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Data Engineering on Microsoft Azure (DP-203T00)

 

Course Overview

In this course, the student will learn about the data engineering patterns and practices as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies. Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics.

Who should attend

The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure.

Prerequisites

Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.

Specifically completing:

Course Objectives

  • Explore compute and storage options for data engineering workloads in Azure
  • Design and Implement the serving layer
  • Understand data engineering considerations
  • Run interactive queries using serverless SQL pools
  • Explore, transform, and load data into the Data Warehouse using Apache Spark
  • Perform data Exploration and Transformation in Azure Databricks
  • Ingest and load Data into the Data Warehouse
  • Transform Data with Azure Data Factory or Azure Synapse Pipelines
  • Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines
  • Optimize Query Performance with Dedicated SQL Pools in Azure Synapse
  • Analyze and Optimize Data Warehouse Storage
  • Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
  • Perform end-to-end security with Azure Synapse Analytics
  • Perform real-time Stream Processing with Stream Analytics
  • Create a Stream Processing Solution with Event Hubs and Azure Databricks
  • Build reports using Power BI integration with Azure Synpase Analytics
  • Perform Integrated Machine Learning Processes in Azure Synapse Analytics

Course Content

  • Explore compute and storage options for data engineering workloads
  • Design and implement the serving layer
  • Data engineering considerations for source files
  • Run interactive queries using Azure Synapse Analytics serverless SQL pools
  • Explore, transform, and load data into the Data Warehouse using Apache Spark
  • Data exploration and transformation in Azure Databricks
  • Ingest and load data into the data warehouse
  • Transform data with Azure Data Factory or Azure Synapse Pipelines
  • Orchestrate data movement and transformation in Azure Synapse Pipelines
  • Optimize query performance with dedicated SQL pools in Azure Synapse
  • Analyze and Optimize Data Warehouse Storage
  • Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
  • End-to-end security with Azure Synapse Analytics
  • Real-time Stream Processing with Stream Analytics
  • Create a Stream Processing Solution with Event Hubs and Azure Databricks
  • Build reports using Power BI integration with Azure Synpase Analytics
  • Perform Integrated Machine Learning Processes in Azure Synapse Analytics
Online Training

Duration 4 days

Digital courseware
Classroom Training

Duration 4 days

Price (excl. tax)
  • Germany:
    Country: DE
    2,290.- €
  • Switzerland:
    Country: CH
    CHF 2,980.-
incl. catering
Catering includes:

  • Coffee, Tea, Juice, Water, Soft drinks
  • Pastry and Sweets
  • Bread
  • Fresh fruits
  • Lunch in a nearby restaurant

* Catering information only valid for courses delivered by iTLS.


Digital courseware

Schedule

Europe
Austria

Currently no local training dates available.  For enquiries please write to info@itls.at.

Germany
Frankfurt This is an German language FLEX course.
Time zone: Central European Summer Time (CEST)
Munich This is an German language FLEX course.
Time zone: Central European Summer Time (CEST)
Hamburg This is an German language FLEX course.
Time zone: Central European Summer Time (CEST)
Berlin This is an German language FLEX course.
Time zone: Central European Summer Time (CEST)
Frankfurt This is an German language FLEX course.
Time zone: Central European Time (CET)
Switzerland
Zurich
Zurich
Zurich
Zurich
Zurich
Italy
Rome This is an Italian language FLEX course.
Time zone: Central European Summer Time (CEST)
Milan This is an Italian language FLEX course.
Time zone: Central European Summer Time (CEST)
Rome This is an Italian language FLEX course.
Time zone: Central European Time (CET)
This is a FLEX course, which is delivered both virtually and in the classroom. All FLEX courses are also Instructor-led Online Trainings (ILO). Until 30.06. we offer our courses also as online trainings.
English
Time zone Central European Summer Time (CEST)
Online Training Time zone: Central European Summer Time (CEST)
Online Training Time zone: Eastern European Time (EET)
1 hour difference
Online Training Time zone: Eastern European Time (EET)
2 hours difference
Online Training Time zone: Gulf Standard Time (GST) Guaranteed date!
Online Training Time zone: Gulf Standard Time (GST)
Guaranteed date:   iTLS will carry out all guaranteed training regardless of the number of attendees, exempt from force majeure or other unexpected events, like e.g. accidents or illness of the trainer, which prevent the course from being conducted.
Instructor-led Online Training:   This computer icon in the schedule indicates that this date/time will be conducted as Instructor-Led Online Training.
This is a FLEX course, which is delivered both virtually and in the classroom. All FLEX courses are also Instructor-led Online Trainings (ILO). Until 30.06. we offer our courses also as online trainings.