Microsoft DP-203: Data Engineering on Microsoft Azure eLearning

Word expert in data-engineering op Azure met 25+ uur eLearning, 20+ uur labs en 200 examenvragen - bouw krachtige batch- en real-time analytics met Synapse en Databricks.

Microsoft DP-203: Data Engineering on Microsoft Azure eLearning Deze online cursus leert studenten de belangrijkste patronen en praktijken voor data-engineering met Azure-technologieën, inclusief batch- en real-time analytische oplossingen. De cursus behandelt opslag- en rekentechnologieën, het ontwerpen van een analytische laag, en technieken voor gegevensinvoer, -verkenning en -transformatie met Apache Spark, Azure Synapse en Databricks. De training bereidt deelnemers voor op het Microsoft DP-203 examen (optioneel) en is geschikt voor data-professionals, architecten en BI-specialisten die analytische oplossingen willen bouwen in Azure. Voorkennis van cloud computing en basiskennis van data-oplossingen (AZ-900, DP-900) wordt aanbevolen.



* 12 Months Online Access
* 25+ hours of eLearning
* 20+ hours of Challenge Labs (90 days access)
* Online Mentor
* Measure-Up Exam simulation - 200 questions
* Tips & Tricks

In this CertKit, 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.
 
CONTENT
* Storage Accounts
* Designing Data Storage Structures
* Data Partitioning
* Designing the Serving Layer
* Physical Data Storage Structures
* Logical Data Structures
* The Serving Layer
* Data Policies & Standards
* Securing Data Access
* Securing Data
* Data Lake Storage
* Data Flow Transformation
* Data Factory
* Databricks
* Databrick Processing
* Stream Analytics
* Synapse Analytics
* Data Storage Monitoring
* Data Process Monitoring
* Data Solution Optimization