Introduction to Programming

Basics of programming logic

Understanding algorithms and flowcharts

Overview of Python as a programming language

Setting Up Python Environment

Installing Python

Working with Python IDEs 

(Integrated Development Environments)

Writing and executing the first Python script

Python Basics

Variables and data types

Basic operations (arithmetic, comparison, logical)

Input and output (print, input)

Control Flow

Conditional statements (if, elif, else)

Loops (for, while)

Break and continue statements

Functions in Python

Defining functions

Parameters and return values

Scope and lifetime of variables

Lists and Tuples

Creating and manipulating lists

Slicing and indexing

Working with tuples

Dictionaries and Sets

Understanding dictionaries

Operations on sets

Use cases for dictionaries and sets

File Handling

Reading and Writing Files

Opening and closing files

Reading from and writing to files

Working with different file formats (text, CSV)

Error Handling and Modules

Error Handling

Introduction to exceptions

Try, except, finally blocks

Handling different types of errors

Overview of Microsoft Azure

History and evolution of Azure

Azure services and products

Azure global infrastructure

Getting Started with Azure

Creating an Azure account

Azure Portal overview

Azure pricing and cost management

Azure Core Services

Azure Virtual Machines (VMs)

Azure Storage (Blobs, Files, Queues, Tables)

Azure Networking (Virtual Network, Load Balancer, VPN Gateway)

Azure Database Services

Azure SQL Database

Azure Cosmos DB

Azure Storage

Azure Data Lake Storage

Introduction to Azure Data Factory

Overview of Azure Data

Factory and its features

Comparison with other data integration services

Getting Started with Azure Data Factory

Setting up an Azure Data Factory instance

Exploring the Azure Data Factory user interface

Data Movement in Azure Data Factory

Copying data from various sources to destinations

Transforming data during the copy process

Data Orchestration in Azure Data Factory

Creating and managing data pipelines

Monitoring and managing pipeline runs

Data Integration with Azure Data Factory

Using datasets and linked services

Building complex data integration workflows

Data Transformation in Azure Data Factory

Using data flows for data transformation

Transforming data using mapping data flows

Integration with Azure Services

Integrating Azure Data Factory with other Azure services like Azure Blob Storage, Azure SQL Database, etc.

Using Azure Data Factory with Azure Databricks for advanced data processing

Monitoring and Management

Monitoring pipeline and activity runs

Managing and optimizing data pipelines for performance

SQL Advance Queries

SQL Data Models

SQl

Overview of Azure Data

Factory and its features

Comparison with other data integration services

Getting Started with Azure Data Factory

Setting up an Azure Data Factory instance

Exploring the Azure Data Factory user interface

Data Movement in Azure Data Factory

Copying data from various sources to destinations

Transforming data during the copy process

Data Orchestration in Azure Data Factory

Creating and managing data pipelines

Monitoring and managing pipeline runs

Data Integration with Azure Data Factory

Using datasets and linked services

Building complex data integration workflows

Data Transformation in Azure Data Factory

Using data flows for data transformation

Transforming data using mapping data flows

Integration with Azure Services

Integrating Azure Data Factory with other Azure services like Azure Blob Storage, Azure SQL Database, etc.

Using Azure Data Factory with Azure Databricks for advanced data processing

Monitoring and Management

Monitoring pipeline and activity runs

Managing and optimizing data pipelines for performance

Data Modeling: Designing the structure of the data warehouse, including defining dimensions, facts, and relationships between them.

ETL (Extract, Transform, Load): Processes for extracting data from source systems, transforming it into a format suitable for analysis, and loading it into the data warehouse.

Dimensional Modeling: A technique for designing databases that are optimized for querying and analyzing data, often used in data warehousing.

Star and Snowflake Schema: Common dimensional modeling schemas used in data warehousing to organize data into a central fact table and related dimension tables.

Data Mart: A subset of the data warehouse that is designed for a specific department or business function, providing a more focused view of the data.

Fact Table: A table in a data warehouse that contains the primary data for analysis, typically containing metrics or facts that can be analyzed.

Dimension Table: A table in a data warehouse that contains descriptive information about the data, such as time, location, or product details.

ETL Tools: Software tools used to extract data from various sources, transform it into a usable format, and load it into the data warehouse.

Data Quality: Ensuring that data is accurate, consistent, and reliable, often through processes such as data cleansing and validation.

Data Governance: Policies and procedures for managing data assets, ensuring data quality, and ensuring compliance with regulations and standards.

Data Warehouse Architecture: The overall structure and components of a data warehouse, including data sources, ETL processes, storage, and access layers.

Introduction to Azure Databricks

Overview of Azure Databricks and its features

Benefits of using Azure Databricks for data engineering and data science

Getting Started with Azure Databricks

Creating an Azure Databricks workspace

Overview of the Azure Databricks workspace interface

Apache Spark Basics

Introduction to Apache Spark

Understanding Spark RDDs, DataFrames, and Datasets

Working with Azure Databricks Notebooks

Creating and managing notebooks in Azure Databricks

Writing and executing Spark code in notebooks

Data Exploration and Preparation

Loading and saving data in Azure Databricks

Data exploration and basic data cleaning using Spark

Data Processing with Spark

Performing data transformations using Spark SQL and DataFrame API

Working with structured and semi-structured data

Advanced Analytics with Azure Databricks

Running machine learning algorithms using MLlib in Azure Databricks

Visualizing data and results in Azure Databricks

Optimizing Performance

Best practices for optimizing Spark jobs in Azure Databricks

Understanding and tuning Spark configurations

Integration with Azure Services

Integrating Azure Databricks with Azure Storage (e.g., Azure Blob Storage, Azure Data Lake Storage)

Using Azure Databricks in conjunction with other Azure services (e.g., Azure SQL Database, Azure Cosmos DB)

Collaboration and Version Control

Collaborating with team members using Azure Databricks

Using version control with Azure Databricks notebooks

Real-time Data Processing

Processing streaming data using Spark Streaming in Azure Databricks

Building real-time data pipelines

Introduction to Azure Synapse Analytics

What is Synapse Analytics Service?
Create Dedicated SQL Pool Explore Synapse Studio V2
Analyse Data using Apache Spark Notebook
Analyse Data using Dedicated SQL Pool
Monitor Synapse Studio

Apache Spark
Introduction of Spark
Spark Architecture
PySpark