Corso certificato

 Presentazione del corso

in questo corso Microsoft, DP-203 – Data Engineering on Microsoft Azure, lo studente imparerà i modelli e le pratiche relativi alla ingegneria dei dati per quanto riguarda il lavoro con soluzioni analitiche batch e in tempo reale utilizzando le tecnologie della piattaforma dati di Azure.

Questo corso aiuta gli studenti a prepararsi per l’esame di certificazione DP-203: Data Engineering on Microsoft Azure.

 Obiettivi

Al termine del corso MOC DP-203 – Data Engineering on Microsoft Azure gli allievi saranno in grado di:

  • progettare e implementare l’archiviazione dei dati;
  • pianificare e sviluppare l’elaborazione dei dati;
  • progettare e implementare la sicurezza dei dati;
  • monitorare e ottimizzare l’archiviazione e l’elaborazione dei dati.

 

Prerequisiti

Per partecipare con profitto a questo corso gli allievi dovrebbero aver partecipato ai seguenti corsi oppure possedere competenze equivalenti:

AZ-900 – Azure Fundamentals

DP-900 – Azure Data Fundamentals

 

Destinatari

Il pubblico principale del corso MOC DP-203 – Data Engineering on Microsoft Azure è costituito da professionisti, data architect e professionisti della business intelligence che vogliono imparare non solo l’ingegneria dei dati, ma anche la costruzione di soluzioni analitiche, utilizzando le tecnologie della piattaforma dati che esistono su Microsoft Azure.
Questo corso può essere di interesse anche per una platea costituita da analisti di dati e data scientist che lavorano con soluzioni analitiche costruite su Microsoft Azure.

Contenuti

Module 1: Explore compute and storage options for data engineering workloads

Introduction to Azure Synapse Analytics

Describe Azure Databricks

Introduction to Azure Data Lake storage

Describe Delta Lake architecture

Work with data streams by using Azure Stream Analytics

Module 2: Design and implement the serving layer

Design a multidimensional schema to optimize analytical workloads

Code-free transformation at scale with Azure Data Factory

Populate slowly changing dimensions in Azure Synapse Analytics pipelines

Module 3: Data engineering considerations for source files

Design a Modern Data Warehouse using Azure Synapse Analytics

Secure a data warehouse in Azure Synapse Analytics

Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pool

Explore Azure Synapse serverless SQL pools capabilities

Query data in the lake using Azure Synapse serverless SQL pools

Create metadata objects in Azure Synapse serverless SQL pools

Secure data and manage users in Azure Synapse serverless SQL pools

Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark

Understand big data engineering with Apache Spark in Azure Synapse Analytics

Ingest data with Apache Spark notebooks in Azure Synapse Analytics

Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics

Integrate SQL and Apache Spark pools in Azure Synapse Analytics

Module 6: Data exploration and transformation in Azure Databricks

Describe Azure Databricks

Read and write data in Azure Databricks

Work with DataFrames in Azure Databricks

Work with DataFrames advanced methods in Azure Databricks

Module 7: Ingest and load data into the data warehouse

Use data loading best practices in Azure Synapse Analytics

Petabyte-scale ingestion with Azure Data Factory

Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines

Data integration with Azure Data Factory or Azure Synapse Pipelines

Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines

Orchestrate data movement and transformation in Azure Data Factory

Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse

Optimize data warehouse query performance in Azure Synapse Analytics

Understand data warehouse developer features of Azure Synapse Analytics

Module 11: Analyze and Optimize Data Warehouse Storage

Analyze and optimize data warehouse storage in Azure Synapse Analytics

Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

Design hybrid transactional and analytical processing using Azure Synapse Analytics

Configure Azure Synapse Link with Azure Cosmos DB

Query Azure Cosmos DB with Apache Spark pools

Query Azure Cosmos DB with serverless SQL pools

Module 13: End-to-end security with Azure Synapse Analytics

Secure a data warehouse in Azure Synapse Analytics

Configure and manage secrets in Azure Key Vault

Implement compliance controls for sensitive data

Module 14: Real-time Stream Processing with Stream Analytics

Enable reliable messaging for Big Data applications using Azure Event Hubs

Work with data streams by using Azure Stream Analytics

Ingest data streams with Azure Stream Analytics

Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks

Process streaming data with Azure Databricks structured streaming

Module 16: Build reports using Power BI integration with Azure Synpase Analytics

Create reports with Power BI using its integration with Azure Synapse Analytics

Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics

Use the integrated machine learning process in Azure Synapse Analytics

Durata

28 ore

Materiale didattico

Il Corso include:

  • Manuale ufficiale Microsoft Learning (in lingua inglese) accessibile online, di durata illimitata.
  • Ambiente di Laboratorio con macchine virtuali accessibili online per 180 giorni dalla data del corso.

 

 

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