At the fall 2018 PASS Summit in Seattle, I'm excited to be co-presenting a full day pre-conference session with my good friend & fellow BlueGranite colleague Meagan Longoria. 

Why Do a Pre-Con?

I have a natural inclination to share information that I have learned. Being a hands-on techie is something I absolutely love, but I have a bit of educator in my blood as well. And, continually learning new skills is at the core of what makes me happy. All of which means that I aim to teach others in a way that I would want to learn. 

What Will You Learn? 

This session will very much be about planning the architecture and factors around decision-making, presented in a very practical and realistic way (full abstract can be found here). We will build the components for one reference architecture, using scripts that we will provide you. 

The full abstract can be found on the PASS Summit site. To highlight just a few of the topics that you'll hear about:

• Going to the cloud - What's easier? What's harder? What trade-offs can you expect to make with respect to cost, control, complexity, performance, and security?
• Cloud design patterns - In what ways are cloud design patterns different from traditional on-premises solutions? How does that change the typical roles for developers and administrators?
• Schema-on-read - In what scenarios does schema-on-read work extremely well? In which situations is it not ideal?
• Patterns-based development - What automation techniques can save you time, improve efficiency, and reduce the chance for error? 
• Architecture - What does a BI/analytics/DW architecture look like if we value the concept of polyglot persistence vs. architectural simplicity? What kind of differences should we be aware of if we are using a distributed architecture? What are the Azure options for supporting data science and self-service BI?
• Data storage - When do we want to analyze data in place vs. move it to another data store? What technology options do we have in Azure, and what factors do we want to consider for deciding between data virtualization and data integration? In what cases can you take advantage of a data lake in your architecture? 

Who is the Target Audience?

The ideal audience member has some experience as a data engineer, BI professional, or database developer, and is in the early stages of migrating or building solutions in Azure.

This session is broad because the data platform offerings in Azure are broad with many choices and considerations. Our day job *is* planning and building data solutions in Azure. Meagan and I are very excited to help you get started with building a solid data architecture in Azure.

More details and to register: Designing Modern Data and Analytics Solutions in Azure