DP-3011 | Implement a Data Analytics Solution with Azure Databricks
 
This course explores how to use Databricks and Apache Spark on Azure to take data projects from exploration to production. You’ll learn how to ingest, transform, and analyze large-scale datasets with Spark DataFrames, Spark SQL, and PySpark, while also building confidence in managing distributed data processing. Along the way, you’ll get hands-on with the Databricks workspace—navigating clusters and creating and optimizing Delta tables.   You’ll also dive into data engineering practices, including designing ETL pipelines, handling schema evolution, and enforcing data quality. The course then moves into orchestration, showing you how to automate and manage workloads with Lakeflow Jobs and pipelines. To round things out, you’ll explore governance and security capabilities such as Unity Catalog and Purview integration, ensuring you can work with data in a secure, well-managed, and production-ready environment.
 
Objectives
After completing this course, students will be able to:
Explore Azure Databricks.
Perform data analysis with Azure Databricks.
Use Apache Spark in Azure Databricks.
Manage data with Delta Lake
Build data pipelines with Delta Live Tables
Deploy workloads with Azure Databricks Workflows

Audience profile
You should be an Azure data analyst with subject matter expertise in data management, data warehousing and data visualization using Azure services.

Prerequisites
None.

Course Outline
Explore Azure Databricks
Perform data analysis with Azure Databricks
Use Apache Spark in Azure Databricks
Manage data with Delta Lake
Build Lakeflow Declarative Pipelines
Deploy workloads with Azure Databricks Lakeflow Jobs

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Debido a las constantes actualizaciones de los contenidos de los cursos por parte del fabricante, el contenido de este temario puede variar con respecto al publicado en el sitio oficial, sin embargo, Netec siempre entregará la versión actualizada de éste.