Designing an Azure Data Solution (DP-201). 29520 - Seminar / Kurs von TÜV Rheinland Akademie GmbH

Seminar 2 im Data Engineer Track

Inhalte

In diesem Kurs werden die Teilnehmer mittels verschiedener Data Platform TechnologienLösungen entwerfen, die mit betrieblichen und technischen Anforderungen übereinstimmen. Dazu gehören on-premises, Cloud und Hybride Datenszenarien die relationale, NoSQL oder Data Warehouse Daten einbeziehen.Module 1: Data Platform Architecture Considerations In this module, the students will learn how to design and build secure, scalable, and performant solutions in Azure by examining the core principles found in every good architecture. They will learn how using key principles throughout architecture, regardless of technology choice, can help you design, build, and continuously improve the architecture for an organization's benefit. Core Principles of Creating ArchitecturesDesign with Security in MindPerformance and ScalabilityDesign for Availability and RecoverabilityDesign for Efficiency and OperationsCase Study Module 2: Azure Batch Processing Reference Architectures In this module, students will learn the reference design and architecture patterns for dealing with the batch processing of data. The student will be exposed to dealing with the movement of data from on-premises systems into a cloud data warehouse and how it can be automated. The students will also be exposed to an AI architecture and how the data platform can integrate with an AI solution. Lambda Architectures from a Batch Mode PerspectiveDesign an Enterprise BI solution in AzureAutomate Enterprise BI solutions in AzureArchitect an Enterprise-grade Conversational Bot in Azure Module 3: Azure Real-Time Reference Architectures In this module, the students will learn the reference design and architecture patterns for dealing with streaming data. They will learn how streaming data can be ingested by Event Hubs and Stream Analytics to deliver real-time analysis of data. They will also explore a data science architecture that streams data into Azure Databricks to perform trend analysis. They will finally learn how an Internet of Things (IoT) architecture will require data platform technologies to store data. Describe Lambda Architectures for a Real-Time PerspectiveArchitect a Stream Processing Pipeline with Azure Stream AnalyticsDesign a Stream Processing Pipeline with Azure DatabricksCreate an Azure IoT Reference Architecture Module 4: Data Platform Security Design Considerations In this module, the students will learn how to incorporate security into an architecture design and learn the key decision points in Azure provided to help create a secure environment through all the layers of architecture. Defense in Depth Security ApproachIdentity ManagementInfrastructure ProtectionEncryption UsageNetwork Level ProtectionApplication Security Module 5: Designing for Resiliency and Scale In this module, students will learn scaling services to handle load. They will learn how identifying network bottlenecks and optimizing storage performance are important to ensure users have the best experience. They will also learn how to handle infrastructure and service failure, recover from the loss of data, and recover from a disaster by designing availability and recoverability into the architecture. Adjust Workload Capacity by ScalingOptimize Network PerformanceDesign for Optimized Storage and Database PerformanceIdentify Performance BottlenecksDesign a Highly Available SolutionIncorporate Disaster Recovery into ArchitecturesDesign Backup and Restore Strategies Module 6: Design for Efficiency and Operations In this module, students will learn how to design an Azure architecture that is operationally-efficient and minimizes costs by reducing spend and they will understand how to design architectures that eliminate waste and gives them full visibility into what is being utilized in the organization's Azure environment. Maximizing the Efficiency of your Cloud EnvironmentUse Monitoring and Analytics to Gain Operational InsightsUse Automation to Reduce Effort and Error

Lernziele

Sie lernen, wie Prozessarchitekturen mit einer Reihe an Technologien für Streaming und Batchdaten verarbeitet werden. Die Teilnehmer werden das Entwerfen von Datensicherheit untersuchen, wie Datenzugriff, Datenrichtlinien und Standards. Sie werden Azure Datenlösungen entwerfen, dazu gehört die Optimierung, das Sicherstellen der Verfügbarkeitsowie ein Disaster Recovery von großen Daten, Batchverarbeitung und Streamingdaten-Lösungen, wie sie bei Big Data Szenarien vorkommen. Dieses Seminar bereitet auch für das zweite Examen DP-201 zum "Microsoft Certified: Azure Data Engineer Associate" vor.

Zielgruppen

Die Zielgruppe für diesen Kurs sind Datenexperten, Data Architects und Engineers sowie Business Intelligence (BI) Experten, die etwas über die Microsoft Azure Data Platform Technologien lernen möchten. Die sekundäre Zielgruppe für diesen Kurs sind Lösungsdeveloper, die Anwendungen entwickeln, welche Inhalte aus den Microsoft Azure Datenplattformtechnologien liefern.

SG-Seminar-Nr.: 5885298

Anbieter-Seminar-Nr.: 29520

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