Azure Data Engineer with Scrum Master (PSM I)

Azure Data Engineers are responsible for integrating data from different data systems and managing and maintaining data processing pipelines. With Scrum, you will also learn a project management framework based on agile principles and find out how artificial intelligence is used in the profession.
  • Certificates: Certificate "Azure Data Engineer"
    Certificate "Professional Scrum Master (PSM I) from Scrum.org"
  • Additional Certificates: Original Microsoft certificate "Microsoft Certified: Azure Administrator Associate"
    "Relational Databases – SQL" Certificate
    Certificate "PCEP™ - Certified Entry-Level Python Programmer"
    Data Engineer" certificate
  • Examination: Praxisbezogene Projektarbeiten mit Abschlusspräsentationen
    Microsoft-Zertifizierungsprüfung AZ-104: Azure Administrator
    Certified Entry-Level Python Programmer (PCEP™) (in englischer Sprache)
    Scrum.org-Zertifizierung PSM I - Professional Scrum Master (in englischer Sprache)
  • Teaching Times: Full-time
    Monday to Friday from 8:30 a.m. to 3:35 p.m. (in weeks with public holidays from 8:30 a.m. to 5:10 p.m.)
  • Language of Instruction: German
  • Duration: 20 Weeks

Microsoft Azure Administration

Prerequisites for Azure administrators (approx. 1 day)

Azure administration interfaces (Azure Portal, Azure CLI, Azure PowerShell)

Azure Resource Manager (ARM)

Resources and resource groups

Azure Resource Manager Templates (ARM) and Bicep

Use of AI tools to support administrative tasks and scripting


Managing Azure identities and governance (approx. 2 days)

Microsoft Entra ID

Users and groups

Device settings

Mass user updates

Guest accounts

Self-service password

Role-based access control (RBAC)

Access assignments

Managed identities for Azure resources

Directories

Subscriptions and governance: Azure policies, resources, tags

Cost management

Management groups

 


Configure and manage virtual networks (approx. 4.5 days)

Virtual networks

VNet peering

Private and public IP addresses, network routes, network interfaces, subnets and virtual networks

Name resolution: Azure DNS

Secure access to virtual networks and resources

Network Security Groups (NSG) for subnets and network interfaces

Azure Bastion

Azure Application Gateway (Layer 7 load balancing)

Local connectivity

Connection Monitor

Network Watcher

Network diagnostics

Integrate a local network into an Azure virtual network

ExpressRoute

Azure Virtual WAN


Implement and manage storage space (approx. 2 days)

Storage accounts

Shared Access Signatures (SAS)

Access keys

Azure storage replication

Microsoft Entra ID authentication

Azure Storage Explorer

AZCopy

Azure Files and Azure Blob Storage

Azure File Sharing

Azure File Sync


Provisioning and managing Azure computing resources (approx. 3.5 days)

Azure Virtual Machines (VMs) for high availability and scalability

Azure Resource Manager Templates (ARM) and Bicep

VHD template

Azure hard disk encryption

Azure Key Vault for keys and secrets

VM sizes

Adding disks

Availability zones and availability sets

Configuring the network

Containers

Container apps

Azure Container Instances (ACI)

Web apps


Monitoring and securing Azure resources (approx. 2 days)

Azure Monitor

metrics

Log Analytics

Diagnostic settings

Application Insights

Support for analyzing logs and error messages using AI tools

Backup and restore processes

Backup reports

Azure Backup

Soft delete process

Backup policies

Azure Site Recovery


Project work (approx. 5 days)

To consolidate the content learned

Presentation of the results

Certification exam AZ-104: Microsoft Azure Administrator

Relational databases with SQL

Basics of database systems and SQL (approx. 3 days)

Overview of database systems and models

Redundant data and data integrity

Normalization

Database design and entity relationship model (ERM)

Primary and foreign keys

Relationships between relations

Data types in SQL

Indexes and performance

Constraints and validation

Queries in SQL

Structured data as the basis for AI-supported analysis methods


Introduction to SQL Server Management Studio (SSMS) (approx. 2 days)

Overview of SQL Server and SSMS

Physical database design

Creating tables and defining data types

Constraints, default values and relationships

Database diagrams (ERM) and relationships

Backup and restore

Introduction to performance monitoring

Overview of AI-supported query optimization and query analysis


Introduction to DDL (Data Definition Language) and DML (Data Manipulation Language) (approx. 8 days)

SQL basics and extended syntax

Operators and integrated functions

Queries and manipulation of data

Error handling and transaction management

Creation and administration of database objects

Basics of performance optimization

Working with modern data types

Data modeling and structured preparation for AI and analysis applications


DCL - Data Control Language and Security (approx. 1 day)

User administration and authorizations

Roles and security concepts

Auditing

Introduction to Row Level Security

Data security in the context of AI-supported evaluations


Data types, data import and export in modern systems (approx. 1 day)

Data import and export

Modern data types

Import, transformation and provision of data for analysis processes


Project work (approx. 5 days)

To consolidate the content learned

Presentation of the project results

Programming with Python

Python basics (approx. 1 day)

History, concepts

Usage and areas of application

syntax

Lexis, semantics

PEP-8 conventions

Interpreter vs. compiler

Numeral systems: binary, octal, hexadecimal

Scientific Notation


First steps with Python (approx. 5 days)

Numbers

Strings

Date and time

Standard input and output

Numeric operators

Comparison, logical and bitwise operators

Data type conversion

list, tuple, dict, set

List functions and methods

Branching and loops (if, for, while)

Member operators

String basics: escaping, multiline strings

Prioritizing and binding operators


Artificial intelligence (AI) in the work process

Presentation of specific AI technologies

and possible applications in the professional environment


Functions (approx. 5 days)

Define your own functions

Variables

Parameters and arguments

Return values

Recursion

Namespaces

Functional programming

Parameter types: positional, keyword, mixed

Default values

Shadowing and global keyword

None and return without value


Troubleshooting (approx. 0.5 days)

Basics of error handling with try and except

Typical error types and exception hierarchy

Error propagation and program interruptions

Structuring the except blocks


Object-oriented programming (approx. 4.5 days)

Python classes

Methods

Immutable objects

Data class

Inheritance


Project work, certification preparation and certification exam "PCEP™ - Certified Entry-Level Python Programmer" in English (approx. 4 days)

Data Engineer

Fundamentals of Data Engineering and Data Architectures (approx. 2 days)

Tasks and Application Areas in Data Engineering

Fundamentals of Modern Data Architectures and Data Platforms

Fundamentals and Comparison of Cloud-Based Data Architectures

OLTP and OLAP

Processing Structured, Semi-Structured, and Unstructured Data


Requirements Management and Data Modeling (approx. 1 day)

Requirements for Data and Data Sources

Conceptual Data Modeling

Modeling Data Structures and Data Flows

Data Contracts, Data Governance, and Data Lineage


Databases and Data Processing (approx. 1 day)

Database System Architecture

SQL vs. NoSQL vs. NewSQL: A Comparison

Relational Data Modeling

SQL for Querying and Processing Data


Data Warehousing and Modern Data Architectures (approx. 2 days)

Fundamentals and Architecture of Data Warehouse Systems

Dimensional Data Modeling and Schema Design

Facts, Dimensions, and Historical Data Storage

Comparing Data Warehouses, Data Lakes, and Lakehouses

Introduction to dbt

Practical Implementation of Data Models


Data Integration and Data Pipelines (approx. 5 days)

Data Integration and Data Provisioning

ETL and ELT Processes

Design and Orchestration of Data Pipelines

Data Cleaning, Validation, and Data Quality

Batch and Streaming Processing

Practical Application of Airflow and Spark


Programming and Automated Data Processing (approx. 4 days)

Python Environments and Package Management

Data Processing and Transformation with Python

Automation of data processing workflows

Connecting to Different Data Sources and Interfaces

Development of data engineering workflows

Containerization with Docker

AI-powered support in the development and data processing process


Project work (approx. 5 days)

To consolidate the content learned

Presentation of the project results

Agile project management with Scrum: Master (PSM I)

Fundamentals (approx. 3 days)

Agile Mindset

Agile Project Management: Overview

Differences and Additions Compared to Traditional Project Management Methods

Project Management Methods

Typical workflow of an agile project

Strengths and Weaknesses of Agile Project Management

Artificial Intelligence in an Agile Work Environment


Prerequisites and Framework Conditions for Agile Projects (approx. 5 days)

Project Context, Values, and Principles

Technical Requirements for Agile Projects in IT

Applicability of Agile Methods to Projects Outside of IT

Impact of AI on project requirements and work processes


The Scrum Framework (approx. 3 days)

Scrum Philosophy

The Three Pillars of Empiricism (Transparency, Inspection, Adaptation)

The Various Roles in Scrum and Their Responsibilities: Scrum Master, Developer, Product Owner

Self-Organized Teams

The Scrum Meetings: Sprint Planning, Daily Scrum, Sprint Review, Sprint Retrospective

Scrum Artifacts: Product Backlog, Sprint Backlog, Increment

Planning with Scrum

Impact on Organizations

Potential Applications of AI to Support Scrum Artifacts and Meetings


Project Management (approx. 2 days)

Stakeholder Management

Problem Identification and Resolution

Nexus: Framework for scaling Scrum

Support through data-driven evaluations and analysis tools


Key Factor: The Team (approx. 2 days)

Framework Conditions for Agile Teams

Responsibility, Collaboration, and Commitment in an Agile Team

Effective Team and Self-Management

Communication Within the Team

The Impact of Assistive Tools on Collaboration and Communication Within the Team


Project work, certification preparation and Scrum.org Professional Scrum Master certification (PSM I) in English (approx. 5 days)



Changes are possible, the course content is updated regularly.

Basic knowledge of Azure administration and a good knowledge of English for the Scrum and Python certification exam is required.

After this course, you will have in-depth knowledge of Azure configuration and administration. Python and SQL skills complete your profile and you can combine these with the tasks of Data Engineers.

In addition, you are proficient in the Scrum process and support product owners in process management and improvement. You are able to organize and moderate Agile/Scrum meetings and implement sprints and are familiar with Scrum artifacts.

IT and network specialists, (specialist) computer scientists, people with practical experience and good knowledge of IT.

As companies need to manage and structure ever-increasing amounts of data to evaluate and target their business processes, skills in data development and construction are in demand in all industries.

With Scrum, you will also learn a project and product management process model that was originally used for agile software development in particular, but is now also used in many other specialist areas. As a scalable project management and development method, it is used successfully in numerous large-scale projects with several hundred team members. The official certificate from Scrum.org provides you with internationally recognized proof of your qualifications as a Scrum Master.

Didactic concept

Your lecturers are highly qualified both professionally and didactically and will teach you from the first to the last day (no self-study system).

You will learn in effective small groups. The courses usually consist of 6 to 25 participants. The general lessons are supplemented by numerous practical exercises in all course modules. The practice phase is an important part of the course, as it is during this time that you process what you have just learned and gain confidence and routine in its application. The final section of the course involves a project, a case study or a final exam.

 

Virtual classroom alfaview®

Lessons take place using modern alfaview® video technology - either from the comfort of your own home or at our premises at Bildungszentrum. The entire course can see each other face-to-face via alfaview®, communicate with each other in lip-sync voice quality and work on joint projects. Of course, you can also see and talk to your connected trainers live at any time and you will be taught by your lecturers in real time for the entire duration of the course. The lessons are not e-learning, but real live face-to-face lessons via video technology.

 

The courses at alfatraining are funded by Agentur für Arbeit and are certified in accordance with the AZAV approval regulation. When submitting a Bildungsgutscheinor Aktivierungs- und Vermittlungsgutschein, the entire course costs are usually covered by your funding body.
Funding is also possible via Europäischen Sozialfonds (ESF), Deutsche Rentenversicherung (DRV) or regional funding programs. As a regular soldier, you have the option of attending further training courses via Berufsförderungsdienst (BFD). Companies can also have their employees qualified via funding from Agentur für Arbeit (Qualifizierungschancengesetz).

We will gladly advise you free of charge.

0800 3456-500 Mon. - Fri. from 8 am to 5 pm
free of charge from all German networks.

Contact

We will gladly advise you free of charge. 0800 3456-500 Mon. - Fri. from 8 am to 5 pm free of charge from all German networks.