13 Where to go next

 

This chapter covers

  • Making your data assets user friendly
  • Keeping your data assets safe
  • Keeping your development assets safe
  • Proving your skills with Microsoft exams

You’ve reached the end of this book on building an analytics system in Azure using the Lambda architecture. Each chapter demonstrates part of the overall system. You can see flow of data through the system in figure 13.1.

The knowledge you’ve learned in this book can be applied more broadly too. Consider the following:

  • Storage account services support backups of other Azure services, disks for Azure Virtual Machines, and durable, scalable storage for web applications.
  • Queues and Event Hubs decouple components of applications. Use them in other systems for scalability and asynchronous processing.
  • Relational databases are used in many multi-tiered software applications. The many flavors of SQLDB allow drop-in substitution for on-premises SQL Server databases.
  • Azure Data Factory can connect to external cloud services like AWS S3 and Google Cloud Storage. It can trigger web hooks, and retrieve data from websites, FTP servers, and services like Salesforce.
Figure 13.1 Lambda architecture with Azure PaaS services

As you begin gathering data and supporting analysis work, consider how you can encourage adoption and protection of the analytics system you’ve built. You can start by making a descriptive list of your data.

13.1 Data catalog

13.1.1 Data Catalog as a service

13.1.2 Data locations

13.1.3 Data definitions

13.1.4 Data frequency

13.1.5 Business drivers

13.2 Version control and backups

13.2.1 Blob Storage

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