ -68,7 +440,7 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
-groups = ["dev"]
+groups = ["dev", "tutorials"]
 markers = "platform_system == \"Windows\""
 files = [
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@@ -92,6 +464,77 @@ mccabe = ">=0.7.0,<0.8.0"
 pycodestyle = ">=2.12.0,<2.13.0"
 pyflakes = ">=3.2.0,<3.3.0"
 
+[[package]]
+name = "idna"
+version = "3.10"
+description = "Internationalized Domain Names in Applications (IDNA)"
+optional = false
+python-versions = ">=3.6"
+groups = ["tutorials"]
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+]
+
+[package.extras]
+all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
+
+[[package]]
+name = "inflate64"
+version = "1.0.1"
+description = "deflate64 compression/decompression library"
+optional = false
+python-versions = ">=3.9"
+groups = ["tutorials"]
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+    {file = "inflate64-1.0.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:91c0c1d41c1655fb0189630baaa894a3b778d77062bb90ca11db878422948395"},
+    {file = "inflate64-1.0.1.tar.gz", hash = "sha256:3b1c83c22651b5942b35829df526e89602e494192bf021e0d7d0b600e76c429d"},
+]
+
+[package.extras]
+check = ["check-manifest", "flake8", "flake8-black", "flake8-deprecated", "flake8-isort", "mypy (>=1.10.0)", "mypy_extensions (>=0.4.1)", "pygments", "readme-renderer", "twine"]
+docs = ["docutils", "sphinx (>=5.0)"]
+test = ["pytest"]
+
 [[package]]
 name = "isort"
 version = "6.0.0"
@@ -120,6 +563,23 @@ files = [
     {file = "mccabe-0.7.0.tar.gz", hash = "sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325"},
 ]
 
+[[package]]
+name = "multivolumefile"
+version = "0.2.3"
+description = "multi volume file wrapper library"
+optional = false
+python-versions = ">=3.6"
+groups = ["tutorials"]
+files = [
+    {file = "multivolumefile-0.2.3-py3-none-any.whl", hash = "sha256:237f4353b60af1703087cf7725755a1f6fcaeeea48421e1896940cd1c920d678"},
+    {file = "multivolumefile-0.2.3.tar.gz", hash = "sha256:a0648d0aafbc96e59198d5c17e9acad7eb531abea51035d08ce8060dcad709d6"},
+]
+
+[package.extras]
+check = ["check-manifest", "flake8", "flake8-black", "isort (>=5.0.3)", "pygments", "readme-renderer", "twine"]
+test = ["coverage[toml] (>=5.2)", "coveralls (>=2.1.1)", "hypothesis", "pyannotate", "pytest", "pytest-cov"]
+type = ["mypy", "mypy-extensions"]
+
 [[package]]
 name = "mypy-extensions"
 version = "1.0.0"
@@ -241,6 +701,31 @@ docs = ["furo (>=2024.8.6)", "proselint (>=0.14)", "sphinx (>=8.0.2)", "sphinx-a
 test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=8.3.2)", "pytest-cov (>=5)", "pytest-mock (>=3.14)"]
 type = ["mypy (>=1.11.2)"]
 
+[[package]]
+name = "psutil"
+version = "7.0.0"
+description = "Cross-platform lib for process and system monitoring in Python.  NOTE: the syntax of this script MUST be kept compatible with Python 2.7."
+optional = false
+python-versions = ">=3.6"
+groups = ["tutorials"]
+markers = "sys_platform != \"cygwin\""
+files = [
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 version = "3.5.4"
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+]
+
+[[package]]
+name = "requests"
+version = "2.32.3"
+description = "Python HTTP for Humans."
+optional = false
+python-versions = ">=3.8"
+groups = ["tutorials"]
+files = [
+    {file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"},
+    {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"},
+]
+
+[package.dependencies]
+certifi = ">=2017.4.17"
+charset-normalizer = ">=2,<4"
+idna = ">=2.5,<4"
+urllib3 = ">=1.21.1,<3"
+
+[package.extras]
+socks = ["PySocks (>=1.5.6,!=1.5.7)"]
+use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
+
+[[package]]
+name = "texttable"
+version = "1.7.0"
+description = "module to create simple ASCII tables"
+optional = false
+python-versions = "*"
+groups = ["tutorials"]
+files = [
+    {file = "texttable-1.7.0-py2.py3-none-any.whl", hash = "sha256:72227d592c82b3d7f672731ae73e4d1f88cd8e2ef5b075a7a7f01a23a3743917"},
+    {file = "texttable-1.7.0.tar.gz", hash = "sha256:2d2068fb55115807d3ac77a4ca68fa48803e84ebb0ee2340f858107a36522638"},
+]
+
 [[package]]
 name = "tomli"
 version = "2.2.1"
@@ -354,7 +1178,25 @@ files = [
     {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]
 
+[[package]]
+name = "urllib3"
+version = "2.3.0"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.9"
+groups = ["tutorials"]
+files = [
+    {file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
+    {file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""]
+h2 = ["h2 (>=4,<5)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.9 <3.13"
-content-hash = "52c129fee3e94e69edf727f219bc7582ddbfcedf6c43547a7f67a876051bd7c4"
+content-hash = "33ae7f96a3999d6822af7778f9b7878355d811534a4b5fec14d51ec29aa8dce2"
diff --git a/python/pyproject.toml b/python/pyproject.toml
index 8c0c1ba05..819d2bbdd 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -34,6 +34,14 @@ black = "^25.1.0"
 flake8 = "^7.1.1"
 isort = "^6.0.0"
 
+[tool.poetry.group.tutorials.dependencies]
+py7zr = "^0.22.0"
+requests = "^2.32.3"
+click = "^8.1.8"
+
+[tool.poetry.scripts]
+graphframes = "graphframes.console:main"
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
@@ -43,6 +51,9 @@ line-length = 100
 target-version = ["py39"]
 include = ["graphframes"]
 
+[tool.flake8]
+max-line-length = 100
+
 [tool.isort]
 profile = "black"
 src_paths = ["graphframes"]