PySpark for Big Data Analysis Certified Course

Course Descripting:

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:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After PySpark for Big Data Analysis Certified Course:

• Big Data Engineer
• Data Analyst (Big Data)
• Spark/PySpark Developer
• Machine Learning Engineer (Big Data focus)
• Cloud Data Engineer

Essential Skills you will Develop PySpark for Big Data Analysis Certified Course:

• Writing optimized distributed data pipelines with PySpark
• Handling structured, semi-structured, and unstructured data
• Building scalable ML models with Spark MLlib
• Real-time analytics with Spark Streaming
• Deploying Spark jobs on cloud platforms

Tools Covered:

• Apache Spark (PySpark)
• Hadoop HDFS
• Hive, HBase
• Kafka
• AWS EMR / Databricks
• Jupyter Notebook / VS Code

Syllabus:

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.

Industry Projects:

• Retail & E-commerce Analytics
• Real-Time Sentiment Analysis (Social Media / Streaming Data)
• Fraud Detection in Financial Transactions

 Who is this program for?

• Data Analysts & Data Scientists
• Software Engineers & Developers
• Machine Learning & AI Enthusiasts
• Database & ETL Professionals
• Business Intelligence (BI) Professionals
• Researchers & Academics
• Students & Fresh Graduates (CS, IT, Data Science, Engineering)

How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com