7 Sections
162 Lessons
10 Weeks
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Python
39
1.1
Introduction to Programming
1.2
Basics of programming logic
1.3
Understanding algorithms and flowcharts
1.4
Overview of Python as a programming language
1.5
Setting Up Python Environment
1.6
Installing Python
1.7
Working with Python IDEs
1.8
(Integrated Development Environments)
1.9
Writing and executing the first Python script
1.10
Python Basics
1.11
Variables and data types
1.12
Basic operations (arithmetic, comparison, logical)
1.13
Input and output (print, input)
1.14
Control Flow
1.15
Conditional statements (if, elif, else)
1.16
Loops (for, while)
1.17
Break and continue statements
1.18
Functions in Python
1.19
Defining functions
1.20
Parameters and return values
1.21
Scope and lifetime of variables
1.22
Lists and Tuples
1.23
Creating and manipulating lists
1.24
Slicing and indexing
1.25
Working with tuples
1.26
Dictionaries and Sets
1.27
Understanding dictionaries
1.28
Operations on sets
1.29
Use cases for dictionaries and sets
1.30
File Handling
1.31
Reading and Writing Files
1.32
Opening and closing files
1.33
Reading from and writing to files
1.34
Working with different file formats (text, CSV)
1.35
Error Handling and Modules
1.36
Error Handling
1.37
Introduction to exceptions
1.38
Try, except, finally blocks
1.39
Handling different types of errors
Azure
17
2.1
Overview of Microsoft Azure
2.2
History and evolution of Azure
2.3
Azure services and products
2.4
Azure global infrastructure
2.5
Getting Started with Azure
2.6
Creating an Azure account
2.7
Azure Portal overview
2.8
Azure pricing and cost management
2.9
Azure Core Services
2.10
Azure Virtual Machines (VMs)
2.11
Azure Storage (Blobs, Files, Queues, Tables)
2.12
Azure Networking (Virtual Network, Load Balancer, VPN Gateway
2.13
Azure Database Services
2.14
Azure SQL Database
2.15
Azure Cosmos DB
2.16
Azure Storage
2.17
Azure Data Lake Storage
ADF
25
3.1
Introduction to Azure Data Factory
3.2
Overview of Azure Data
3.3
Factory and its features
3.4
Comparison with other data integration services
3.5
Getting Started with Azure Data Factory
3.6
Setting up an Azure Data Factory instance
3.7
Exploring the Azure Data Factory user interface
3.8
Data Movement in Azure Data Factory
3.9
Copying data from various sources to destinations
3.10
Transforming data during the copy process
3.11
Data Orchestration in Azure Data Factory
3.12
Creating and managing data pipelines
3.13
Monitoring and managing pipeline runs
3.14
Data Integration with Azure Data Factory
3.15
Using datasets and linked services
3.16
Building complex data integration workflows
3.17
Data Transformation in Azure Data Factory
3.18
Using data flows for data transformation
3.19
Transforming data using mapping data flows
3.20
Integration with Azure Services
3.21
Integrating Azure Data Factory with other Azure services like Azure Blob Storage, Azure SQL Database, etc.
3.22
Using Azure Data Factory with Azure Databricks for advanced data processing
3.23
Monitoring and Management
3.24
Monitoring pipeline and activity runs
3.25
Managing and optimizing data pipelines for performance
MySQL
27
4.1
SQL Advance Queries
4.2
SQL Data Models
4.3
SQL
4.4
Overview of Azure Data
4.5
Factory and Its Features
4.6
Comparison and Other Data Integration Services
4.7
Getting Started with Azure Data Factory
4.8
Setting up an Azure Data Factory instance
4.9
Exploring the Azure Data Factory user interface
4.10
Data Movement in Azure Data Factory
4.11
Copying data from various sources to destinations
4.12
Transforming data during the copy process
4.13
Data Orchestration in Azure Data Factory
4.14
Creating and managing data pipelines
4.15
Monitoring and managing pipeline runs
4.16
Data Integration with Azure Data Factory
4.17
Using datasets and linked services
4.18
Building complex data integration workflows
4.19
Data Transformation in Azure Data Factory
4.20
Using data flows for data transformation
4.21
Transforming data using mapping data flows
4.22
Integration with Azure Services
4.23
Integrating Azure Data Factory with other Azure services like Azure Blob Storage, Azure SQL Database, etc.
4.24
Using Azure Data Factory with Azure Databricks for advanced data processing
4.25
Monitoring and Management
4.26
Monitoring pipeline and activity runs
4.27
Managing and optimizing data pipelines for performance
Data Warehouse
11
5.1
Data Modeling: Designing the structure of the data warehouse, including defining dimensions, facts, and relationships between them.
5.2
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.
5.3
Dimensional Modeling: A technique for designing databases that are optimized for querying and analyzing data, often used in data warehousing.
5.4
Star and Snowflake Schema: Common dimensional modeling schemas used in data warehousing to organize data into a central fact table and related dimension tables.
5.5
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.
5.6
Fact Table: A table in a data warehouse that contains the primary data for analysis, typically containing metrics or facts that can be analyzed.
5.7
Dimension Table: A table in a data warehouse that contains descriptive information about the data, such as time, location, or product details.
5.8
ETL Tools: Software tools used to extract data from various sources, transform it into a usable format, and load it into the data warehouse.
5.9
Data Quality: Ensuring that data is accurate, consistent, and reliable, often through processes such as data cleansing and validation.
5.10
Data Governance: Policies and procedures for managing data assets, ensuring data quality, and ensuring compliance with regulations and standards.
5.11
Data Warehouse Architecture: The overall structure and components of a data warehouse, including data sources, ETL processes, storage, and access layers.
Azure Data Bricks
33
6.1
Introduction to Azure Databricks
6.2
Overview of Azure Databricks and its features
6.3
Benefits of using Azure Databricks for data engineering and data science
6.4
Getting Started with Azure Databricks
6.5
Creating an Azure Databricks workspace
6.6
Overview of the Azure Databricks workspace interface
6.7
Apache Spark Basics
6.8
Introduction to Apache Spark
6.9
Understanding Spark RDDs, DataFrames, and Datasets
6.10
Working with Azure Databricks Notebooks
6.11
Creating and managing notebooks in Azure Databricks
6.12
Writing and executing Spark code in notebooks
6.13
Data Exploration and Preparation
6.14
Loading and saving data in Azure Databricks
6.15
Data exploration and basic data cleaning using Spark
6.16
Data Processing with Spark
6.17
Performing data transformations using Spark SQL and DataFrame API
6.18
Working with structured and semi-structured data
6.19
Advanced Analytics with Azure Databricks
6.20
Running machine learning algorithms using MLlib in Azure Databricks
6.21
Visualizing data and results in Azure Databricks
6.22
Optimizing Performance
6.23
Best practices for optimizing Spark jobs in Azure Databricks
6.24
Understanding and tuning Spark configurations
6.25
Integration with Azure Services
6.26
Integrating Azure Databricks with Azure Storage (e.g., Azure Blob Storage, Azure Data Lake Storage)
6.27
Using Azure Databricks in conjunction with other Azure services (e.g., Azure SQL Database, Azure Cosmos DB)
6.28
Collaboration and Version Control
6.29
Collaborating with team members using Azure Databricks
6.30
Using version control with Azure Databricks notebooks
6.31
Real-time Data Processing
6.32
Processing streaming data using Spark Streaming in Azure Databricks
6.33
Building real-time data pipelines
Azure Synapse Analytics
10
7.1
Introduction to Azure Synapse Analytics
7.2
What is Synapse Analytics Service?
7.3
Create Dedicated SQL Pool Explore Synapse Studio V2
7.4
Analyse Data using Apache Spark Notebook
7.5
Analyse Data using Dedicated SQL Pool
7.6
Monitor Synapse Studio
7.7
Apache Spark
7.8
Introduction of Spark
7.9
Spark Architecture
7.10
PySpark
Azure Data Engineering Course In Gurgaon
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Understanding and tuning Spark configurations
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