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.
How do you test your data projects? Do you even test? Let's face the truth that data processing solutions are very rarely tested ... So let's start with fundamentals of (not only data warehouse) testing.
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.
Application and Database migrations can be a cause for concern. How many times have you heard, "If it isn't broke don't fix it". Come see how you can give confidence to key stake holders on testing the process.
When buzzwords like DevOps and Machine Learning collide, you need a demo and talk that shows how to actually increase the delivery speed of Advanced Analytics solutions.
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.
Automated Machine Learning, "1 button data science", has been with us for a while. As always there's fans and detractors. We'll discuss when and why you'd use AutoML and when it's better to avoid it.
Learn how to configure source control and use Azure Key Vault to implement Continuous Integration and Continuous Delivery (CI/CD) for Azure Data Factory solutions using Azure DevOps pipelines.
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.
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.
This session will talk about how you can combine the serverless power of Azure Functions with the flexibility of Azure Cosmos DB to process large amounts of data and gain real-time insights.
Azure Key Vault, Azure Dev Ops and Data Factory how do these Azure Services work perfectly together!
How do you deploy multiple machine learning models in production to solve your challenge? How do enable canary releases and A/B testing? How do you make sure a user is always served by the same version of the model?
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 and optimized it for the Edge.This session explores customer implementations with Azure SQL Database Edge, along with new product features.
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!
Together Power BI and Azure Synapse Analytics can handle huge datasets. This session details how the composite model and aggregation capabilities of Power BI can be used to fully exploit the scale provided by Synapse.
Come learn how to build enterprise-scale analytics platforms and fully harness the power of Apache Spark with Azure Databricks
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.
Learn how to use parameters, variables, expressions, functions, lookups, and loops to build a dynamic and reusable Azure Data Factory solution.
A client's case on how to build a robust metadata driven datahub by utilizing Databricks (Delta Lake) for data ingestion, validation, and loading
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
This session will cover how to create flexible, reliable, and scalable ETL and ELT pipelines across Azure using Azure Data Factory
Columnstore Indexes were launched in SQL Server 2012 and greatly enhanced in 2014 & 2016 - but if you thought that Microsoft was done with them - you are missing a lot of important improvements that were added.
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.
Learn the four core practices which enable high performance for database development and delivery.
Look beyond the marketing to understand how, when and why you should consider Cosmos DB. Based on real world experience, we'll explain the complexities and gotchas in building realistic cost and performance models.
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.
SQL Server 2019 comes with Python as an installation option. Let’s use this to gather data from various sources to create your test and development data.
Learn common patterns for testing data, and the anti-patterns that trip developers up.
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.
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