{"id":48446,"date":"2025-11-01T07:47:26","date_gmt":"2025-11-01T07:47:26","guid":{"rendered":"https:\/\/youzum.net\/how-to-build-an-end-to-end-data-engineering-and-machine-learning-pipeline-with-apache-spark-and-pyspark\/"},"modified":"2025-11-01T07:47:26","modified_gmt":"2025-11-01T07:47:26","slug":"how-to-build-an-end-to-end-data-engineering-and-machine-learning-pipeline-with-apache-spark-and-pyspark","status":"publish","type":"post","link":"https:\/\/youzum.net\/de\/how-to-build-an-end-to-end-data-engineering-and-machine-learning-pipeline-with-apache-spark-and-pyspark\/","title":{"rendered":"How to Build an End-to-End Data Engineering and Machine Learning Pipeline with Apache Spark and PySpark"},"content":{"rendered":"

In this tutorial, we explore how to harness Apache Spark\u2019s techniques using PySpark directly in Google Colab. We begin by setting up a local Spark session, then progressively move through transformations, SQL queries, joins, and window functions. We also build and evaluate a simple machine-learning model to predict user subscription types and finally demonstrate how to save and reload Parquet files. Also, we experience how Spark\u2019s distributed data-processing capabilities can be leveraged for analytics and ML<\/a> workflows even in a single-node Colab environment. Check out the\u00a0FULL CODES here<\/a><\/strong>.<\/p>\n

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!pip install -q pyspark==3.5.1\nfrom pyspark.sql import SparkSession, functions as F, Window\nfrom pyspark.sql.types import IntegerType, StringType, StructType, StructField, FloatType\nfrom pyspark.ml.feature import StringIndexer, VectorAssembler\nfrom pyspark.ml.classification import LogisticRegression\nfrom pyspark.ml.evaluation import MulticlassClassificationEvaluator\n\n\nspark = (SparkSession.builder.appName(\"ColabSparkAdvancedTutorial\")\n        .master(\"local[*]\")\n        .config(\"spark.sql.shuffle.partitions\", \"4\")\n        .getOrCreate())\nprint(\"Spark version:\", spark.version)\n\n\ndata = [\n   (1, \"Alice\", \"IN\", \"2025-10-01\", 56000.0, \"premium\"),\n   (2, \"Bob\", \"US\", \"2025-10-03\", 43000.0, \"standard\"),\n   (3, \"Carlos\", \"IN\", \"2025-09-27\", 72000.0, \"premium\"),\n   (4, \"Diana\", \"UK\", \"2025-09-30\", 39000.0, \"standard\"),\n   (5, \"Esha\", \"IN\", \"2025-10-02\", 85000.0, \"premium\"),\n   (6, \"Farid\", \"AE\", \"2025-10-02\", 31000.0, \"basic\"),\n   (7, \"Gita\", \"IN\", \"2025-09-29\", 46000.0, \"standard\"),\n   (8, \"Hassan\", \"PK\", \"2025-10-01\", 52000.0, \"premium\"),\n]\nschema = StructType([\n   StructField(\"id\", IntegerType(), False),\n   StructField(\"name\", StringType(), True),\n   StructField(\"country\", StringType(), True),\n   StructField(\"signup_date\", StringType(), True),\n   StructField(\"income\", FloatType(), True),\n   StructField(\"plan\", StringType(), True),\n])\ndf = spark.createDataFrame(data, schema)\ndf.show()<\/code><\/pre>\n<\/div>\n<\/div>\n
We begin by setting up PySpark, initializing the Spark session, and preparing our dataset. We create a structured DataFrame containing user information, including country, income, and plan type. This forms the foundation for all transformations and analyses that follow. Check out the\u00a0FULL CODES here<\/a><\/strong>.<\/p>\n
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df2 = (df.withColumn(\"signup_ts\", F.to_timestamp(\"signup_date\"))\n        .withColumn(\"year\", F.year(\"signup_ts\"))\n        .withColumn(\"month\", F.month(\"signup_ts\"))\n        .withColumn(\"is_india\", (F.col(\"country\") == \"IN\").cast(\"int\")))\ndf2.show()\n\n\ndf2.createOrReplaceTempView(\"users\")\nspark.sql(\"\"\"\nSELECT country, COUNT(*) AS cnt, AVG(income) AS avg_income\nFROM users\nGROUP BY country\nORDER BY cnt DESC\n\"\"\").show()\n\n\nw = Window.partitionBy(\"country\").orderBy(F.col(\"income\").desc())\ndf_ranked = df2.withColumn(\"income_rank_in_country\", F.rank().over(w))\ndf_ranked.show()\n\n\ndef plan_priority(plan):\n   if plan == \"premium\": return 3\n   if plan == \"standard\": return 2\n   if plan == \"basic\": return 1\n   return 0\nplan_priority_udf = F.udf(plan_priority, IntegerType())\ndf_udf = df_ranked.withColumn(\"plan_priority\", plan_priority_udf(F.col(\"plan\")))\ndf_udf.show()<\/code><\/pre>\n<\/div>\n<\/div>\n
We now perform various data transformations, add new columns, and register the DataFrame as a SQL table. We explore Spark SQL for aggregation and apply window functions to rank users by income. We also introduce a user-defined function (UDF) to assign priority levels to subscription plans. Check out the\u00a0FULL CODES here<\/a><\/strong>.<\/p>\n
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country_data = [\n   (\"IN\", \"Asia\", 1.42), (\"US\", \"North America\", 0.33),\n   (\"UK\", \"Europe\", 0.07), (\"AE\", \"Asia\", 0.01), (\"PK\", \"Asia\", 0.24),\n]\ncountry_schema = StructType([\n   StructField(\"country\", StringType(), True),\n   StructField(\"region\", StringType(), True),\n   StructField(\"population_bn\", FloatType(), True),\n])\ncountry_df = spark.createDataFrame(country_data, country_schema)\n\n\njoined = df_udf.alias(\"u\").join(country_df.alias(\"c\"), on=\"country\", how=\"left\")\njoined.show()\n\n\nregion_stats = (joined.groupBy(\"region\", \"plan\")\n               .agg(F.count(\"*\").alias(\"users\"),\n                    F.round(F.avg(\"income\"), 2).alias(\"avg_income\"))\n               .orderBy(\"region\", \"plan\"))\nregion_stats.show()<\/code><\/pre>\n<\/div>\n<\/div>\n
We enrich our user dataset by joining it with country-level metadata that includes region and population. We then compute analytical summaries such as average income and user counts by region and plan type. This step demonstrates how Spark simplifies the seamless combination and aggregation of large datasets. Check out the\u00a0FULL CODES here<\/a><\/strong>.<\/p>\n
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ml_df = joined.withColumn(\"label\", (F.col(\"plan\") == \"premium\").cast(\"int\")).na.drop()\ncountry_indexer = StringIndexer(inputCol=\"country\", outputCol=\"country_idx\", handleInvalid=\"keep\")\ncountry_fitted = country_indexer.fit(ml_df)\nml_df2 = country_fitted.transform(ml_df)\n\n\nassembler = VectorAssembler(inputCols=[\"income\", \"country_idx\", \"plan_priority\"], outputCol=\"features\")\nml_final = assembler.transform(ml_df2)\ntrain_df, test_df = ml_final.randomSplit([0.7, 0.3], seed=42)\n\n\nlr = LogisticRegression(featuresCol=\"features\", labelCol=\"label\", maxIter=20)\nlr_model = lr.fit(train_df)\npreds = lr_model.transform(test_df)\npreds.select(\"name\", \"country\", \"income\", \"plan\", \"label\", \"prediction\", \"probability\").show(truncate=False)\n\n\nevaluator = MulticlassClassificationEvaluator(labelCol=\"label\", predictionCol=\"prediction\", metricName=\"accuracy\")\nacc = evaluator.evaluate(preds)\nprint(\"Classification accuracy:\", acc)<\/code><\/pre>\n<\/div>\n<\/div>\n
We move into machine learning by preparing data for model training and feature engineering. We index categorical columns, assemble features, and train a logistic regression model to predict premium users. We then evaluate its accuracy, showcasing how Spark MLlib integrates easily into the data workflow. Check out the\u00a0FULL CODES here<\/a><\/strong>.<\/p>\n
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output_path = \"\/content\/spark_users_parquet\"\njoined.write.mode(\"overwrite\").parquet(output_path)\nparquet_df = spark.read.parquet(output_path)\nprint(\"Parquet reloaded:\")\nparquet_df.show()\n\n\nrecent = spark.sql(\"\"\"\nSELECT name, country, income, signup_ts\nFROM users\nWHERE signup_ts >= '2025-10-01'\nORDER BY signup_ts DESC\n\"\"\")\nrecent.show()\n\n\nrecent.explain()\nspark.stop()<\/code><\/pre>\n<\/div>\n<\/div>\n
We conclude by writing the processed data to Parquet format and reading it back into Spark for verification. We run a SQL query to extract recent signups and inspect the query plan for optimization insights. Finally, we gracefully stop the Spark session to complete our workflow.<\/p>\n
In conclusion, we gain a practical understanding of how PySpark unifies data engineering and machine learning tasks within a single scalable framework. We witness how simple DataFrame transformations evolve into SQL analytics, feature engineering, and predictive modeling, all while staying within Google Colab. By experimenting with these concepts, we strengthen our ability to prototype and deploy Spark-based data solutions efficiently in both local and distributed setups.<\/p>\n
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Check out the\u00a0FULL CODES here<\/a><\/strong>. Feel free to check out our\u00a0GitHub Page for Tutorials, Codes and Notebooks<\/a><\/mark><\/strong>.\u00a0Also,\u00a0feel free to follow us on\u00a0Twitter<\/mark><\/a><\/strong>\u00a0and don\u2019t forget to join our\u00a0100k+ ML SubReddit<\/a><\/strong>\u00a0and Subscribe to\u00a0our Newsletter<\/a><\/strong>. Wait! are you on telegram?\u00a0now you can join us on telegram as well.<\/a><\/strong><\/p>\n
The post How to Build an End-to-End Data Engineering and Machine Learning Pipeline with Apache Spark and PySpark<\/a> appeared first on MarkTechPost<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"
In this tutorial, we explore how to harness Apache Spark\u2019s techniques using PySpark directly in Google Colab. We begin by setting up a local Spark session, then progressively move through transformations, SQL queries, joins, and window functions. We also build and evaluate a simple machine-learning model to predict user subscription types and finally demonstrate how to save and reload Parquet files. Also, we experience how Spark\u2019s distributed data-processing capabilities can be leveraged for analytics and ML workflows even in a single-node Colab environment. Check out the\u00a0FULL CODES here. Copy CodeCopiedUse a different Browser !pip install -q pyspark==3.5.1 from pyspark.sql import SparkSession, functions as F, Window from pyspark.sql.types import IntegerType, StringType, StructType, StructField, FloatType from pyspark.ml.feature import StringIndexer, VectorAssembler from pyspark.ml.classification import LogisticRegression from pyspark.ml.evaluation import MulticlassClassificationEvaluator spark = (SparkSession.builder.appName(“ColabSparkAdvancedTutorial”) .master(“local[*]”) .config(“spark.sql.shuffle.partitions”, “4”) .getOrCreate()) print(“Spark version:”, spark.version) data = [ (1, “Alice”, “IN”, “2025-10-01”, 56000.0, “premium”), (2, “Bob”, “US”, “2025-10-03”, 43000.0, “standard”), (3, “Carlos”, “IN”, “2025-09-27”, 72000.0, “premium”), (4, “Diana”, “UK”, “2025-09-30”, 39000.0, “standard”), (5, “Esha”, “IN”, “2025-10-02”, 85000.0, “premium”), (6, “Farid”, “AE”, “2025-10-02”, 31000.0, “basic”), (7, “Gita”, “IN”, “2025-09-29”, 46000.0, “standard”), (8, “Hassan”, “PK”, “2025-10-01”, 52000.0, “premium”), ] schema = StructType([ StructField(“id”, IntegerType(), False), StructField(“name”, StringType(), True), StructField(“country”, StringType(), True), StructField(“signup_date”, StringType(), True), StructField(“income”, FloatType(), True), StructField(“plan”, StringType(), True), ]) df = spark.createDataFrame(data, schema) df.show() We begin by setting up PySpark, initializing the Spark session, and preparing our dataset. We create a structured DataFrame containing user information, including country, income, and plan type. This forms the foundation for all transformations and analyses that follow. Check out the\u00a0FULL CODES here. Copy CodeCopiedUse a different Browser df2 = (df.withColumn(“signup_ts”, F.to_timestamp(“signup_date”)) .withColumn(“year”, F.year(“signup_ts”)) .withColumn(“month”, F.month(“signup_ts”)) .withColumn(“is_india”, (F.col(“country”) == “IN”).cast(“int”))) df2.show() df2.createOrReplaceTempView(“users”) spark.sql(“”” SELECT country, COUNT(*) AS cnt, AVG(income) AS avg_income FROM users GROUP BY country ORDER BY cnt DESC “””).show() w = Window.partitionBy(“country”).orderBy(F.col(“income”).desc()) df_ranked = df2.withColumn(“income_rank_in_country”, F.rank().over(w)) df_ranked.show() def plan_priority(plan): if plan == “premium”: return 3 if plan == “standard”: return 2 if plan == “basic”: return 1 return 0 plan_priority_udf = F.udf(plan_priority, IntegerType()) df_udf = df_ranked.withColumn(“plan_priority”, plan_priority_udf(F.col(“plan”))) df_udf.show() We now perform various data transformations, add new columns, and register the DataFrame as a SQL table. We explore Spark SQL for aggregation and apply window functions to rank users by income. We also introduce a user-defined function (UDF) to assign priority levels to subscription plans. Check out the\u00a0FULL CODES here. Copy CodeCopiedUse a different Browser country_data = [ (“IN”, “Asia”, 1.42), (“US”, “North America”, 0.33), (“UK”, “Europe”, 0.07), (“AE”, “Asia”, 0.01), (“PK”, “Asia”, 0.24), ] country_schema = StructType([ StructField(“country”, StringType(), True), StructField(“region”, StringType(), True), StructField(“population_bn”, FloatType(), True), ]) country_df = spark.createDataFrame(country_data, country_schema) joined = df_udf.alias(“u”).join(country_df.alias(“c”), on=”country”, how=”left”) joined.show() region_stats = (joined.groupBy(“region”, “plan”) .agg(F.count(“*”).alias(“users”), F.round(F.avg(“income”), 2).alias(“avg_income”)) .orderBy(“region”, “plan”)) region_stats.show() We enrich our user dataset by joining it with country-level metadata that includes region and population. We then compute analytical summaries such as average income and user counts by region and plan type. This step demonstrates how Spark simplifies the seamless combination and aggregation of large datasets. Check out the\u00a0FULL CODES here. Copy CodeCopiedUse a different Browser ml_df = joined.withColumn(“label”, (F.col(“plan”) == “premium”).cast(“int”)).na.drop() country_indexer = StringIndexer(inputCol=”country”, outputCol=”country_idx”, handleInvalid=”keep”) country_fitted = country_indexer.fit(ml_df) ml_df2 = country_fitted.transform(ml_df) assembler = VectorAssembler(inputCols=[“income”, “country_idx”, “plan_priority”], outputCol=”features”) ml_final = assembler.transform(ml_df2) train_df, test_df = ml_final.randomSplit([0.7, 0.3], seed=42) lr = LogisticRegression(featuresCol=”features”, labelCol=”label”, maxIter=20) lr_model = lr.fit(train_df) preds = lr_model.transform(test_df) preds.select(“name”, “country”, “income”, “plan”, “label”, “prediction”, “probability”).show(truncate=False) evaluator = MulticlassClassificationEvaluator(labelCol=”label”, predictionCol=”prediction”, metricName=”accuracy”) acc = evaluator.evaluate(preds) print(“Classification accuracy:”, acc) We move into machine learning by preparing data for model training and feature engineering. We index categorical columns, assemble features, and train a logistic regression model to predict premium users. We then evaluate its accuracy, showcasing how Spark MLlib integrates easily into the data workflow. Check out the\u00a0FULL CODES here. Copy CodeCopiedUse a different Browser output_path = “\/content\/spark_users_parquet” joined.write.mode(“overwrite”).parquet(output_path) parquet_df = spark.read.parquet(output_path) print(“Parquet reloaded:”) parquet_df.show() recent = spark.sql(“”” SELECT name, country, income, signup_ts FROM users WHERE signup_ts >= ‘2025-10-01’ ORDER BY signup_ts DESC “””) recent.show() recent.explain() spark.stop() We conclude by writing the processed data to Parquet format and reading it back into Spark for verification. We run a SQL query to extract recent signups and inspect the query plan for optimization insights. Finally, we gracefully stop the Spark session to complete our workflow. In conclusion, we gain a practical understanding of how PySpark unifies data engineering and machine learning tasks within a single scalable framework. We witness how simple DataFrame transformations evolve into SQL analytics, feature engineering, and predictive modeling, all while staying within Google Colab. By experimenting with these concepts, we strengthen our ability to prototype and deploy Spark-based data solutions efficiently in both local and distributed setups. Check out the\u00a0FULL CODES here. Feel free to check out our\u00a0GitHub Page for Tutorials, Codes and Notebooks.\u00a0Also,\u00a0feel free to follow us on\u00a0Twitter\u00a0and don\u2019t forget to join our\u00a0100k+ ML SubReddit\u00a0and Subscribe to\u00a0our Newsletter. Wait! are you on telegram?\u00a0now you can join us on telegram as well. The post How to Build an End-to-End Data Engineering and Machine Learning Pipeline with Apache Spark and PySpark appeared first on MarkTechPost.<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_pvb_checkbox_block_on_post":false,"footnotes":""},"categories":[52,5,7,1],"tags":[],"class_list":["post-48446","post","type-post","status-publish","format-standard","hentry","category-ai-club","category-committee","category-news","category-uncategorized","pmpro-has-access"],"acf":[],"yoast_head":"\nHow to Build an End-to-End Data Engineering and Machine Learning Pipeline with Apache Spark and PySpark - 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