This course provides hands-on training in Apache Spark with Python (PySpark) to process, analyze, and visualize large-scale datasets. Learners will gain expertise in distributed data processing, Spark SQL, machine learning pipelines, and big data analytics workflows for real-world applications.
Key Features of Course Divine:
Module 1: Introduction to Big Data & PySpark Basics of Big Data & challenges with traditional systems Hadoop vs Spark overview Spark ecosystem and architecture (RDD, DAG, Cluster Manager) Introduction to PySpark and environment setup.
Module 2: Working with RDDs (Resilient Distributed Datasets) Creating and transforming RDDs Lazy vs. eager evaluation Actions & transformations in RDD Fault tolerance & lineage.
Module 3: Spark Data Frames & SQL Introduction to Data Frames & Spark SQL Schema definition & inference Filtering, grouping, aggregations SQL queries with Spark Joins and window functions.
Module 4: Data Ingestion & Sources Reading and writing data: CSV, JSON, Parquet, ORC Connecting to databases (JDBC) Working with structured & unstructured data Streaming data ingestion basics (Kafka, real-time sources).
Module 5: Data Cleaning & Transformation Handling missing values Data normalization & transformation User-defined functions (UDFs) in PySpark Optimizing queries with Catalyst Optimizer.
Module 6: Spark MLlib – Machine Learning with PySpark Introduction to MLlib Feature engineering Classification, regression, clustering models Building ML pipelines Model evaluation & tuning.
Module 7: PySpark Streaming Batch vs real-time processing Spark Streaming architecture Streaming with Kafka & socket data Stateful stream processing Real-time dashboards.
Module 8: Advanced PySpark Concepts Spark GraphFrames for Graph Processing Performance tuning & optimization Partitioning & caching strategies Broadcast variables & accumulators.
Module 9: Big Data Ecosystem Integration PySpark with Hadoop HDFS PySpark with Hive & HBase Spark on cloud (AWS EMR, Azure Databricks, GCP DataProc) Containerization with PySpark (Docker & Kubernetes basics).
Module 10: Capstone Projects Retail sales data analysis with PySpark SQL Real-time sentiment analysis using Spark Streaming + Kafka Predictive analytics (churn prediction / fraud detection) using Mallis Building a recommendation system with PySpark.
Mobile: 9100348679
Email: coursedivine@gmail.com
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