Data Engineering and Data Analytics course

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Overview
Course Content

Description

Build job-ready Data Engineers capable of designing data pipelines, handling big data, working with cloud platforms, and delivering insights through BI tools.

Course Content Overview:

- Foundations of Data Engineering

- SQL & Database Systems (Core Foundation)

- Python for Data Engineering (Support Module)

- PySpark for Big Data Processing

- Databricks Platform

- Azure Data Factory (ADF)

- AWS for Data Engineering (Basics)

- Snowflake Cloud Data Warehouse

- Data Integration & Orchestration

- Business Intelligence (BI)

- Capstone Projects (Very Important)

- Placement Preparation

Foundations of Data Engineering

Introduction to Data Engineering vs Data Science
Data lifecycle and architecture
Types of data (Structured, Semi-structured, Unstructured)
Batch vs Real-time processing
Data Warehousing concepts

SQL & Database Systems (Core Foundation)

1: SQL Basics
SELECT, WHERE, ORDER BY
CREATE, ALTER, DROP
2: Advanced SQL
JOINs (Inner, Left, Right, Full)
GROUP BY, HAVING
Subqueries & CTEs
3: Advanced Analytics
Window Functions
User Defined Functions (UDFs)
4: Performance Optimization
Indexing
Query optimization techniques
5: Hands-on: Real-world dataset queriesn

Python for Data Engineering (Support Module)

Python basics for data processing
Working with Pandas & NumPy
File handling (CSV, JSON, Parquet)
API data extraction
Writing reusable scripts for pipelines

PySpark for Big Data Processing

Introduction to Apache Spark ecosystem
RDDs vs DataFrames
Transformations & Actions
Data Cleaning and Processing
Aggregations & Joins at scale
Performance tuning in Spark
Hands-on: Large dataset processing

Databricks Platform

Introduction to Databricks
Workspace setup and notebooks
Cluster management
Running Spark jobs on Databricks
Collaborative workflows
Integration with ML pipelines

Azure Data Factory (ADF)

Introduction to Azure Data Factory
Building ETL pipelines
Data ingestion (APIs, DBs, files)
Pipeline triggers and scheduling
Monitoring and debugging pipelines
Hands-on: End-to-end ETL project

AWS for Data Engineering (Basics)

Introduction to Amazon Web Services
Amazon S3 for data storage
Amazon EC2 basics
AWS IAM for access control
Introduction to:
- AWS Glue
- Amazon Redshift

Snowflake Cloud Data Warehouse

Introduction to Snowflake
Architecture and concepts
Data loading (Bulk & Streaming)
Querying and optimization
Scaling and performance tuning
Secure data sharing

Business Intelligence (BI)

Introduction to BI concepts
Working with Power BI
Data modeling for dashboards
Creating interactive dashboards
Reports and KPI tracking
Connecting BI tools with data pipelines

Capstone Projects (Very Important)

Students must complete 2–3 real-world projects:
Project 1: End-to-End Data Pipeline
- Data ingestion → Processing → Storage → Visualization
Project 2: Big Data Processing System
- PySpark + Databricks + Cloud
Project 3: BI Dashboard Project
- Data warehouse → Power BI dashboards

Placement Preparation

Resume building (Data Engineering focused)
SQL + PySpark interview questions
Real-time scenario-based questions
Mock interviews

Course Features

	Duration	6 Month upto 4 Hrs Daily
	Language	Hindi/English
	Certificate	Yes
	Expert trainers	Yes
	Flexible Timing	Yes
	Fee Installements	Yes

Our Reviews

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contact@codebetter.in

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