In this session you'll learn some of the key data privacy problems facing database developers and DBAs and 10 steps to help your database team build a "defensible position" through development.
Data on its own is useful, but boring. Data in story form makes people sit up and take notice. Learn to craft effective stories and maximise the impact of your data communication.
In this session, we'll cover the 10 most common tasks a DBA needs to learn to manage in Azure SQL as well as they currently manage their On Prem installs.
An introduction to the philosophy of tidy data and the collection of R packages called Tidyverse that help to treat your data appropriately. Including lots of demos from ingesting to cleaning to visualizing your data.
You are a developer and you want to leverage the 'Power' features in Azure DevOps via YAML pipeline as code, then this session is for you !
Curious about Visual Recognition and Object Detection in Azure? Are you wondering what the difference is between the Computer Vision API and the Custom Vision API? Get up Speed with the Vision API in less than an hour!
That session explains how to put AI algorithms on Edge devices, with feedbacks from a computer vision project
Let's learn about what Always Encrypted is, how it works, and the implications for your environment. By the end you will know how to now easily encrypt columns of data and just as importantly how to unencrypt.
Azure Synapse Analytics combines the power of Data Lakes with Data Warehouses, empowering the organizations to build Big Data, Advanced Analytics and Business Intelligence in one single platform.
DBA and Data scientists should work together! Analyzing data gathered with XE and Query Store data using R or Python for better database insight and discovering hidden patterns.
The most challenging area of machine learning are Data acquisition, Feature extraction, Feature Selection. Almost in all data science project, 80% of time people spend in Data acquisition and Feature engineering.
What is the difference between Artificial Intelligence, Machine Learning and Deep Learning and how can each be used? Join Buck Woody cover simple, clear explanations for these technologies, how they can be applied.
AI and data is at the center of the digital feedback loops. We have invested in a comprehensive portfolio of AI tools, infrastructure and services. Come to this session to get an update of Azure AI with demos.
Azure Data Factory is a cloud native ETL service built for all data integration needs and skill levels. You can easily construct ETL and ELT processes code free or write your own code.
Data lakes have been around for years yet there is still much hype and hyperbole surrounding their use. This session goes beyond corny puns and broken metaphors and instead provides real-world guidance.
In this session you’ll learn more about Azure data services on Azure Arc
In this action-packed demo full session, we will be showing you how to combine Azure DW and Power BI to get optimal performance for running your day to day reports.
Learn how and where to use Azure Functions in cloud-based ETL processes
Azure Key Vault, Azure Dev Ops and Data Factory how do these Azure Services work perfectly together!
Understand how to create, train and operationalize your data science projects in Azure Machine Learning.
Azure SQL Database Edge has taken the same SQL database engine you know and optimized it for the Edge. This session explores popular customer implementations, along with new product features for the Intelligent Edge
In this session we explore Azure Synapse Analytics, we will dive into this limitless analytics service and explore how it brings together enterprise data warehousing and Big Data analytics.
Come learn how Azure Synapse brings together big data and data warehousing through new technology and a unified development experience
Come learn how to use .NET and Spark together in Azure Synapse and elsewhere to cook and analyze your data!
Come learn how you can convert you data into structured formats, apply machine learning skills and index it to make it easy to find information and relationships that can save you time and money.
If AI is on the cards in your business you might need to make a recommendation as to whether your company should Buy or Build. Come to this session to work out the moving parts to turn "It Depends!" into an answer.
In this session we'll focus on the benefits of using Azure Data Lake. We will cover how we use ADLS in real projects to produce a security architecture which is adaptable, scalable and provides monitoring capabilities.
Azure Synapse Analytics combines the power of Data Lakes with Data Warehouses, empowering the organizations to build Big Data, Advanced Analytics and Business Intelligence in one single platform.
Building real-time big data analytics solutions with Azure Data Explorer and Azure Stream Analytics - Come learn about the latest that Azure has to offer in real-time analytics
How to code everything in SSRS with PowerShell.
We will see how to construct a cloud-first architecture based on serverless data analytics. We will look at specific challenges and cost saving strategies, to produce a reliable, scalable and cost effective solution!
Common Data Model as the foundation of Power BI Dataflows and as part of the Open Data Initiative with SAP and Adobe, seems to be a pretty good move from Microsoft. We want to take a closer look to this approach
Taken from the 20+ years of field experiences, many common statistical and data science mistakes have been detected. Session will tackle couple of them.
A Q&A panel session with Andrew Pruski and Anthony Nocentino talking about all things containers! Hosted by Rob Sewell
This presentation is about how to create custom visuals (components) for your PowerBI reports. Power BI reporting features could be extended with custom components, that developers can implement.
Learn how Azure supports interactive, exploratory notebooks (e.g. Jupyter) for data processing and experimentation across a range of scales from simple single-computer work up to massively parallel Databricks clusters.
Learn to make better analytic solutions by following current thoughts on data modeling.
In this session we will merge practice and theory, analyzing what is data virtualization and data lake, what their benefits and how to implement them using SQL Server 2019 Big Data Cluster
In this demo heavy session we will show how Containers and Kubernetes, along with Azure DevOps can work for your CI/CD pipeline.
In this session you’ll learn the real-world ins and outs of how we successfully migrated customers to Azure data platform.
Databricks, Lakes & Parquet are a match made in heaven, but explode with extra power when using Delta Lake. This session will dive into the details of how Databricks Delta works and how to make the most of it.
If you're not afraid of a little bit of C# and LINQ, Tabular Editor can take your productivity to a whole new level. This session will teach you how to use Tabular Editor's scripting and Best Practice Analyzer features.
Deep dive into the Common Data Model world and learn how to deploy, extend and use it along with other Microsoft technologies like Power Apps, Power BI Dataflows and Azure Databricks.
Come and join us this session to unleash the power from your data as we introduce Machine Learning Services for Python and R across the SQL Platform.
Everything you need to know about dataflows & how to easily prep your data, leverage Microsoft’s standardized schema (CDM), improve time-to-value, eliminate data silos, & create one source of truth for your organization.
Come learn how to design your enterprise-scale data lake on Azure using Azure Data Lake Storage
An introduction to the principles that underpin DevOps and the practices that are derived from those principles. This session is mostly theoretical, although it finishes with a brief overview of an Azure DevOps pipeline.
Learn how to take your Azure Databricks Workspace and store all you care about in a git repo, and maybe even have some tests running as part of your release pipelines!
How to understand where bias lies, how to collect data and the impact of bias and ethical considerations in your data science solutions.
What happens when you combine a cloud orchestration service with a Spark cluster?! The answer is a feature rich, graphical, scalable data flow environment to rival any ETL tech we’ve previously had available in Azure.
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