Conceptualizing the Processing Model for Azure Databricks Service from Pluralsight

In this course, you will learn about the Spark based Azure Databricks platform. You will see how Spark Structured Streaming processing model works, and then use it to build end-to-end production ready streaming pipeline on Azure Databricks platform.

Modern data pipelines often include streaming data, that needs to be processed in real-time. While Apache Spark is very popular for big data processing and can help us build reliable streaming pipelines, managing the Spark environment is no cakewalk.

In this course, Conceptualizing the Processing Model for Azure Databricks Service, you will learn how to use Spark Structured Streaming on Databricks platform, which is running on Microsoft Azure, and leverage its features to build an end-to-end streaming pipeline quickly and reliably. And all this while learning about collaboration options and optimizations that it brings, but without worrying about the infrastructure management.

First, you will learn about the processing model of Spark Structured Streaming, about the Databricks platform and features, and how it is runs on Microsoft Azure.

Next, you will see how to setup the environment, like workspace, clusters, and security; configure streaming sources and sinks, and see how Structured Streaming fault tolerance works.

Followed by this, you will learn how to build each phase of streaming pipeline, by extracting the data from source, transforming it, and loading it in a sink. And then make it production ready, and run it using Databricks jobs.

You will also see, how to customize the cluster using Initialization scripts and Docker containers, to suit your business requirements.

Finally, you will explore other aspects. You will see what are the different workloads available, and how pricing works. We will also talk about best practices, in terms of development, performance, stability and cost. And lastly, you will see how Spark Structured Streaming on Azure Databricks compares to other managed services, like Flink on AWS, Azure Stream Analytics, Beam on Google Cloud etc.

By the end of this course, you will have the skills and knowledge of Azure Databricks platform needed to build an end-to-end streaming pipeline, using Spark Structured streaming.

What's inside

Syllabus

Course Overview

Getting Started with Structured Streaming on Azure Databricks

Setting up Databricks Environment

Configuring Source and Sink Stores

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Focuses on a key part of the Microsoft Azure ecosystem

Designed for learners with some existing experience in Apache Spark

Suitable for professionals looking to build real-world streaming pipelines

Appropriate for individuals aiming to enhance their skills in modern data pipelines

Structured Streaming on Azure Databricks is a specialized domain, making this a niche course

Teaching Azure Databricks and Spark Structured Streaming together requires some prior familiarity

Reviews summary

Azure databricks streaming pipeline essentials

According to students, this course provides a comprehensive and practical guide to conceptualizing and building production-ready streaming pipelines on the Azure Databricks platform using Spark Structured Streaming. Learners highlight the instructor's clear explanations of complex topics like fault tolerance and the processing model, appreciating the strong hands-on labs and real-world applicability. While generally well-received and regularly updated, some learners note a potential assumption of prior Spark or Azure knowledge, which can make the pace challenging for absolute beginners.

Course content stays current with platform.

"The course has been updated regularly, keeping content current with new Databricks features, which is a huge plus."

"It's great to see the course being maintained and updated for new Databricks functionalities."

"I appreciate that the instructor keeps the material relevant and up-to-date with the evolving cloud services."

Instructor is knowledgeable and explains well.

"The instructor explains complex concepts with such clarity and provides excellent hands-on labs."

"Excellent course! The instructor's deep knowledge of Databricks and Structured Streaming shines through."

"The instructor is very knowledgeable and responsive to questions in the forum. Highly recommended!"

Focuses on hands-on skills for real-world use.

"I found the sections on optimizing performance and making pipelines production-ready incredibly valuable."

"The practical examples were spot on, directly applicable to real-world scenarios. Highly recommend for professionals."

"The hands-on coding and projects are the strongest part of the course for me, providing a strong foundation."

Offers a thorough and practical understanding.

"This course is absolutely fantastic for anyone looking to truly understand Azure Databricks and Spark Structured Streaming."

"A truly comprehensive guide to Databricks. The course flows logically, starting from the basics of Structured Streaming and progressing to advanced topics."

"It covers a lot of ground regarding Azure Databricks and Structured Streaming. The core content on building pipelines was solid."

Pace can be fast, demos sometimes quick.

"My only minor gripe is that sometimes the demos moved a bit fast, making it hard to follow along precisely without pausing constantly."

"Some parts felt a bit rushed, especially the final section on competition, but the core content was solid."

"I found the overall pace of the lectures to be quite quick, requiring frequent pauses to digest the information."

Requires existing familiarity with Spark/Azure.

"I struggled with the pace. It assumes a certain level of prior knowledge in Spark or Azure that I didn't quite have."

"Found this course quite challenging. The prerequisites weren't clearly stated, and I felt lost quickly without a strong background in Spark."

"The practical setup often had me debugging more than learning. An introductory module on basic Spark/Azure might help a lot."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Conceptualizing the Processing Model for Azure Databricks Service with these activities:

Read tutorial on Apache Spark fundamentals

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Understanding the underlying concepts of Spark before the course begins will facilitate your understanding of the course materials.

Browse courses on Apache Spark

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Go to the official Spark documentation
Read through the tutorial on Spark fundamentals

Explore Azure Databricks documentation

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Familiarizing yourself with the Azure Databricks platform will help you navigate the environment during the course.

Browse courses on Azure Databricks

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Visit the Azure Databricks documentation website
Read through the tutorials on configuring and managing Databricks services

Participate in online discussion forums

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Interacting with peers can provide different perspectives and reinforce your understanding of the concepts.

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Join the course discussion forums
Ask questions and engage with other students

Four other activities

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Practice transforming data using Structured Streaming

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Hands-on practice with Structured Streaming transformations will solidify your understanding of its capabilities.

Browse courses on Structured Streaming

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Create a sample DataFrame
Apply transformations using streaming queries
Verify the results

Design a production-ready streaming pipeline

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Building a production-ready pipeline will test your ability to apply the concepts learned in the course to a real-world scenario.

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Define the data source and sink
Design the data transformation logic
Implement fault tolerance mechanisms
Test and deploy the pipeline

Attend a workshop on Spark Structured Streaming

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Attending a workshop can provide hands-on guidance and insights from experienced professionals.

Browse courses on Spark Structured Streaming

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Find a workshop on Spark Structured Streaming
Register and attend the workshop

Create a resource guide for Azure Databricks

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Creating a resource guide will deepen your understanding of Azure Databricks and provide a valuable reference for future projects.

Browse courses on Azure Databricks

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Gather resources on Azure Databricks from various sources
Organize and categorize the resources
Create a document or website to share the guide

Career center

Learners who complete Conceptualizing the Processing Model for Azure Databricks Service will develop knowledge and skills that may be useful to these careers:

Business Intelligence Analyst

Business Intelligence Analysts use data to help businesses make informed decisions. This course may be useful in helping you become a Business Intelligence Analyst by teaching you how to use Spark Structured Streaming on Databricks platform. Spark Structured Streaming is a powerful tool for processing streaming data, and it can be used to build a variety of business intelligence applications, such as real-time dashboards, fraud detection systems, and anomaly detection systems.

See salaries and explore the career path for Business Intelligence Analyst

Data Integration Engineer

Data Integration Engineers design and build data integration solutions. This course may be useful in helping you become a Data Integration Engineer by teaching you how to use Spark Structured Streaming on Databricks platform. Spark Structured Streaming is a powerful tool for building streaming data pipelines, and it can be used to design and build data integration solutions that can handle large volumes of streaming data.

See salaries and explore the career path for Data Integration Engineer

Database Administrator

Database Administrators manage databases. This course may be useful in helping you become a Database Administrator by teaching you how to use Spark Structured Streaming on Databricks platform. Spark Structured Streaming can be used to build streaming data pipelines that can be used to populate and maintain databases.

See salaries and explore the career path for Database Administrator

Data Architect

Data Architects design and manage data architectures. This course may be useful in helping you become a Data Architect by teaching you how to use Spark Structured Streaming on Databricks platform. Spark Structured Streaming is a powerful tool for building streaming data pipelines, and it can be used to design and manage data architectures that can handle large volumes of streaming data.

See salaries and explore the career path for Data Architect

Data Platform Engineer

Data Platform Engineers design, build, and maintain data platforms. This course may be useful in helping you become a Data Platform Engineer by teaching you how to use Spark Structured Streaming on Databricks platform. Spark Structured Streaming is a powerful tool for building streaming data pipelines, and it can be used to design and build data platforms that can handle large volumes of streaming data.

See salaries and explore the career path for Data Platform Engineer

Cloud Engineer

Cloud Engineers design and manage cloud computing solutions. This course may be useful in helping you become a Cloud Engineer by teaching you how to use Spark Structured Streaming on Databricks platform. Databricks platform is a managed service that makes it easy to build and run streaming pipelines in the cloud. By learning how to use this platform, you will be able to design and manage cloud computing solutions that can handle large volumes of streaming data.

See salaries and explore the career path for Cloud Engineer

Cloud Architect

Cloud Architects design and manage cloud computing solutions. This course may be useful in helping you become a Cloud Architect by teaching you how to use Spark Structured Streaming on Databricks platform. Databricks platform is a managed service that makes it easy to build and run streaming pipelines in the cloud. By learning how to use this platform, you will be able to design and manage cloud computing solutions that can handle large volumes of streaming data.

See salaries and explore the career path for Cloud Architect

DevOps Engineer

DevOps Engineers work to bridge the gap between development and operations teams. This course may be useful in helping you become a DevOps Engineer by teaching you how to use Spark Structured Streaming on Databricks platform. Databricks platform is a managed service that makes it easy to build and run streaming pipelines. By learning how to use this platform, you will be able to build and manage streaming data pipelines that can be easily integrated into your development and operations processes.

See salaries and explore the career path for DevOps Engineer

Data Governance Analyst

Data Governance Analysts develop and implement data governance policies and procedures. This course may be useful in helping you become a Data Governance Analyst by teaching you how to use Spark Structured Streaming on Databricks platform. Spark Structured Streaming can be used to build streaming data pipelines that can be used to collect and analyze data for data governance purposes.

See salaries and explore the career path for Data Governance Analyst

Big Data Engineer

Big Data Engineers design, build, and maintain the infrastructure and systems that store and process big data. This course may be useful in helping you become a Big Data Engineer by teaching you how to use Spark Structured Streaming on Databricks platform. Spark Structured Streaming is a powerful tool for processing streaming big data, and it can be used to build a variety of big data applications, such as real-time dashboards, fraud detection systems, and anomaly detection systems.

See salaries and explore the career path for Big Data Engineer

Data Analyst

Data Analysts collect, clean, and analyze data to help businesses make informed decisions. This course may be useful in helping you become a Data Analyst by teaching you how to use Spark Structured Streaming on Databricks platform. Spark Structured Streaming is a powerful tool for processing streaming data, and it can be used to build a variety of data analytics applications, such as real-time dashboards, fraud detection systems, and anomaly detection systems.

See salaries and explore the career path for Data Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. This course may be useful in helping you become a Software Engineer by teaching you how to use Spark Structured Streaming on Databricks platform. Spark Structured Streaming is a powerful tool for building streaming data pipelines, and it can be used to build a variety of software applications, such as real-time dashboards, fraud detection systems, and anomaly detection systems.

See salaries and explore the career path for Software Engineer

Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models. This course may be useful in helping you become a Machine Learning Engineer by teaching you how to use Spark Structured Streaming on Databricks platform. Spark Structured Streaming can be used to build real-time machine learning pipelines, which can be used to train and deploy models on new data as it arrives.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. This course may be useful in helping you become a Data Scientist by teaching you how to use Spark Structured Streaming on Databricks platform. Spark Structured Streaming is a powerful tool for processing streaming data, and it can be used to build a variety of data science applications, such as fraud detection, anomaly detection, and predictive analytics.

See salaries and explore the career path for Data Scientist

Data Engineer

Data Engineers design, build, and maintain the infrastructure and systems that store and process data for an organization. This course may be useful in helping you become a Data Engineer by teaching you how to use Spark Structured Streaming on Databricks platform, a managed service that makes it easy to build and run streaming pipelines. By learning how to use this platform, you will be able to build reliable and scalable streaming pipelines that can handle large volumes of data.

See salaries and explore the career path for Data Engineer