Data Engineering Job Oriented Program

Introduction to Python Libraries
Overview of commonly used libraries in data workflows

NumPy Basic Overview for Understanding array-based operations (foundation level only)

Pandas for Working with tabular data, filtering, and simple transformations

Introduction to cloud computing

Types of Cloud Models

Types of Cloud Service Models

IAAS

SAAS

PAAS

Creation of Microsoft Azure Account

Microsoft Azure Portal Overview

Introduction to IAM in Azure

Why secure identity management is critical for data engineering

What is Microsoft Entra ID (Azure AD)

Understand Tenants, Users, Groups

What is an SPN and when to use it

Differences: SPN vs user vs managed identity

How to create and authenticate with SPN

What is a Managed Identity and how it differs from SPN

What is an Azure Storage Account?

​​Common use cases in Data Engineering

Use cases: data lakes, file ingestion, backup

Blob types: Block, Append, Page

Folder structure & containers

Use cases: metadata storage, audit logs, config tables

Table schema: PartitionKey, RowKey, Timestamp

Azure Queue Storage

Introduction to Azure Data Lake Gen2

File systems, directories, and files

Creating and Configuring ADLS Gen2

Ingesting Data into ADLS Gen2

Accessing Data from ADLS Gen2

What is a REST API?

REST vs SOAP

Tools to Work with APIs

Understanding CRUD Operations

What is ADF and why is it used?

Key components: Pipelines, Datasets, Linked Services, Triggers, Integration Runtime

Azure Data Factory Architecture and Pipeline execution flow

Creating Linked Services by Connecting to Azure Storage, SQL, Data Lake and REST APIs

Creating and Managing Dataset Types like DelimitedText, Parquet, Binary, JSON, SQL Tables

Understand the Data Transformation Pipeline, Movement Pipeline and Activities

Data Pipeline Scheduling and Triggers

Monitoring and Debugging Pipelines

Best Practices to follow in Real World Environment

What is Microsoft Fabric?

How it is different from Microsoft Azure Data Engineering

Microsoft Fabric Components

Understand Dataflows Gen 2 in Microsoft Fabric

Explore and Integrate Dataflows Gen2 in Microsoft Fabric

Integrate Pipelines in Microsoft Fabric

Understand pipelines for data engineering

Use pipeline templates

Run and monitor Pipelines

Introduction to real-time data analytics in Microsoft Fabric

Ingest, Transform, Store and query real-time data

Visualise real-time data in Microsoft Fabric

Introduction to Microsoft Fabric eventhouse

Work with KQL effectively

Explore materialized views and stored functions for Microsoft Fabric Certification

Understand Real World lakehouse architecture for Data Engineering Roles

Use Microsoft Fabric for data ingestion, transformation, and analysis

Manage and utilize lakehouses for Microsoft Fabric Data Engineer Certificationˇ

Comprehend Delta Lake and delta tables within Fabric.

Create and handle delta tables using Spark.

Enhance the performance of delta tables.

Work on delta tables with Spark’s structured streaming.

Define data warehouses within Fabric.

Differentiate between a data warehouse and a data lakehouse.

Work on data warehouses in Microsoft Fabric.

Create and manage fact tables and dimensions in a data warehouse.

Explore strategies for loading data into a Fabric data warehouse.

Construct a data pipeline to populate a warehouse in Fabric.

Load data into a warehouse using T-SQL.

Load and transform data with Dataflows Gen 2.

Manage Data into a Fabric Datawarehouse

Protect Data into a Fabric Datawarehouse

Grasp the basics of CI/CD and their use in Microsoft Fabric.

Configure version control with Git repositories.

Leverage deployment pipelines to streamline the deployment workflow.

Automate CI/CD tasks using Fabric APIs.

Focus: Building strong fundamentals in PySpark for data ingestion, transformation, and analysis.

• Introduction to Databricks
• Understanding SparkSession and execution flow
• Basics of RDD (conceptual overview)
• DataFrames and distributed data processing
• Reading data from sources (CSV, Parquet)
• Writing data to targets using DataFrame APIs
• Data ingestion and transformation using PySpark
• Spark SQL for data processing and analytics
• Working with joins, aggregations, and window functions
• Performance optimization techniques in Spark
• Monitoring jobs using Spark UI

Focus: Understanding Databricks environment, architecture, and data governance.

• Azure Databricks architecture overview
• Managing clusters and cluster pools in Databricks
• Understanding Unity Catalog for data governance and access control
• Managing data assets and permissions using Unity Catalog
• Understanding Delta Lake architecture
• Working with Delta tables in Databricks

Focus: Designing scalable data pipelines and real-world data engineering workflows.

• Implementing Medallion architecture (Bronze, Silver, Gold layers)
• Designing layered data pipelines using Medallion architecture
• Building batch and real-time data pipelines
• End-to-end data processing using Databricks

What is Snowflake?

Snowflake’s use cases in data engineering

Setting up Snowflake

Creating a Snowflake account

Setting up the Snowflake environment

User roles and permissions

Navigating the Snowflake Web UI

Supported data types (BOOLEAN, INTEGER, STRING, etc.)

VARIANT data type for semi-structured data (JSON, XML, Parquet)

Tables (Permanent, Temporary, Transient)

Snowflake Architecture Deep Dive

Cloud Services Layer, Compute Layer, Storage Layer

Micro-partitioning and its benefits

How data is stored and accessed in Snowflake

Time Travel and Fail-safe

Zero Copy Cloning

Snowflake’s automatic scaling and partitioning

Loading Data into Snowflake (Data Engineering)

File formats supported by Snowflake (CSV, JSON, Parquet, Avro)

Using Snowflake’s COPY command

Using Snowflake’s SQL capabilities for ETL

Creating and managing stages

Data Transformation using Streams and Tasks

What are Streams and Tasks?

Implementing real-time ETL pipelines using Snowflake

Automation and scheduling tasks in Snowflake

Snowflake’s Integration with Data Lake and Data Science Tools

Connecting Snowflake to BI tools like Tableau, Looker, Power BI

Understanding virtual warehouses in Snowflake

Optimizing virtual warehouse size and performance

Auto-suspend and auto-resume configurations

Clustering Keys

Query profiling and performance tuning

Caching in Snowflake

Star schema vs Snowflake schema

Authentication and Authorization

Role-based access control (RBAC)

Data encryption at rest and in transit

Auditing and monitoring usage

Setting up data sharing and data masking

Access controls for sensitive data

Sharing data securely with other Snowflake accounts

Using Snowflake’s secure data sharing feature

Data sharing best practices

Introduction of Airflow

Different Components of Airflow

Installing Airflow

Understanding Airflow Web UI

DAG Operators & Tasks in Airflow Job

Create & Schedule Airflow Jobs For Data Processing

Need for Kafka

What is Kafka

Core Concepts of Kafka

Kafka Architecture

Where is Kafka Used

Understanding the Components of Kafka Cluster 

Configuring Kafka Cluster

Hands-On: 

• Configuring Single Node Single Broker Cluster Configuring Single Node Multi-Broker Cluster

• Introduction to Snowflake Cortex AI
• Cortex AI Search Service
• Cortex Analyst
• Document AI for NLP & predictive analytics

• Understanding RAG Architecture from a Data Pipeline Perspective
• How data flows from source systems → embeddings → vector database → LLM
• Role of Data Engineers in building and maintaining RAG data pipelines
• Vector Databases for Data Storage & Retrieval
• Storing and managing embeddings as a new form of data storage
• Similarity search and retrieval mechanisms from a data engineering lens
• Designing and Building Embedding Pipelines
• Processing and Managing Unstructured Data
• Ingesting and transforming PDFs, logs, and text data into usable formats
• Data cleaning and preprocessing for AI-ready pipelines
• Data Ingestion Strategies for AI Applications
• Batch and streaming ingestion for AI systems

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