Microsoft Azure Databricks for Data Engineering

Go to Course: https://www.coursera.org/learn/microsoft-azure-databricks-for-data-engineering

Introduction

### Course Review: Microsoft Azure Databricks for Data Engineering If you're venturing into the world of data engineering, then the **Microsoft Azure Databricks for Data Engineering** course on Coursera is an exceptional choice that merits your consideration. This course adeptly combines theoretical knowledge with practical application, presenting a cohesive learning experience aimed at both beginners and seasoned professionals alike. #### Course Overview The course is centered around the **Azure Databricks** platform, a powerful cloud solution that leverages **Apache Spark** to process large-scale data engineering workloads. With the increasing necessity for robust data handling capabilities in today’s data-driven landscape, this course offers hands-on training to harness these technologies efficiently. Participants will explore various components of Azure Databricks, including its architecture, data processing capabilities, and the usage of notebooks for effective data manipulation. By the end of the course, you’ll not only understand the strengths of Azure Databricks but also master tasks well-suited for Apache Spark. #### Detailed Syllabus Review 1. **Introduction to Azure Databricks** - The course kicks off with an overview of what Azure Databricks is and its capabilities. Students will walk away with a solid comprehension of how to utilize the Apache Spark notebook for handling substantial data files and grasping the underlying architecture of Spark Clusters and Jobs. 2. **Read and Write Data in Azure Databricks** - In this section, you’ll learn essential data-handling techniques like reading, writing, and querying data within Databricks, allowing you to manage datasets effectively. 3. **Data Processing in Azure Databricks** - Participants delve into defining DataFrames, performing data transformations, and executing actions critical for effective data processing. The course elucidates concepts such as transforms vs. actions and the distinct evaluations that optimize data handling. 4. **Working with DataFrames in Azure Databricks** - This module highlights advanced DataFrame functions including column-level transformations, aggregations, and date-time operations, equipping students to manipulate data skillfully. 5. **Platform Architecture, Security, and Data Protection** - The focus shifts to the architecture of Azure Databricks, including security measures such as using Azure Key Vault for secret management, which is paramount for safe data operations. 6. **Delta Lake** - This section explores Delta Lake, presenting techniques to create and manage tables while benefiting from its inherent reliability and optimizations — a must-know for modern data engineering. 7. **Analyze Streaming Data and Create Production Workloads** - Students will gain insights into processing streaming data, which is increasingly relevant in today’s data architecture, along with methods of creating production workloads using Azure Data Factory. 8. **Create a Data Architecture** - This comprehensive module covers integrating Azure Databricks with Azure Synapse Analytics and managing deployment pipelines, ensuring students are well-versed in building a streamlined data architecture. 9. **Practice Exam on Data Engineering with Azure Databricks** - Finally, the course concludes with a preparation segment for the **Microsoft Certified: Azure Data Engineer Associate** exam, giving you the tools to assess your understanding of the material covered. #### Final Recommendations Overall, the **Microsoft Azure Databricks for Data Engineering** course on Coursera is an outstanding resource for anyone looking to deepen their knowledge in data engineering using Azure Databricks. The course’s structured layout enhances learning retention, and the inclusion of practical examples ensures that theoretical concepts are translated into actionable insights. With the growing relevance of cloud computing and big data technologies, gaining proficiency in Azure Databricks not only equips you with valuable skills but also enhances your marketability as a data professional. Whether you are preparing for certification or looking to upskill, this course is a wise investment in your career trajectory. Highly recommended for data enthusiasts eager to navigate the expansive field of data engineering!

Syllabus

Introduction to Azure Databricks

Describe the capabilities of Azure Databricks and the Apache Spark notebook for processing huge files. Describe the Azure Databricks platform and identify the types of tasks well-suited for Apache Spark. Describe the architecture of an Azure Databricks Spark Cluster and Spark Jobs.

Describe how to use Azure Databricks supports day-to-day data-handling functions, such as reads, writes, and queries.

Process data in Azure Databricks by defining DataFrames to read and process the Data. Perform data transformations in DataFrames and execute actions to display the transformed data. Explain the difference between a transform and an action, lazy and eager evaluations, Wide and Narrow transformations, and other optimizations in Azure Databricks.

Use the DataFrame Column Class Azure Databricks to apply column-level transformations, such as sorts, filters and aggregations. Use advanced DataFrame functions operations to manipulate data, apply aggregates, and perform date and time operations in Azure Databricks.

Describe the Azure Databricks platform architecture and how it is securedUse Azure Key Vault to store secrets used by Azure Databricks and other services. Access Azure Storage with Key Vault-based secrets

Describe how to use Delta Lake to create, append, and upsert data to Apache Spark tables, taking advantage of built-in reliability and optimizations. Describe Azure Databricks Delta Lake architecture

Process streaming data with Azure Databricks structured streaming. Create production workloads on Azure Databricks with Azure Data Factory.

Describe how to put Azure Databricks notebooks under version control in an Azure DevOps repo and build deployment pipelines to manage your release process. Describe how to integrate Azure Databricks with Azure Synapse Analytics as part of your data architecture. Describe best practices for workspace administration, security, tools, integration, databricks runtime, HA/DR, and clusters in Azure Databricks

Prepare for the Microsoft Certified: Azure Data Engineer Associate exam