I frequently present at user groups, and always try to create a brand new presentation to keep things interesting.  We all know technology changes so quickly so there is no shortage of topics!  There is a list of all my presentations with slide decks.  Here are the new presentations I created the past year:

Differentiate Big Data vs Data Warehouse use cases for a cloud solution

It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together.  In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn’t, in order for you to position, design and deliver the proper adoption use cases for each with your customers.  We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS  as well as high-level concepts such as when to use a data lake.  We will also review the most common reference architectures (“patterns”) witnessed in customer adoption. (slides)

Introduction to Azure Databricks

Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project.  It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib).  It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark. (slides)

Azure SQL Database Managed Instance

Azure SQL Database Managed Instance is a new flavor of Azure SQL Database that is a game changer.  It offers near-complete SQL Server compatibility and network isolation to easily lift and shift databases to Azure (you can literally backup an on-premise database and restore it into a Azure SQL Database Managed Instance).  Think of it as an enhancement to Azure SQL Database that is built on the same PaaS infrastructure and maintains all it’s features (i.e. active geo-replication, high availability, automatic backups, database advisor, threat detection, intelligent insights, vulnerability assessment, etc) but adds support for databases up to 35TB, VNET, SQL Agent, cross-database querying, replication, etc.  So, you can migrate your databases from on-prem to Azure with very little migration effort which is a big improvement from the current Singleton or Elastic Pool flavors which can require substantial changes. (slides)

What’s new in SQL Server 2017

Covers all the new features in SQL Server 2017, as well as details on upgrading and migrating to SQL Server 2017 or to Azure SQL Database. (slides)

Microsoft Data Platform – What’s included

The pace of Microsoft product innovation is so fast that even though I spend half my days learning, I struggle to keep up. And as I work with customers I find they are often in the dark about many of the products that we have since they are focused on just keeping what they have running and putting out fires. So, let me cover what products you might have missed in the Microsoft data platform world. Be prepared to discover all the various Microsoft technologies and products for collecting data, transforming it, storing it, and visualizing it.  My goal is to help you not only understand each product but understand how they all fit together and there proper use case, allowing you to build the appropriate solution that can incorporate any data in the future no matter the size, frequency, or type. Along the way we will touch on technologies covering NoSQL, Hadoop, and open source. (slides)

Learning to present and becoming good at it

Have you been thinking about presenting at a user group?  Are you being asked to present at your work?  Is learning to present one of the keys to advancing your career?  Or do you just think it would be fun to present but you are too nervous to try it?  Well take the first step to becoming a presenter by attending this session and I will guide you through the process of learning to present and becoming good at it.  It’s easier than you think!  I am an introvert and was deathly afraid to speak in public.  Now I love to present and it’s actually my main function in my job at Microsoft.  I’ll share with you journey that lead me to speak at major conferences and the skills I learned along the way to become a good presenter and to get rid of the fear.  You can do it! (slides)

Microsoft cloud big data strategy

Think of big data as all data, no matter what the volume, velocity, or variety.  The simple truth is a traditional on-prem data warehouse will not handle big data.  So what is Microsoft’s strategy for building a big data solution?  And why is it best to have this solution in the cloud?  That is what this presentation will cover.  Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it.  My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution. (slides)

Choosing technologies for a big data solution in the cloud

Has your company been building data warehouses for years using SQL Server?  And are you now tasked with creating or moving your data warehouse to the cloud and modernizing it to support “Big Data”?  What technologies and tools should use?  That is what this presentation will help you answer.  First we will level-set what big data is and other definitions, cover questions to ask to help decide which technologies to use, go over the new technologies to choose from, and then compare the pros and cons of the technologies.  Finally we will show you common big data architecture solutions and help you to answer questions such as: Where do I store the data?  Should I use a data lake?  Do I still need a cube?  What about Hadoop/NoSQL?  Do I need the power of MPP?  Should I build a “logical data warehouse”?  What is this lambda architecture?  And we’ll close with showing some architectures of real-world customer big data solutions.  Come to this session to get started down the path to making the proper technology choices in moving to the cloud. (slides)