├── 10_Pyspark_Dataframe_Interview_Question_with_Solution_v1_1748321341.pdf ├── 200 pages of Spark interview resources PDF.pdf ├── Big Data Engineering Module-2.pptx ├── Big Data Engineering.pptx ├── Essential Concepts for Big Data Processing.pdf ├── Hand Written Notes - Otis Data.pdf ├── Master this Basic Concept of PySpark.pdf ├── PYSPARK_ARCHITECTURE.pdf ├── PYSPARK_SQL_1746950165.pdf ├── PySpark Tutorial └── .md ├── PySpark kk.pdf ├── Pyspark Data Engineer.pdf ├── Pyspark Interview.pptx ├── Pyspark Module-1.pptx ├── Pyspark Module-3.pptx ├── Pyspark Module-4.pptx ├── Pyspark Module-5.pptx ├── Pyspark Module-6.pptx ├── Pyspark Module-7.pptx ├── Pyspark Module-8.pptx ├── README.md ├── Spark-Interview-Kit.zip ├── UDF, UDAF, UDFT.pdf ├── master_pyspark_zero_to_hero.pdf ├── reduceByKey() & countByValue() PDF.pdf └── spark opt.pdf /10_Pyspark_Dataframe_Interview_Question_with_Solution_v1_1748321341.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rganesh203/Apache-PySpark-Material/fc4327caf6048325035657d625c208e3d7abbbd2/10_Pyspark_Dataframe_Interview_Question_with_Solution_v1_1748321341.pdf -------------------------------------------------------------------------------- /200 pages of Spark interview resources PDF.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rganesh203/Apache-PySpark-Material/fc4327caf6048325035657d625c208e3d7abbbd2/200 pages of Spark interview resources PDF.pdf -------------------------------------------------------------------------------- /Big Data Engineering Module-2.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rganesh203/Apache-PySpark-Material/fc4327caf6048325035657d625c208e3d7abbbd2/Big Data Engineering Module-2.pptx -------------------------------------------------------------------------------- /Big Data Engineering.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rganesh203/Apache-PySpark-Material/fc4327caf6048325035657d625c208e3d7abbbd2/Big Data Engineering.pptx -------------------------------------------------------------------------------- /Essential Concepts for Big Data Processing.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rganesh203/Apache-PySpark-Material/fc4327caf6048325035657d625c208e3d7abbbd2/Essential Concepts for Big Data Processing.pdf -------------------------------------------------------------------------------- /Hand Written Notes - Otis Data.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rganesh203/Apache-PySpark-Material/fc4327caf6048325035657d625c208e3d7abbbd2/Hand Written Notes - Otis Data.pdf -------------------------------------------------------------------------------- /Master this Basic Concept of PySpark.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rganesh203/Apache-PySpark-Material/fc4327caf6048325035657d625c208e3d7abbbd2/Master this Basic Concept of PySpark.pdf -------------------------------------------------------------------------------- /PYSPARK_ARCHITECTURE.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rganesh203/Apache-PySpark-Material/fc4327caf6048325035657d625c208e3d7abbbd2/PYSPARK_ARCHITECTURE.pdf -------------------------------------------------------------------------------- /PYSPARK_SQL_1746950165.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rganesh203/Apache-PySpark-Material/fc4327caf6048325035657d625c208e3d7abbbd2/PYSPARK_SQL_1746950165.pdf -------------------------------------------------------------------------------- /PySpark Tutorial/.md: -------------------------------------------------------------------------------- 1 | 🔥 What is Apache Spark? 2 | 3 | Apache Spark is an open-source, distributed big data processing framework built for speed, ease of use, and sophisticated analytics. Originally developed at UC Berkeley’s AMPLab, it is now a top-level project of the Apache Software Foundation. 4 | 5 | Apache Spark is an open-source distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. It's designed to be fast and general-purpose, enabling a wide range of applications to run faster and at scale. 6 | 7 | ✅ Key Features of Apache Spark: 8 | 9 | Feature Description 10 | In-Memory Computation Stores intermediate data in memory (RAM) instead of writing to disk, leading to much faster performance compared to Hadoop. 11 | Distributed Processing Works with multiple nodes in a cluster to handle large-scale data across machines. 12 | Fault Tolerance Automatically handles node failures using lineage graphs and resilient distributed datasets (RDDs). 13 | Multi-Language Support APIs available in Scala (native), Java, Python (PySpark), R, and SQL. 14 | Unified Engine Supports batch processing, streaming, machine learning, and graph processing in one engine. 15 | Speed: Spark can run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk. 16 | 17 | PySpark: 18 | 19 | PySpark is the Python API for Spark, which allows developers to harness the simplicity of Python and the power of Apache Spark to scale up their data processing applications. It exposes the Spark programming model to Python through a simple, efficient API built on top of Spark's Java API. 20 | 21 | Key Differences between Apache Spark and PySpark: 22 | 23 | Language: Apache Spark provides APIs in multiple languages like Scala, Java, Python, and R, whereas PySpark specifically caters to Python developers. 24 | 25 | Ease of Use: PySpark is particularly popular among Python developers due to its ease of use and integration with Python's rich ecosystem of libraries. 26 | 27 | Performance: Both Apache Spark and PySpark offer similar performance characteristics, but the choice of language might affect performance slightly due to language-specific optimizations. 28 | 29 | Flexibility: While Apache Spark offers a wider range of language support, PySpark is optimized for Python-centric workflows, making it easier to integrate with Python-based tools and libraries. 30 | 31 | 🏗️ Apache Spark Architecture 32 | 33 | Driver Program – The main application that defines transformations and actions. 34 | 35 | Cluster Manager – Allocates resources (YARN, Mesos, Kubernetes, or Spark’s own Standalone). 36 | 37 | Executors – Run on worker nodes and execute tasks assigned by the driver. 38 | 39 | Tasks – Units of work sent to executors by the driver. 40 | 41 | 📊 Core Components: 42 | 43 | Component Description 44 | Spark Core Base engine providing memory management, fault recovery, task scheduling, etc. 45 | Spark SQL SQL interface to process structured data. 46 | Spark Streaming Real-time data processing (micro-batches). 47 | MLlib Machine learning library for Spark. 48 | GraphX Library for graph and network computation. 49 | 50 | 🐍 What is PySpark? 51 | 52 | PySpark is the Python API for Apache Spark, enabling Python developers to write Spark applications using Python instead of Scala or Java. PySpark wraps the Spark engine using Py4J, allowing Python code to interface with the JVM-based Spark runtime. 53 | 54 | 🔧 Features of PySpark: 55 | 56 | Native integration with Python libraries like pandas, NumPy, matplotlib, and scikit-learn. 57 | Allows using Spark SQL, DataFrames, and RDDs in Python. 58 | Enables large-scale data transformations, ETL pipelines, data analytics, and machine learning using Python. 59 | 60 | 🔁 Apache Spark vs PySpark — Comparison Table 61 | 62 | Feature Apache Spark (Core) PySpark 63 | Language Primarily written in Scala (also supports Java, R, SQL) Python 64 | Target Users Scala/Java developers Python developers / Data Scientists 65 | Ease of Use Moderate (strong typing, verbose syntax) High (Python is more readable and intuitive) 66 | Performance Slightly faster due to native language (Scala) Slightly slower (uses Py4J to connect to JVM) 67 | Data Science Integration Needs external tools Seamlessly integrates with Python data science tools 68 | Use Case Backend systems, enterprise-scale pipelines Data analysis, prototyping, ML workflows 69 | 70 | 📌 When to Use PySpark? 71 | 72 | When you're a Python developer and want to leverage Spark’s distributed power. 73 | When building ETL pipelines using Python. 74 | When using pandas becomes too slow or memory-bound for large data. 75 | When integrating Spark into machine learning workflows using tools like scikit-learn, MLlib, or TensorFlow. 76 | 77 | 💡 PySpark Code Example: 78 | 79 | from pyspark.sql import SparkSession 80 | 81 | # Initialize Spark session 82 | 83 | spark = SparkSession.builder \ 84 | .appName("PySpark Example") \ 85 | .getOrCreate() 86 | 87 | # Create DataFrame 88 | 89 | data = [("Alice", 25), ("Bob", 30), ("Cathy", 27)] 90 | df = spark.createDataFrame(data, ["Name", "Age"]) 91 | 92 | # Filter and show results 93 | 94 | df.filter(df.Age > 26).display() 95 | 96 | 🧠 Real-World Use Cases 97 | Use Case Description 98 | ETL Pipelines Load, transform, and process terabytes of data across distributed systems. 99 | Real-Time Analytics Monitor streams of data (logs, metrics) with Spark Streaming. 100 | Machine Learning Build distributed ML models using MLlib or external libraries in PySpark. 101 | Recommendation Systems Use collaborative filtering or deep learning for personalized recommendations. 102 | Data Warehousing Use Spark SQL to replace or augment traditional data warehouses. 103 | 104 | 🆚 Summary: Spark vs PySpark 105 | 106 | In summary, Apache Spark is the underlying framework that provides scalable and efficient data processing capabilities, while PySpark is the Python API that allows developers to harness these capabilities using Python. 107 | 108 | Use Apache Spark (Scala/Java) when: 109 | You need maximum performance. 110 | You're building backend systems. 111 | You're already using the JVM ecosystem. 112 | Use PySpark when: 113 | You're a Python developer or data scientist. 114 | You're doing exploratory data analysis or ML. 115 | You want to integrate with Python libraries (like pandas, NumPy, scikit-learn). 116 | Would you like a visual diagram of the architecture or a step-by-step project tutorial using PySpark? 117 | -------------------------------------------------------------------------------- /PySpark kk.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rganesh203/Apache-PySpark-Material/fc4327caf6048325035657d625c208e3d7abbbd2/PySpark kk.pdf -------------------------------------------------------------------------------- /Pyspark Data Engineer.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rganesh203/Apache-PySpark-Material/fc4327caf6048325035657d625c208e3d7abbbd2/Pyspark Data Engineer.pdf -------------------------------------------------------------------------------- /Pyspark Interview.pptx: -------------------------------------------------------------------------------- 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Complete Picture of Apache Spark 11 |  Why Spark with Python 12 |  Apache spark Architecture 13 |  Big Data Analytics in industry 14 | 15 | Section 2: Using Hadoop’s Core: HDFS and MapReduce 16 | 17 |  HDFS: What it is, and how it works 18 |  MapReduce: What it is, and how it works 19 |  How MapReduce distributes processing 20 |  HDFS commands 21 | 22 | Section 3: Spark Databox Cloud Lab 23 | 24 |  How to access Spark Databox cloud lab? 25 |  Step by Step instruction to access cloud Big data Lab. 26 | 27 | Section 4: Data analytics lifecycle 28 | 29 |  Data Discovery 30 |  Data Preparation 31 |  Data Model Planning 32 |  Data Model Building 33 |  Data Insights 34 | 35 | Section 5: python 3.0 (Crash Course) 36 | 37 |  Environment Setup 38 |  Decision Making 39 |  Loops and Number 40 |  Strings 41 |  Lists 42 |  Tuples 43 |  Dictionary 44 |  Date and Time 45 |  Regex 46 |  Functions 47 |  Modules 48 |  Files I/O 49 |  Exceptions 50 |  Multi-Threading 51 |  Set 52 |  Lamda Function 53 | 54 | Section 6: Why Spark with Python ? 55 | 56 |  Why Spark? 57 |  Why Spark with Python (PySpark)? 58 | 59 | Section 7: Configure Running Platform 60 | 61 |  Run on Databricks Community Cloud 62 |  Configure Spark on Mac and Ubuntu 63 |  Configure Spark on Windows 64 |  PySpark With Text Editor or IDE 65 |  PySparkling Water: Spark + H2O 66 |  Set up Spark on Cloud 67 |  PySpark on Colaboratory 68 |  Demo Code in this Section 69 | 70 | Section 8: An Introduction to Apache Spark 71 | 72 |  Core Concepts 73 |  Spark Components 74 |  Architecture 75 |  How Spark Works? 76 | 77 | Section 9: Programming with RDDs 78 | 79 |  Create RDD 80 |  Spark Operations 81 |  rdd.DataFrame vs pd.DataFrame 82 | 83 | Section 10: Statistics and Linear Algebra Preliminaries 84 | 85 |  Notations 86 |  Linear Algebra Preliminaries 87 |  Measurement Formula 88 |  Confusion Matrix 89 |  Statistical Tests 90 | 91 | Section 11: Data Exploration 92 | 93 |  Univariate Analysis 94 |  Multivariate Analysis 95 | 96 | Section 12: Data Manipulation: Features 97 | 98 |  Feature Extraction 99 |  Feature Transform 100 |  Feature Selection 101 |  Unbalanced data: Undersampling 102 | 103 | Section 13: Regression 104 | 105 |  Linear Regression 106 |  Generalized linear regression 107 |  Decision tree Regression 108 |  Random Forest Regression 109 |  Gradient-boosted tree regression 110 | 111 | Section 14: Regularization 112 | 113 |  Ordinary least squares regression 114 |  Ridge regression 115 |  Least Absolute Shrinkage and Selection Operator (LASSO) 116 |  Elastic net 117 | 118 | Section 15: Classification 119 | 120 |  Binomial logistic regression 121 |  Multinomial logistic regression 122 |  Decision tree Classification 123 |  Random forest Classification 124 |  Gradient-boosted tree Classification 125 |  XGBoost: Gradient-boosted tree Classification 126 |  Naive Bayes Classification 127 | 128 | Section 16: Clustering 129 | 130 |  K-Means Model 131 | 132 | Section 17: RFM Analysis 133 | 134 |  RFM Analysis Methodology 135 |  Demo 136 |  Extension 137 | Section 18: Text Mining 138 | 139 |  Text Collection 140 |  Text Preprocessing 141 |  Text Classification 142 |  Sentiment analysis 143 |  N-grams and Correlations 144 |  Topic Model: Latent Dirichlet Allocation 145 | 146 | Section 19: Social Network Analysis 147 | 148 |  Introduction 149 |  Co-occurrence Network 150 |  Appendix: matrix multiplication in PySpark 151 |  Correlation Network 152 | 153 | Section 20: ALS: Stock Portfolio Recommendations 154 | 155 |  Recommender systems 156 |  Alternating Least Squares 157 |  Demo 158 | 159 | Section 21: Monte Carlo Simulation 160 | 161 |  Simulating Casino Win 162 |  Simulating a Random Walk 163 | 164 | Section 22: Markov Chain Monte Carlo 165 | 166 |  Metropolis algorithm 167 |  A Toy Example of Metropolis 168 |  Demos 169 | 170 | Section 23: Neural Network 171 | 172 |  Feedforward Neural Network 173 | 174 | Section 24: Automation for Cloudera Distribution Hadoop 175 | 176 |  Automation Pipeline 177 |  Data Clean and Manipulation Automation 178 |  ML Pipeline Automation 179 |  Save and Load PipelineModel 180 |  Ingest Results Back into Hadoop 181 | 182 | Section 25: Wrap PySpark Package 183 | 184 |  Package Wrapper 185 |  Pacakge Publishing on PyPI 186 | 187 | Section 26: PySpark Data Audit Library 188 | 189 |  Install with pip 190 |  Install from Repo 191 |  Uninstall 192 |  Test 193 |  Auditing on Big Dataset 194 | 195 | Section 27: Zeppelin to jupyter notebook 196 | 197 |  How to Install 198 |  Converting Demos 199 | 200 | Section 28: JDBC Connection 201 | 202 |  JDBC Driver 203 |  JDBC read 204 |  JDBC write 205 |  JDBC temp_view 206 | 207 | Section 29: Databricks Tips 208 | 209 |  Display samples 210 |  Auto files download 211 |  Working with AWS S3 212 |  delta format 213 |  mlflow 214 | 215 | Section 27: PySpark API 216 | 217 |  Stat API 218 |  Regression API 219 |  Classification API 220 |  Clustering API . 221 |  Recommendation API 222 |  Pipeline API 223 |  Tuning API 224 |  Evaluation API 225 | -------------------------------------------------------------------------------- /Spark-Interview-Kit.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rganesh203/Apache-PySpark-Material/fc4327caf6048325035657d625c208e3d7abbbd2/Spark-Interview-Kit.zip -------------------------------------------------------------------------------- /UDF, UDAF, UDFT.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rganesh203/Apache-PySpark-Material/fc4327caf6048325035657d625c208e3d7abbbd2/UDF, UDAF, UDFT.pdf -------------------------------------------------------------------------------- /master_pyspark_zero_to_hero.pdf: 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