Designing Data Flows in Azure
Data Flow Basics
Designing a Data Flow Solution
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This Designing Data Flows in Azure course will enable you to implement the best practices for data flows in your own team. Starting from the basics, you will learn how data flows work from beginning to end. Though we do recommend an idea of what data flows are and how they are used, this course contains some demonstration lectures to really make sure you have got to grips with the concept. By better understanding the key components available in Azure to design and deploy efficient data flows, you will be allowing your organization to reap the benefits.
This course is made up of 19 comprehensive lectures including an overview, demonstrations, and a conclusion.
- Review the features, concepts, and requirements that are necessary for designing data flows
- Learn the basic principles of data flows and common data flow scenarios
- Understand how to implement data flows within Microsoft Azure
- IT professionals who are interested in obtaining an Azure certification
- Those looking to implement data flows within their organizations
- A basic understanding of data flows and their uses
Related Training Content
For more training content related to this course, visit our dedicated MS Azure Content Training Library.
In this lesson, I want to touch on Azure Data Lake Storage. Gen 2, in particular. While Gen 1 provided a Hadoop-compatible file system that was intended for large-scale analytics, Gen 2 has upped the ante by incorporating Azure Blob storage. It actually rides on top of it to be exact. As a result, Azure Data Lake Storage Gen 2 offers the analytics performance of an HDFS-compatible file system, while also offering tiering and data lifecycle management features of blob storage. Other features and benefits now available in Gen 2 as a result include data security, data durability, and replication options that weren't available before. With Azure Data Lake Storage Gen 2, you have access to not only the Azure Data Lake Gen 2 file system APIs, but also to the Blob REST APIs. As a result, you can interact with data in different ways. The best part about it is the fact that enabling all this cool functionality is as simple as changing a setting in the storage account. Instead of needing to provision a new service and maintain a new service, all you need to do is enable it on the storage account. A key feature of Data Lake Storage Gen 2 is the hierarchical namespace that's added to blob storage.
With a hierarchical namespace, objects and files are organized into a hierarchy of directories, which results in much more efficient data access. For example, renaming a directory becomes a single metadata operation on the directory being renamed, instead of a process that enumerates and processes all objects that share the name prefix of the directory. Other key features of Azure Data Lake Storage Gen 2 include Hadoop compatible access and a security model that supports ACL as well as POSIX permissions, including some extra granularity that's specific to Data Lake Storage Gen 2. With an ABFS driver that's optimized for big data analytics, data access is improved. And because Data Lake Storage Gen 2 offers low-cost storage capacity, as well as low-cost transactions, the service is cost-effective. In the next lesson, I'll show you how to provision an Azure Data Lake Gen 2 enabled storage account.
Tom is a 25+ year veteran of the IT industry, having worked in environments as large as 40k seats and as small as 50 seats. Throughout the course of a long an interesting career, he has built an in-depth skillset that spans numerous IT disciplines. Tom has designed and architected small, large, and global IT solutions.
In addition to the Cloud Platform and Infrastructure MCSE certification, Tom also carries several other Microsoft certifications. His ability to see things from a strategic perspective allows Tom to architect solutions that closely align with business needs.
In his spare time, Tom enjoys camping, fishing, and playing poker.