Azure Data Engineering Training & Certification

Topics

What is Data Engineering

Data Engineer Roles & Responsibilities

Difference Between ETL Developer & Data Engineer

Types of Data

Steaming Vs Batch Data

Topics

Cloud Introduction and Azure Basics

Azure Implementation Models: IaaS, PaaS, SaaS

Overview of Azure Data Engineer Role

Understanding Azure Storage Components

Introduction to Azure ETL & Streaming Components

Topics

Azure SQL Server and Database Deployment

DTU vs. DWU: Understanding Performance Levels

Managing Firewall Rules and Secure SSMS Connections

Azure Account and Subscription Management

Topics

Azure Resources and Resource Types

Introduction to Azure Data Factory (ADF) and Azure Synapse Analytics

Basic Concepts of Data Movement and Processing

Topics

Synapse SQL Pools (Data Warehousing) and Massively Parallel Processing (MPP)

Data Movement with DMS and SQL Pool Management

Table Creations, Distributions, and Indexing for Performance

Topics

Azure Data Factory Pipeline Architecture and Integration Runtime

Constructing ETL Pipelines with DIU Considerations

Data Flow Activities and Monitoring

Topics

Incremental Data Loading and Handling On-Premise Data Sources

Advanced ADF Features: Data Flows, ETL Logging, and Performance Tuning

Implementing CDC with ADF for Real-Time Data Capture

Topics

Integrating Spark with Synapse Analytics for Big Data Processing

Utilizing Python Notebooks and Spark Pools for Data Analysis

Performance Optimization and Data Transformation Techniques

Topics

Security Measures with Azure Active Directory and Role-Based Access Control

Managing Parameters and Security in Synapse and ADF Pipelines

Utilizing Azure OpenDatasets and Parquet Files for Advanced Analytics

Azure Storage Essentials: Files, Tables, and Queues

Introduction to Azure Data Lake Storage Gen2 (ADLS Gen2)

Configuring and Managing Storage Accounts

Hierarchical Namespace (HNS) and its Advantages

Managing BLOB Storage: Binary Large Objects Explained

Utilizing Azure Storage Explorer for Efficient Storage Management

Directory and File Operations in Azure Data Lake

Best Practices for Organizing Data in ADLS Gen2

Implementing Security Measures in Azure Data Lake Storage

Access Control with Shared Access Signatures (SAS) and Access Control Lists (ACLs)

Role-Based Access Control (RBAC) in Azure Storage

Encryption, Authentication, and Compliance Features

Strategies for SQL Database Migrations to Azure

Integrating Azure SQL with Data Lake Storage

Utilizing Azure Data Factory for Data Movement and Transformation

Data Migration Tools and Techniques

Advanced Concepts in Azure Table Storage

Data Replication and Geo-Redundancy Options

Optimizing Storage Costs and Performance

Leveraging Data Lake for Big Data Analytics

Fundamentals of Azure Stream Analytics

Developing Stream Analytics Jobs for Real-Time Insights

Integrating IoT Devices with Azure for Data Streaming

Processing and Analyzing Streaming Data

Understanding Azure Event Hubs for Large-Scale Event Processing

Configuring Event Hubs and Event Hub Namespaces

Connecting Event Hubs with Azure Stream Analytics

Patterns for Real-Time and Event-Driven Data Processing

Monitoring Azure Storage and Stream Analytics Resources

Performance Tuning for Azure Data Services

Implementing Disaster Recovery Strategies

Using Azure Monitor and Key Vaults for Operational Excellence

Azure Cloud Overview: Understanding SaaS, PaaS, IaaS

Introduction to Azure Databricks: Configuration, Compute Resources, and Workspace Usage

Spark Clusters in Azure Databricks: Configurations, Types, and Resource Management

Databricks File System (DBFS): Utilizing Files and Tables with Spark

Integrating Python with Spark: PySpark Basics

Data Loading Techniques: Using PySpark for Data Ingestion and Processing

Utilizing SQL in Databricks: Creating and Managing Spark Databases and Tables

Advanced Data Transformation: Working with DataFrames and Spark SQL for Data Analytics

Configuring Azure Data Lake Storage (ADLS) for use with Databricks

Data Management: Reading and Writing Data to ADLS using PySpark and Scala

Secure Data Access: Managing Access and Security between Databricks and ADLS

Understanding Databricks Architecture: Driver and Worker Nodes, RDDs, and DAGs

Building and Monitoring Databricks Jobs: Scheduling, Task Management, and Optimization

Implementing Delta Lake for Reliable Data Lakes: ACID Transactions and Performance Tuning

Machine Learning Fundamentals in Databricks: Using MLlib for Predictive Modeling

Data Exploration and Visualization: Leveraging Notebooks for Insights

Advanced Analytic Techniques: Utilizing Scala and Python for Complex Data Analysis

Databricks Security: Integrating with Azure Active Directory (AD)

Managing Permissions: Workspace, Notebooks, and Data Security

Compliance and Data Governance: Best Practices in Databricks Environments

Streaming Data with Databricks: Concepts and Practical Applications

Integrating Azure Event Hubs with Databricks for Real-Time Analytics

Processing Live Data Streams: Building and Deploying Stream Analytics Solutions

Automating Workflows with Azure Logic Apps and Databricks

CI/CD for Databricks: Automation and Version Control Integration

Deployment Strategies: Best Practices for Production Deployments in Azure

Topics

1. Introduction to Python

Overview of Python's history, key features, and comparison with other languages.

Setting up the Python environment, writing your first program. 2. Core Programming Concepts

Variables, data types, conditional statements, loops, control flow.

Introduction to strings, string manipulation, and basic functions.

Topics:

1. Deep Dive into Collections

Understanding lists, tuples, dictionaries, sets, and frozen sets.

Functions, methods, and comprehensions for collections.

2. Functional Programming in Python

Exploring function arguments, anonymous functions, and special functions (map, reduce, filter).

3. Object-Oriented Programming (OOP)

Classes, objects, constructors, destructors, inheritance, polymorphism.

Encapsulation, data hiding, magic methods, and operator overloading.

Topics:

1. Mastering Exception Handling

Exception handling mechanisms, try & finally clauses, user-defined exceptions.

2. File Handling Essentials

Basics of file operations, handling Excel and CSV files.

3. Database Programming

Introduction to database connections and operations with MySQL.

Topics:

1. Getting Started with Flask

Setting up Flask, creating simple applications, routing, and middleware.

2. Exploring Django

Introduction to Django, MVC model, views, URL mapping.

Topics:

1. Automation and Scripting

Enhancing file handling, database automation, and web scraping with BeautifulSoup.

2. GUI Development with TKinter

Basics of TKinter for developing desktop applications.

3. Version Control with Git

Managing projects with Git, understanding repository management, commits, merging, and basic Git commands.

Topics

Cloud Computing Fundamentals: Overview of cloud service models (IaaS, PaaS, SaaS) and deployment models (public, private, hybrid).

Basics of DevOps: Understanding the DevOps culture, practices, and its significance in cloud environments.

Data on the Cloud: Exploring cloud storage solutions, databases, and big data services provided by major cloud providers (AWS, Azure, Google Cloud).

Introduction to Infrastructure as Code (IaC): Concepts and tools for managing infrastructure through code.

Topics:

Cloud Storage Solutions: Differences between object storage, file storage, and block storage. Use cases for each.

Cloud Databases: Overview of relational and NoSQL database services in the cloud (e.g., AWS RDS, Azure SQL Database, Google Cloud Firestore).

Data Warehousing and Big Data Solutions: Introduction to cloud-based data warehousing services (e.g., Amazon Redshift, Google BigQuery, Azure Synapse Analytics).

Data Migration to Cloud: Strategies and tools for migrating data to cloud environments.

Topics:

Automated Data Pipelines: Designing and implementing automated data pipelines using cloud services.

Continuous Integration and Continuous Delivery (CI/CD) for Data: Applying CI/CD practices to data pipeline development, including version control, testing, and deployment strategies.

Monitoring and Logging: Tools and practices for monitoring cloud resources and data pipelines, understanding logs and metrics for troubleshooting.

Infrastructure as Code (IaC) for Data Systems: Using IaC tools (e.g., Terraform, CloudFormation) to provision and manage cloud data infrastructure.

Topics:

Serverless Data Processing: Leveraging serverless architectures for data processing tasks (e.g., AWS Lambda, Azure Functions).

Containerization and Data Services: Using containers (e.g., Docker, Kubernetes) for deploying and scaling data applications and services in the cloud.

Machine Learning and AI in the Cloud: Introduction to cloud-based machine learning services and integrating AI capabilities into data pipelines.

Data Analytics and Visualization: Tools and services for analyzing and visualizing data directly in the cloud (e.g., Amazon QuickSight, Google Data Studio, Power BI on Azure).

Topics:

Introduction to Databases and SQL: Understanding relational databases and the role of SQL.

SQL Syntax Overview: Keywords, statements, and clauses.

Basic SQL Commands: SELECT, FROM, WHERE, and ORDER BY.

Filtering Data: Using conditions to retrieve specific data (AND, OR, NOT).

Topics:

Understanding Table Relationships: Primary keys, foreign keys, and the importance of relationships in databases.

Join Operations: INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

Subqueries and Nested Queries: Using subqueries in the SELECT, FROM, and WHERE clauses.

Aggregating Data: Using GROUP BY and aggregate functions (COUNT, SUM, AVG, MIN, MAX).

Topics:

Data Manipulation Commands: INSERT, UPDATE, DELETE.

Managing Tables: Creating and altering tables (CREATE TABLE, ALTER TABLE, DROP TABLE).

Advanced Filtering Techniques: Using LIKE, IN, BETWEEN, and wildcard characters.

Working with Dates and Times: Understanding and manipulating date and time data.

Topics:

Advanced SQL Functions: String functions, mathematical functions, and date functions.

Window Functions: Overviews of ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG, and their applications.

Query Performance Optimization: Indexes, query planning, and execution paths.

Common Table Expressions (CTEs): Writing cleaner and more readable queries with WITH clause.

Topics:

Analytical SQL for Reporting: Building complex queries to answer analytical questions.

Pivoting Data: Transforming rows to columns (PIVOT) and columns to rows (UNPIVOT).

Data Warehousing Concepts: Introduction to data warehousing practices and how they apply to SQL querying.

Integrating SQL with Data Analysis Tools: Connecting SQL databases with tools like Excel, Power BI, and Python for deeper data analysis.