Apache Spark is a lightning-fast, open-source big data processing framework that supports real-time analytics, machine learning, and large-scale data transformations. This course is designed for data scientists, big data engineers, and analytics professionals who want to master data science using PySpark or Spark with Scala/Java. Learn how to handle massive datasets, perform advanced analytics, and build predictive models using Spark’s MLlib, DataFrame API, and Spark SQL with real-time projects and distributed computing fundamentals.
What is Apache Spark?
Spark vs Hadoop MapReduce
Spark Ecosystem Overview (Spark Core, Spark SQL, Spark Streaming, MLlib, GraphX)
Spark Use Cases in Data Science
Cluster Managers: YARN, Mesos, Spark Standalone
Spark Core Architecture
RDD (Resilient Distributed Dataset) vs DataFrame vs Dataset
Spark Installation (Standalone & Cloud)
Running Spark on Local, Cluster, and Databricks
Introduction to PySpark / Scala for Spark
Creating and Loading DataFrames
Reading from CSV, JSON, Parquet
Spark SQL and Querying Structured Data
DataFrame Operations: Filtering, Grouping, Joins, Aggregation
Handling Missing Data & Nulls
UDFs (User Defined Functions) in Spark
SparkSession & SparkContext
Schema Inference and Manual Schema Definition
Writing Data to Files and Databases
Partitioning, Bucketing, Caching, and Persistence
Query Optimization using Catalyst Optimizer
Spark JDBC Integration
Introduction to MLlib
Feature Engineering in Spark
VectorAssembler & StringIndexer
Supervised Learning: Linear Regression, Logistic Regression, Decision Trees
Clustering: K-Means
Pipelines & Model Evaluation (Cross-validation, Train-Test Split)
Model Persistence and Export
Introduction to Spark Streaming
DStreams vs Structured Streaming
Streaming Data Sources (Kafka, Socket, Files)
Windowing Operations
Use Case: Real-time Log Monitoring or Tweet Sentiment Analysis
Integration with HDFS, Hive, Cassandra
Using Spark with AWS S3
Spark on Databricks and EMR (AWS)
Using Airflow/Scheduler to Run Jobs
Visualization Tools: Spark + Tableau or Power BI
Real-Time Sales Analysis on Big Dataset
Customer Churn Prediction
Log Analytics & Real-time Dashboards
Machine Learning Model using MLlib
Spark SQL for BI Reports
High demand for Big Data + Data Science professionals
Spark is widely used in enterprise-scale data environments
Learn distributed processing with hands-on real-time use cases
Makes you eligible for Data Engineer + Data Scientist hybrid roles
Future-proof skill for cloud, AI, and streaming systems
Big Data/Data Science Career Roadmap
Resume & LinkedIn Optimization
Certification Guidance: Databricks, Spark Developer
Interview Preparation: Data Engineering & Analytics
Portfolio Building with Real Projects
🔍 Roles You Can Apply For:
Data Scientist (with Big Data)
Big Data Analyst
PySpark Developer
Spark Data Engineer
Machine Learning Engineer (Spark MLlib)
💸 Expected Salary Range (India):
| Experience | Role | Avg Salary |
|---|---|---|
| 0–1 years | Big Data Intern / Trainee | ₹3 – ₹4.5 LPA |
| 1–3 years | Spark / PySpark Developer | ₹5 – ₹9 LPA |
| 3–5 years | Sr. Data Scientist / Engineer | ₹10 – ₹18 LPA |
✅ Real-Time Industry Projects
✅ Spark with Python or Scala (based on your preference)
✅ Interview Q&A + Mock
✅ Certificate of Completion
✅ Community Support & Job Group Access
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