Data Engineering on Microsoft Azure

Course DP-203T00

  • Duration:
    • 4 days

Dates:

  • Implementation guarantee - still places available
  • Implementation - probability high - still places available
  • There are no more seats available. For many courses, it may still be possible to participate online, via virtual classroom.
  • Course times: As a rule, our seminars are held from 10:00 am to 5:00 pm on day 1 and from 9:00 am to 4:00 pm on the following days. Changes are possible. The concrete seminar times you will find in the binding order confirmation.
  • Dates with this symbol are bookable both on-site and in the Virtual Classroom.
24.06.2024 - 27.06.2024 Munich, Virtual Classroom
  • 2390 EUR / Person
 German
01.07.2024 - 04.07.2024 Cologne, Virtual Classroom
  • 2390 EUR / Person
 German
05.08.2024 - 08.08.2024 Virtual Classroom
  • 2390 EUR / Person
 German
02.09.2024 - 05.09.2024 Leipzig, Virtual Classroom
  • 2390 EUR / Person
 German
21.10.2024 - 24.10.2024 Berlin, Virtual Classroom
  • 2390 EUR / Person
 German
11.11.2024 - 14.11.2024 Virtual Classroom
  • 2390 EUR / Person
 German
16.12.2024 - 19.12.2024 Munich, Virtual Classroom
  • 2390 EUR / Person
 German
06.01.2025 - 09.01.2025 Virtual Classroom
  • 2390 EUR / Person
 German
03.02.2025 - 06.02.2025 Leipzig, Virtual Classroom
  • 2390 EUR / Person
 German
17.03.2025 - 20.03.2025 Cologne, Virtual Classroom
  • 2390 EUR / Person
 German
07.04.2025 - 10.04.2025 Virtual Classroom
  • 2390 EUR / Person
 German
05.05.2025 - 08.05.2025 Munich, Virtual Classroom
  • 2390 EUR / Person
 German
23.06.2025 - 26.06.2025 Berlin, Virtual Classroom
  • 2390 EUR / Person
 German
In this course, students will learn about data science as it relates to working with batch and real-time analytics solutions using Azure data platform technologies. Participants will begin with fundamentals of the key computing and storage technologies used to create an analytical solution. Participants will learn how to interactively explore data stored in files in a sea of data. They will learn the different collection techniques that can be used to load data using the Apache Spark function in Azure Synapse Analytics or Azure Databricks, and how to perform collection using Azure Data Factory or Azure Synapse Pipelines. Attendees will also learn about the different ways to transform data using the same technologies that are used to ingest it. They will understand the importance of implementing security to ensure that data is protected at rest or in transit. You will then be shown how to create a real-time analytics system to create real-time analytics solutions.

In this course, participants will gain the following skills:
  • Explore compute and storage options for data science workloads in Azure.
  • Run interactive queries using serverless SQL pools
  • Perform data exploration and transformation in Azure Databricks
  • Explore, transform and load data in the data warehouse using Apache Spark
  • Capturing and loading data in the data warehouse
  • Transforming data with Azure Data Factory or Azure Synapse pipelines
  • Integrate data from notebooks with Azure Data Factory or Azure Synapse pipelines
  • Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
  • Implement end-to-end security with Azure Synapse Analytics
  • Perform real-time stream processing with Stream Analytics
  • Build a stream processing solution with Event Hubs and Azure Databricks
Exploring compute and storage options for data science workloads.
  • Introducing Azure Synapse Analytics
  • Describing Azure Databricks
  • Introducing Azure Data Lake Storage
  • Describing the Delta Lake architecture
  • Working with data streams using Azure Stream Analytics
Running interactive queries using Azure Synapse Analytics serverless SQL pools
  • Learn about serverless SQL pool features in Azure Synapse
  • Query data in the lake using Azure Synapse serverless SQL pools
  • Creating metadata objects in Azure Synapse serverless SQL pools
  • Protecting data and managing users in Azure Synapse serverless SQL pools
Explore and transform data in Azure Databricks
  • Writing to Azure Databricks
  • Reading and writing data in Azure Databricks
  • Working with DataFrames in Azure Databricks
  • Working with advanced methods for DataFrames in Azure Databricks
Exploring, transforming and loading data in the data warehouse using Apache Spark
  • Basic Big Data development with Apache Spark in Azure Synapse Analytics
  • Capturing data with Apache Spark notebooks in Azure Synapse Analytics
  • Transforming data with dataframes into Apache Spark pools in Azure Synapse Analytics
  • Integrating SQL and Apache Spark pools in Azure Synapse Analytics
Capturing and loading data into the data warehouse
  • Using best practices to load data into Azure Synapse Analytics
  • Capture petabyte-scale data with Azure Data Factory
Transforming data with Azure Data Factory or Azure Synapse Pipelines
  • Integrate data with Azure Data Factory or Azure Synapse Pipelines
  • Transform without code at scale with Azure Data Factory or Azure Synapse pipelines
Orchestrating data movement and transformation in Azure Synapse pipelines
  • Orchestrate data movement and transformation in Azure Data Factory
End-to-end security with Azure Synapse Analytics
  • Protecting a data warehouse database in Azure Synapse Analytics
  • Configure and manage secrets in Azure Key Vault
  • Implement compliance controls for confidential data
Supporting Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
  • Design hybrid transactional and analytical processing using Azure Synapse Analytics
  • Configuring Azure Synapse Link with Azure Cosmos DB
  • Querying Azure Cosmos DB with Apache Spark pools
  • Querying Azure Cosmos DB with Serverless SQL Pools
Real-time stream processing with Stream Analytics
  • Enabling reliable messaging for Big Data applications using Azure Event Hubs
  • Work with data streams using Azure Stream Analytics
  • Capture data streams with Azure Stream Analytics
Build a stream processing solution with Event Hubs and Azure Databricks
  • Processing streaming data with Structured Streaming in Azure Databricks
The primary audience for this course is Data Scientists, Data Architects and Business Intelligence professionals who want to learn about data science and how to build analytics solutions using the data platform technologies in Microsoft Azure. The secondary audience for this course is Data Analysts and Data Scientists who use Microsoft Azure-based analytics solutions.
Successful students will begin this course with knowledge of cloud computing and core data concepts, as well as professional experience in data solutions.

Specifically, the following courses must be completed:
This course prepares you for the DP-203: Data Engineering on Microsoft Azure exam.
The course price includes:
- The original Microsoft training materials in English and digital form.
- Refreshments during breaks: drinks, biscuits and lunch.

We are also happy to conduct this training as an in-house seminar. Please request your individual offer.

The course is offered in German and English.

Please click here to go to our English course: DP-203T00_e - Data Engineering on Microsoft Azure (English).

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