Enterprise analytics with SQL Server and in Azure

Focus of this one-day workshop will be building data-driven analytical solution for enterprise environment. Using Microsoft on-prem and in the cloud solutions, the workshop will guide you through building a solution in either of environments, as well as a hybrid solution, getting best of both worlds. Building working end-to-end solution with given technology will be main focus of the workshop and not so focusing on exploring different machine learning algorithms.

Building on-prem solutions with Python (and some R) starting with overview on what is available in SQL Server 2017, continuing with 2019 features and big data clusters and exploring the capabilities of data virtualization and the power of Polybase language.

Moving to cloud services, we will be exploring the automated machine learning services, Azure Databricks, Azure Data Factory, Azure SQL Datawarehouse and Cosmos DB with stream analytics.

Last part will be focusing on ML.NET, as part of creating and building .NET applications using Machine Learning.

Workshop will be using mainly one (in addition another data-set for streaming analytics) and covering different machine learning scenarios based on better services (on-prem or in cloud).

Program of different modules

Module 1: 9.00 - 10.00 Using Python and R on-prem. Module will introduce Python and R with SQL Server 2017/2019 in-database integration, using native scoring, building and deploying the models. (Module: 30% presentation; 70% Hands-on)

(10 min break)

Module 2: 10:10 – 11.00 SQL Server 2019 Big Data Clusters Module will show and use the new functionalities of Big Data Clusters in SQL Server 2019, using Azure data studio and bringing together data from different sources, using Polybase and Jupyter notebooks in order to build the enterprise analytical solution. (Module: 50% presentation; 50% Hands-on)

(10 min coffee break)

Module 3: 11.10 – 12.00 - Machine Learning services with Python; Module will show you the capabilities of using automated machine learning project as part of a service, creating pipelines and deploying the model. (Module: 30% presentation; 70% hands-on)

Lunch: (30 min Lunch break)

Module 4: 12.30 – 13.30 Using Azure Databricks for data analysis – Module will walk you through building a complete machine learning environment for collaborative and shared projects in Databricks. Using Python, Scala, R or SQL, we will be building, deploying and consuming a predictive model.

(Module: 30% presentation; 70% Demo/hands-on).

(10 min break)

Module 5: 13.40 – 14.50 Azure Cosmos DB and stream analytics – Module will look into using and setting up Comsos DB, Event hub, Azure functions and Stream analytics for viewing and running analytics on stream data. With simple data analytics and simple model predictions, you will be able to predict on stream data in the cloud.

(Module: 50% presentation; 50% Demo/hands-on)

(10 min break)

Module 6: 15.00 – 16.10 Azure Data Factory, Azure SQL Datawarehouse – Module will explore the data wrangling capabilities with ADF, storing the cold path data in Azure SQL Datawarehouse and some help of Databricks and using Power BI to do end-to-end data analysis.

(Module: 40% Demo, 60% Demo)

(10 min break)

Module 6: 16.20 – 17.00 Simplified ML.NET – Module will walk you through simplified Machine learning for .NET developers, helping developers using ML without leaving the .NET ecosystem. At the same time bringing .NET solutions with ML solution integrated.

(Module: 60% Presentation, 40% Demo)

Target Audience:

This workshop is intended for Data Analyst, data scientist and data engineers that are building ML projects on-prem but would like to extend their process into the cloud and extend the capabilities of cloud computations. Invited are also business people, domain experts, who would like to get a better knowledge on new approaches in Azure cloud and SQL Server 2019.


This one-day pre-con will be demo based and users will be able to tag along. It is encouraging to bring your own laptop and have free azure subscription activated. You will need a laptop running SQL Server 2019 (Developer edition) and Azure Data Studio and Visual Studio with SSTD, Azure Storage explorer and Microsoft SQL Server Integration Service Feature Pack for Azure. Script and code will be available upon session on Github; same datasets will be used on all scenarios and will be distributed as the beginning.

Take aways:

Participants will walk away with understanding how Analytics in Azure can be done, what are the benefits of SQL Server 2019, basic knowledge on Python Language, and all the relevant processes that enterprise analytics need: data manipulation, data preparation, predictive analytics, model deployments and consumption.


Wednesday 30 September 2020