feat: add pet-project and gitignore

.gitignore

@@ -0,0 +1,3 @@
+__pycahe__
+.venv
+.vscode

pet-project/create_dm_ch.py

@@ -0,0 +1,132 @@
+import os
+from pathlib import Path
+
+from pyspark.sql import DataFrame, SparkSession
+from pyspark.sql import functions as F
+
+spark = SparkSession.builder.appName("create_dm_gp").master("local[*]").getOrCreate()
+
+
+def read_csv(csv_filename: str, path: Path = Path("src/ch")) -> DataFrame:
+    return spark.read.csv(
+        path=str(path / csv_filename),
+        header=True,
+        inferSchema=True,
+    )
+
+
+core_dim_user = read_csv("core_dim_user.csv")
+core_dim_course = read_csv("core_dim_course.csv")
+core_dim_lesson = read_csv("core_dim_lesson.csv")
+core_fact_enrollments = read_csv("core_fact_enrollments.csv")
+core_fact_lesson_views = read_csv("core_fact_lesson_views.csv")
+
+
+def lesson_popularity_summary() -> DataFrame:
+    result = (
+        core_dim_lesson
+        .alias("l")
+        .join(
+            core_dim_course.alias("c"),
+            F.col("c.course_id") == F.col("l.course_id"),
+            how="inner",
+        )
+        .join(
+            core_fact_lesson_views.alias("lv"),
+            F.col("l.lesson_id") == F.col("lv.lesson_id"),
+            how="left",
+        )
+        .groupBy(["l.lesson_id", "l.title", "c.title", "l.course_id"])
+        .agg(
+            F.count("lv.lesson_id").alias("total_views"),
+            F.count_distinct("lv.user_id").alias("unique_users"),
+            F.min("lv.viewed_at").alias("first_view"),
+            F.max("lv.viewed_at").alias("last_view"),
+        )
+        .select(
+            "lesson_id",
+            F.col("l.title").alias("lesson_title"),
+            "course_id",
+            F.col("c.title").alias("course_title"),
+            "total_views",
+            "unique_users",
+            "first_view",
+            "last_view",
+        )
+    )
+
+    return result
+
+
+def inactive_users_summary() -> DataFrame:
+    active_users = core_fact_lesson_views.select("user_id").distinct()
+    inactive_users = core_dim_user.join(active_users, on="user_id", how="left_anti")
+
+    registered_courses_count = core_fact_enrollments.groupBy("user_id").agg(
+        F.count_distinct("course_id").alias("registered_courses_count")
+    )
+
+    result = inactive_users.join(
+        registered_courses_count, on="user_id", how="left"
+    ).select(
+        "user_id",
+        "name",
+        "email",
+        "age",
+        F.coalesce("registered_courses_count", F.lit(0)).alias(
+            "registered_courses_count"
+        ),
+        "registration_date",
+    )
+
+    return result
+
+
+def course_completion_rate() -> DataFrame:
+    course_stats = core_dim_lesson.groupBy("course_id").agg(
+        F.count("lesson_id").alias("lessons_in_course")
+    )
+
+    view_stats = core_fact_lesson_views.groupBy("user_id", "course_id").agg(
+        F.count_distinct("lesson_id").alias("lessons_viewed")
+    )
+
+    result = (
+        core_dim_user
+        .alias("u")
+        .join(
+            view_stats.alias("vs"),
+            F.col("u.user_id") == F.col("vs.user_id"),
+            how="left",
+        )
+        .join(core_dim_course.alias("c"), on="course_id", how="inner")
+        .join(course_stats.alias("cs"), on="course_id", how="left")
+        .select(
+            "u.user_id",
+            F.col("u.name").alias("user_name"),
+            "c.course_id",
+            F.col("c.title").alias("course_title"),
+            "lessons_in_course",
+            "lessons_viewed",
+            F.round(
+                F.coalesce("lessons_viewed", F.lit(0)) / F.col("lessons_in_course"),
+                2,
+            ).alias("completion_rate"),
+        )
+    )
+
+    return result
+
+
+output_dir = Path("marts/ch")
+os.makedirs(output_dir, exist_ok=True)
+
+lesson_popularity_summary().write.mode("overwrite").option("header", "true").csv(
+    str(output_dir / "lesson_popularity_summary")
+)
+inactive_users_summary().write.mode("overwrite").option("header", "true").csv(
+    str(output_dir / "inactive_users_summary")
+)
+course_completion_rate().write.mode("overwrite").option("header", "true").csv(
+    str(output_dir / "course_completion_rate")
+)

pet-project/create_dm_gp.py

@@ -0,0 +1,163 @@
+import os
+from pathlib import Path
+
+import pyspark.sql.functions as F
+from pyspark.sql import DataFrame, SparkSession
+
+spark = SparkSession.builder.appName("create_dm_pg").master("local[*]").getOrCreate()
+
+
+def read_csv(csv_filename: str, path: Path = Path("src/gp")) -> DataFrame:
+    return spark.read.csv(
+        path=str(path / csv_filename),
+        header=True,
+        inferSchema=True,
+    )
+
+
+users = read_csv("users.csv").withColumnRenamed("id", "user_id")
+courses = read_csv("courses.csv").withColumnRenamed("id", "course_id")
+lessons = read_csv("lessons.csv").withColumnRenamed("id", "lesson_id")
+enrollments = read_csv("enrollments.csv")
+lesson_views = read_csv("lesson_views.csv")
+
+
+def user_activity_summary() -> DataFrame:
+    enrollment_stats = enrollments.groupBy("user_id").agg(
+        F.count("course_id").alias("enrollment_count")
+    )
+
+    view_stats = lesson_views.groupBy("user_id").agg(
+        F.count_distinct("lesson_id").alias("viewed_lessons_count"),
+        F.min("viewed_at").alias("first_view"),
+        F.max("viewed_at").alias("last_view"),
+    )
+
+    possible_lessons = (
+        enrollments
+        .join(lessons, on="course_id", how="inner")
+        .groupBy("user_id")
+        .agg(F.count_distinct("lesson_id").alias("possible_lessons"))
+    )
+
+    result = (
+        users
+        .join(enrollment_stats, on="user_id", how="inner")
+        .join(view_stats, on="user_id", how="left")
+        .join(possible_lessons, on="user_id", how="left")
+    )
+
+    result = result.withColumn(
+        "engagement_rate",
+        F.concat(
+            F.round(
+                F.coalesce(F.col("viewed_lessons_count"), F.lit(0))
+                * 100.0
+                / F.col("possible_lessons"),
+                2,
+            ),
+            F.lit("%"),
+        ),
+    )
+
+    result = result.select(
+        "user_id",
+        "name",
+        "enrollment_count",
+        "viewed_lessons_count",
+        "first_view",
+        "last_view",
+        "engagement_rate",
+    )
+
+    return result
+
+
+def course_summary() -> DataFrame:
+    enrollment_stats = enrollments.groupBy("course_id").agg(
+        F.count_distinct("user_id").alias("unique_users")
+    )
+
+    view_stats = (
+        lessons
+        .alias("l")
+        .join(lesson_views.alias("lv"), on="lesson_id", how="left")
+        .groupBy("course_id")
+        .agg(
+            F.count_distinct("l.lesson_id").alias("lessons_count"),
+            F.count("lv.lesson_id").alias("total_views"),
+            F.min("lv.viewed_at").alias("first_view"),
+            F.max("lv.viewed_at").alias("last_view"),
+        )
+    )
+
+    result = courses.join(enrollment_stats, on="course_id", how="inner").join(
+        view_stats, on="course_id", how="inner"
+    )
+
+    result = result.withColumn(
+        "avg_views_per_user",
+        F.round(
+            F.coalesce(F.col("total_views"), F.lit(0)) / F.col("unique_users"),
+            2,
+        ),
+    ).select(
+        "course_id",
+        "title",
+        "unique_users",
+        "lessons_count",
+        "total_views",
+        "avg_views_per_user",
+        "first_view",
+        "last_view",
+    )
+
+    return result
+
+
+def platform_summary() -> DataFrame:
+    total_users = users.agg(F.count("*").alias("total_users"))
+    total_courses = courses.agg(F.count("*").alias("total_courses"))
+    total_lessons = lessons.agg(F.count("*").alias("total_lessons"))
+    users_with_views = lesson_views.agg(
+        F.count_distinct("user_id").alias("users_with_views")
+    )
+
+    lessons_per_course = lessons.groupBy("course_id").agg(
+        F.count_distinct("lesson_id").alias("lessons_count")
+    )
+    avg_lessons_per_course = lessons_per_course.agg(
+        F.round(F.avg("lessons_count"), 2).alias("avg_lessons_per_course")
+    )
+
+    views_per_lesson = lesson_views.groupBy("lesson_id").agg(
+        F.count("*").alias("total_views")
+    )
+    avg_views_per_lesson = views_per_lesson.agg(
+        F.round(F.avg("total_views"), 2).alias("avg_views_per_lesson")
+    )
+
+    result = (
+        total_users
+        .crossJoin(total_courses)
+        .crossJoin(total_lessons)
+        .crossJoin(users_with_views)
+        .crossJoin(avg_lessons_per_course)
+        .crossJoin(avg_views_per_lesson)
+    )
+
+    return result
+
+
+output_dir = Path("marts/gp")
+os.makedirs(output_dir, exist_ok=True)
+
+user_activity_summary().write.mode("overwrite").option("header", "true").csv(
+    str(output_dir / "user_activity_summary")
+)
+course_summary().write.mode("overwrite").option("header", "true").csv(
+    str(output_dir / "course_summary")
+)
+platform_summary().write.mode("overwrite").option("header", "true").csv(
+    str(output_dir / "platform_summary")
+)

pet-project/create_dm_pg.py

@@ -0,0 +1,163 @@
+import os
+from pathlib import Path
+
+import pyspark.sql.functions as F
+from pyspark.sql import DataFrame, SparkSession
+
+spark = SparkSession.builder.appName("create_dm_pg").master("local[*]").getOrCreate()
+
+
+def read_csv(csv_filename: str, path: Path = Path("src/pg")) -> DataFrame:
+    return spark.read.csv(
+        path=str(path / csv_filename),
+        header=True,
+        inferSchema=True,
+    )
+
+
+users = read_csv("users.csv").withColumnRenamed("id", "user_id")
+courses = read_csv("courses.csv").withColumnRenamed("id", "course_id")
+lessons = read_csv("lessons.csv").withColumnRenamed("id", "lesson_id")
+enrollments = read_csv("enrollments.csv")
+lesson_views = read_csv("lesson_views.csv")
+
+
+def user_activity_summary() -> DataFrame:
+    enrollment_stats = enrollments.groupBy("user_id").agg(
+        F.count("course_id").alias("enrollment_count")
+    )
+
+    view_stats = lesson_views.groupBy("user_id").agg(
+        F.count_distinct("lesson_id").alias("viewed_lessons_count"),
+        F.min("viewed_at").alias("first_view"),
+        F.max("viewed_at").alias("last_view"),
+    )
+
+    possible_lessons = (
+        enrollments
+        .join(lessons, on="course_id", how="inner")
+        .groupBy("user_id")
+        .agg(F.count_distinct("lesson_id").alias("possible_lessons"))
+    )
+
+    result = (
+        users
+        .join(enrollment_stats, on="user_id", how="inner")
+        .join(view_stats, on="user_id", how="left")
+        .join(possible_lessons, on="user_id", how="left")
+    )
+
+    result = result.withColumn(
+        "engagement_rate",
+        F.concat(
+            F.round(
+                F.coalesce(F.col("viewed_lessons_count"), F.lit(0))
+                * 100.0
+                / F.col("possible_lessons"),
+                2,
+            ),
+            F.lit("%"),
+        ),
+    )
+
+    result = result.select(
+        "user_id",
+        "name",
+        "enrollment_count",
+        "viewed_lessons_count",
+        "first_view",
+        "last_view",
+        "engagement_rate",
+    )
+
+    return result
+
+
+def course_summary() -> DataFrame:
+    enrollment_stats = enrollments.groupBy("course_id").agg(
+        F.count_distinct("user_id").alias("unique_users")
+    )
+
+    view_stats = (
+        lessons
+        .alias("l")
+        .join(lesson_views.alias("lv"), on="lesson_id", how="left")
+        .groupBy("course_id")
+        .agg(
+            F.count_distinct("l.lesson_id").alias("lessons_count"),
+            F.count("lv.lesson_id").alias("total_views"),
+            F.min("lv.viewed_at").alias("first_view"),
+            F.max("lv.viewed_at").alias("last_view"),
+        )
+    )
+
+    result = courses.join(enrollment_stats, on="course_id", how="inner").join(
+        view_stats, on="course_id", how="inner"
+    )
+
+    result = result.withColumn(
+        "avg_views_per_user",
+        F.round(
+            F.coalesce(F.col("total_views"), F.lit(0)) / F.col("unique_users"),
+            2,
+        ),
+    ).select(
+        "course_id",
+        "title",
+        "unique_users",
+        "lessons_count",
+        "total_views",
+        "avg_views_per_user",
+        "first_view",
+        "last_view",
+    )
+
+    return result
+
+
+def platform_summary() -> DataFrame:
+    total_users = users.agg(F.count("*").alias("total_users"))
+    total_courses = courses.agg(F.count("*").alias("total_courses"))
+    total_lessons = lessons.agg(F.count("*").alias("total_lessons"))
+    users_with_views = lesson_views.agg(
+        F.count_distinct("user_id").alias("users_with_views")
+    )
+
+    lessons_per_course = lessons.groupBy("course_id").agg(
+        F.count_distinct("lesson_id").alias("lessons_count")
+    )
+    avg_lessons_per_course = lessons_per_course.agg(
+        F.round(F.avg("lessons_count"), 2).alias("avg_lessons_per_course")
+    )
+
+    views_per_lesson = lesson_views.groupBy("lesson_id").agg(
+        F.count("*").alias("total_views")
+    )
+    avg_views_per_lesson = views_per_lesson.agg(
+        F.round(F.avg("total_views"), 2).alias("avg_views_per_lesson")
+    )
+
+    result = (
+        total_users
+        .crossJoin(total_courses)
+        .crossJoin(total_lessons)
+        .crossJoin(users_with_views)
+        .crossJoin(avg_lessons_per_course)
+        .crossJoin(avg_views_per_lesson)
+    )
+
+    return result
+
+
+output_dir = Path("marts/pg")
+os.makedirs(output_dir, exist_ok=True)
+
+user_activity_summary().write.mode("overwrite").option("header", "true").csv(
+    str(output_dir / "user_activity_summary")
+)
+course_summary().write.mode("overwrite").option("header", "true").csv(
+    str(output_dir / "course_summary")
+)
+platform_summary().write.mode("overwrite").option("header", "true").csv(
+    str(output_dir / "platform_summary")
+)

pet-project/src/ch/core_dim_course.csv

@@ -0,0 +1,6 @@
+course_id,title,category,created_at
+5,Основы машинного обучения,data,2023-03-15
+1,SQL для начинающих,data,2023-01-01
+2,Python для анализа данных,programming,2023-01-15
+3,BI с нуля,business,2023-03-01
+4,Excel для аналитиков,business,2023-02-01

pet-project/src/ch/core_dim_lesson.csv

@@ -0,0 +1,19 @@
+lesson_id,title,duration_min,course_id
+1,SELECT и FROM,10,1
+2,JOIN,15,1
+3,WHERE и фильтрация,12,1
+4,Агрегации и GROUP BY,18,1
+5,Подзапросы,25,1
+6,Pandas основы,20,2
+7,NumPy,15,2
+9,Основы BI,12,3
+10,Метрики и дашборды,18,3
+11,Power BI введение,20,3
+12,Формулы в Excel,15,4
+13,Сводные таблицы,18,4
+14,Графики и диаграммы,14,4
+15,Введение в ML,20,5
+16,Линейная регрессия,30,5
+17,Классификация,28,5
+18,Кластеризация,25,5
+8,Визуализация данных,22,2

pet-project/src/ch/core_dim_user.csv

@@ -0,0 +1,21 @@
+user_id,name,age,email,registration_date
+20,Tina,25,tina@mail.com,2023-04-18
+1,Alice,25,alice@mail.com,2023-01-10
+2,Bob,30,bob@gmail.com,2023-02-05
+3,Charlie,22,charlie@mail.com,2023-02-20
+4,Diana,28,diana@mail.com,2023-03-01
+5,Ethan,35,ethan@gmail.com,2023-03-10
+6,Frank,29,frank@mail.com,2023-01-05
+7,Grace,26,grace@gmail.com,2023-01-12
+8,Henry,32,henry@mail.com,2023-01-20
+9,Iris,24,iris@gmail.com,2023-02-10
+10,Jack,27,jack@mail.com,2023-04-15
+11,Kelly,23,kelly@gmail.com,2023-04-20
+12,Leo,31,leo@mail.com,2023-01-15
+13,Mia,26,mia@gmail.com,2023-01-18
+14,Nina,28,nina@mail.com,2023-01-08
+15,Oscar,33,oscar@gmail.com,2023-01-10
+16,Paul,29,paul@mail.com,2023-02-15
+17,Quinn,24,quinn@gmail.com,2023-03-20
+18,Rita,27,rita@mail.com,2023-04-01
+19,Sam,30,sam@gmail.com,2023-04-10

pet-project/src/ch/core_fact_enrollments.csv

@@ -0,0 +1,46 @@
+user_id,course_id,enrolled_at
+1,1,2023-01-15 00:00:00.000
+1,2,2023-02-01 00:00:00.000
+1,4,2023-02-10 00:00:00.000
+2,1,2023-01-20 00:00:00.000
+2,3,2023-03-15 00:00:00.000
+3,1,2023-02-25 00:00:00.000
+3,2,2023-03-05 00:00:00.000
+4,2,2023-03-10 00:00:00.000
+4,3,2023-04-01 00:00:00.000
+5,5,2023-03-20 00:00:00.000
+6,1,2023-01-06 00:00:00.000
+6,2,2023-01-10 00:00:00.000
+6,3,2023-03-08 00:00:00.000
+6,4,2023-02-05 00:00:00.000
+7,1,2023-01-13 00:00:00.000
+7,2,2023-02-01 00:00:00.000
+7,3,2023-03-10 00:00:00.000
+8,1,2023-01-22 00:00:00.000
+8,2,2023-01-22 00:00:00.000
+8,3,2023-01-22 00:00:00.000
+9,1,2023-02-15 00:00:00.000
+9,4,2023-02-12 00:00:00.000
+10,1,2023-04-16 00:00:00.000
+11,2,2023-04-21 00:00:00.000
+12,1,2023-01-16 00:00:00.000
+12,3,2023-03-10 00:00:00.000
+13,2,2023-01-19 00:00:00.000
+13,4,2023-02-20 00:00:00.000
+14,1,2023-01-09 00:00:00.000
+14,2,2023-01-09 00:00:00.000
+14,3,2023-01-09 00:00:00.000
+14,4,2023-02-01 00:00:00.000
+14,5,2023-03-20 00:00:00.000
+15,1,2023-01-11 00:00:00.000
+15,3,2023-03-05 00:00:00.000
+16,2,2023-02-16 00:00:00.000
+16,4,2023-02-16 00:00:00.000
+17,3,2023-03-22 00:00:00.000
+17,5,2023-03-25 00:00:00.000
+18,1,2023-04-02 00:00:00.000
+18,4,2023-04-05 00:00:00.000
+19,1,2023-04-11 00:00:00.000
+19,2,2023-04-11 00:00:00.000
+20,2,2023-04-19 00:00:00.000
+15,2,2023-02-01 00:00:00.000

pet-project/src/ch/core_fact_lesson_views.csv

@@ -0,0 +1,58 @@
+user_id,lesson_id,course_id,viewed_at
+13,12,4,2023-02-21 09:00:00.000
+1,1,1,2023-01-16 10:00:00.000
+1,2,1,2023-01-16 10:15:00.000
+1,3,1,2023-01-17 09:00:00.000
+1,4,1,2023-01-17 09:30:00.000
+1,4,1,2023-01-17 11:00:00.000
+1,4,1,2023-01-18 10:00:00.000
+1,6,2,2023-02-02 14:00:00.000
+1,12,4,2023-02-15 10:00:00.000
+1,13,4,2023-02-15 10:30:00.000
+2,1,1,2023-01-21 09:00:00.000
+2,9,3,2023-03-16 10:00:00.000
+2,10,3,2023-03-16 10:30:00.000
+3,1,1,2023-02-26 11:00:00.000
+3,2,1,2023-02-26 11:20:00.000
+3,6,2,2023-03-06 12:00:00.000
+4,6,2,2023-03-12 14:00:00.000
+4,7,2,2023-03-13 09:00:00.000
+4,9,3,2023-04-02 10:00:00.000
+4,10,3,2023-04-03 10:00:00.000
+5,15,5,2023-03-21 10:00:00.000
+5,16,5,2023-03-22 11:00:00.000
+6,1,1,2023-01-07 10:00:00.000
+6,2,1,2023-01-07 10:15:00.000
+6,3,1,2023-01-08 09:00:00.000
+6,4,1,2023-01-08 09:30:00.000
+6,5,1,2023-01-09 10:00:00.000
+6,6,2,2023-01-11 14:00:00.000
+6,7,2,2023-01-12 10:00:00.000
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pet-project/src/gp/courses.csv

@@ -0,0 +1,6 @@
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+2,Python для анализа данных,programming,2023-01-15
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pet-project/src/gp/enrollments.csv

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pet-project/src/gp/lesson_views.csv

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pet-project/src/gp/lessons.csv

@@ -0,0 +1,19 @@
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pet-project/src/gp/users.csv

@@ -0,0 +1,21 @@
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+5,Ethan,35,ethan@gmail.com,2023-03-10
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pet-project/src/pg/courses.csv

@@ -0,0 +1,6 @@
+id,title,category,created_at
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+2,Python для анализа данных,programming,2023-01-15
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pet-project/src/pg/enrollments.csv

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pet-project/src/pg/lesson_views.csv

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pet-project/src/pg/lessons.csv

@@ -0,0 +1,19 @@
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pet-project/src/pg/users.csv

@@ -0,0 +1,21 @@
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+2,Bob,30,bob@gmail.com,2023-02-05
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