Are you dreaming of a high-paying career in Big Data, Cloud, or Data Engineering?
At Sreyobhilashi IT, we offer one of the most comprehensive and hands-on PySpark training programs in Hyderabad, tailored for today’s rapidly evolving tech landscape. Whether you’re a fresh graduate, an IT professional looking to upskill, or someone aiming to switch to a data engineering role, this course is built just for you.
We don’t just teach tools—we shape problem solvers who can handle real-world big data challenges confidently.
🚀 Why Choose Our PySpark & Big Data Training?
🎯 Live Online + Offline Training (Hyderabad)
💡 Daily tasks with real-time scenarios
🎥 Every session recorded & shared for lifetime access
🧑💼 Trainer with 13+ years of real-time experience
💼 Resume building, mock interviews, and job support
🏅 Certification guidance (Cloudera, Databricks, Hortonworks)
📅 Timings:
Morning Batches: 6:00–7:30 AM IST & 7:40–9:15 AM IST
Evening Batch: 7:30–9:00 PM IST
📍 Mode: Online + Offline (Hyderabad)
👨🏫 Trainer: Venu
💰 Course Fee: ₹30,000/- (worth every rupee if you practice consistently)
📱 WhatsApp/Call: +91-8500002025 / +91-9247159150
✅ Fill the form to book your seat
Important notes:
- Daily after training assign a task
- Who completed all these tasks they will get 5000/- money back.
- After training provides a solution to that problem.
- Minimum 3 months online support & Job Assistance
- Training in Spark 3.5.1 using Python (Pyspark) and Scala language
- Excellent Materials all major spark and Scala books
- Guide to get Cloudera/MapR/Databricks spark certification
Recommendations: To learn Apache Spark, no need to learn Hadoop, but If you have Hadoop knowledge, it’s a huge plus to implement production level project.
To learn Spark Minimum core java (to learn Scala) and SQL queries knowledge mandatory.
This training intentionally is done for non Hadoop background students.
🔹 Hadoop & Big Data Foundations
Lecture:
Introduction to Big Data – Volume, Velocity, Variety
Hadoop Architecture Overview
HDFS Internal Working (Read & Write Flow)
YARN Architecture – ResourceManager, NodeManager
Limitations of Hadoop & rise of Spark
Hands-On:
HDFS Shell Commands – mkdir, put, get, cat
Install Hadoop & Spark on Ubuntu (Single Node)
Set up Hadoop & Spark in Eclipse / IntelliJ
Upload, read, and process data in HDFS
🔹 Hive & Sqoop Integration
Lecture:
Hive Data Warehouse Concepts
How Hive Converts SQL to MapReduce/Spark
Partitioning vs Bucketing in Hive
Sqoop Architecture & Use Cases
Hands-On:
Create and Query Hive Tables (CSV, JSON)
Load and Query Partitioned & Bucketed Tables
Import MySQL/Oracle Data using Sqoop
Export Hive Data to RDBMS
🔹 Scala Basics for Spark (Optional)
Lecture:
Why Scala for Spark? Scala vs Java
Functional Programming Concepts
Immutable vs Mutable Collections
Hands-On:
Scala Syntax: Strings, Lists, Arrays, Maps, Sets
Functions, Pattern Matching, Control Statements
Writing Scala scripts in REPL and IDE
🔹 Spark Core Essentials
Lecture:
Spark Architecture: Driver, Executors, Cluster Manager
RDD vs DataFrame vs Dataset
Transformations, Actions & DAG Execution
Spark Shell & Cluster Modes
Hands-On:
Create RDDs from Collections & Files
Map, Filter, Reduce, FlatMap operations
Analyze Logs using RDDs
Execute programs via CLI & IDE
🔹 Spark SQL & DataFrames
Lecture:
DataFrame vs RDD – When to use what?
Spark SQL Architecture & Catalyst Optimizer
Data Sources: CSV, JSON, Parquet, ORC
Working with Views & Temp Tables
Hands-On:
Read & Query CSV, JSON, Parquet files
Use DataFrame API and Spark SQL
Data Transformations: groupBy, join, filter
Caching & Persisting DataFrames
Process Hive tables with Spark SQL
🔹 Dataset API & Complex Data Types
Lecture:
Dataset API in Spark 2.x and above
Case Classes & Encoders
Working with Nested Data (Structs, Arrays)
Hands-On:
Load JSON, XML into Dataset
Define Schemas for Structured Data
Parse and Transform Nested JSON
Create reusable DataFrame functions
🔹 Spark Job Execution & Optimization
Lecture:
Spark Jobs, Stages, Tasks
Shuffles, Wide vs Narrow Dependencies
Broadcast Variables & Accumulators
Best Practices for Performance
Hands-On:
Monitor DAG using Spark UI
Analyze Stage Execution and Memory Usage
Enable Spark Logs for Debugging
Use Caching, Partitioning for Optimization
🔹 Hadoop & Big Data Foundations
Lecture:
Introduction to Big Data – Volume, Velocity, Variety
Hadoop Architecture Overview
HDFS Internal Working (Read & Write Flow)
YARN Architecture – ResourceManager, NodeManager
Limitations of Hadoop & rise of Spark
Hands-On:
HDFS Shell Commands – mkdir, put, get, cat
Install Hadoop & Spark on Ubuntu (Single Node)
Set up Hadoop & Spark in Eclipse / IntelliJ
Upload, read, and process data in HDFS
🔹 Hive & Sqoop Integration
Lecture:
Hive Data Warehouse Concepts
How Hive Converts SQL to MapReduce/Spark
Partitioning vs Bucketing in Hive
Sqoop Architecture & Use Cases
Hands-On:
Create and Query Hive Tables (CSV, JSON)
Load and Query Partitioned & Bucketed Tables
Import MySQL/Oracle Data using Sqoop
Export Hive Data to RDBMS
🔹 Scala Basics for Spark (Optional)
Lecture:
Why Scala for Spark? Scala vs Java
Functional Programming Concepts
Immutable vs Mutable Collections
Hands-On:
Scala Syntax: Strings, Lists, Arrays, Maps, Sets
Functions, Pattern Matching, Control Statements
Writing Scala scripts in REPL and IDE
🔹 Spark Core Essentials
Lecture:
Spark Architecture: Driver, Executors, Cluster Manager
RDD vs DataFrame vs Dataset
Transformations, Actions & DAG Execution
Spark Shell & Cluster Modes
Hands-On:
Create RDDs from Collections & Files
Map, Filter, Reduce, FlatMap operations
Analyze Logs using RDDs
Execute programs via CLI & IDE
🔹 Spark SQL & DataFrames
Lecture:
DataFrame vs RDD – When to use what?
Spark SQL Architecture & Catalyst Optimizer
Data Sources: CSV, JSON, Parquet, ORC
Working with Views & Temp Tables
Hands-On:
Read & Query CSV, JSON, Parquet files
Use DataFrame API and Spark SQL
Data Transformations: groupBy, join, filter
Caching & Persisting DataFrames
Process Hive tables with Spark SQL
🔹 Dataset API & Complex Data Types
Lecture:
Dataset API in Spark 2.x and above
Case Classes & Encoders
Working with Nested Data (Structs, Arrays)
Hands-On:
Load JSON, XML into Dataset
Define Schemas for Structured Data
Parse and Transform Nested JSON
Create reusable DataFrame functions
🔹 Spark Job Execution & Optimization
Lecture:
Spark Jobs, Stages, Tasks
Shuffles, Wide vs Narrow Dependencies
Broadcast Variables & Accumulators
Best Practices for Performance
Hands-On:
Monitor DAG using Spark UI
Analyze Stage Execution and Memory Usage
Enable Spark Logs for Debugging
Use Caching, Partitioning for Optimization
Course Features
- Lecture 0
- Quiz 0
- Duration 10 weeks
- Skill level All levels
- Language English
- Students 0
- Assessments Yes
