One of the last technical challenges of cloud adoption is right security configuration. This session focuses on Azure Sql PaaS, covers governance, risk management and compliance and provides 8-step process for securing public cloud.
Within Azure we have a rich ecosystem of AI services that can be leveraged to gain new insights into your data. This session will give you an easy to digest breakdown of the key services that matter and how to approach each one. Cognitive Services, Bot Framework, Azure Machine Learning Studio, Databricks, Notebooks, the Azure ML SDK for Python and the Azure ML Service
Tabular Editor, an open source tool for authoring Tabular Models, makes it easier for teams of developers to work on the same model simultaneously. It also provides functionality for automated build and deployment. In short, DevOps for SSAS Tabular.
Azure offers a comprehensive set of big-data solutions that help you gather, store, process, analyse and visualise data of any variety, volume or velocity, so you can discover new opportunities and take quick action. In this overview session, we’ll look at the various components within Azure that make up the Modern Data Warehouse, enable Real-Time Analytics, and support Advanced Analytics scenarios. You should leave with a high level understanding of the capabilities and limitations of each of the products within the Azure Analytics portfolio.
We will review this new feature of ADFv2, do deep dive to understand the mentioned techniques, compare them to SSIS and/or T-SQL and learn how modelled data flow runs Scala behind the scenes.
Azure Databricks support both Classic and Deep Learning ML Algorithms to analyse Large DataSets at scale. The Integrated Notebook experience gives the Data Scientists and Data Engineers to do exploratory Data Analysis, also feels like native to Jupyter notebook users. In this session we will extract intelligence from Higgs Dataset (Particle Physics) by running Classic and Deep Learning models using Azure Databricks. We will also peek into AMl service's integration with Azure Databricks for managing the end-to-end machine learning lifecycle.
Managed Instances can make your cloud migrations simpler, but have their own nuances. Learn about what you need to know to manage this new platform.
In this session, we’ll look at the different options within the Cognitive Services suite, show you how to connect to the APIs using Python code, walk through a live bot demo, and build an Azure Cognitive Search index. You should leave this session feeling like you’ve had a jump start to further your AI developer skill set.
Using Azure DevOps and Azure RM templates to created isolated environments for testing PaaS solutions.
Real-time decisions enabled by massively scalable distributed event driven systems with embedded analytics are re-shaping our digital landscapes. But how do you know which technology to choose? We'll cover the fundamentals of streaming systems, how and why they can deliver value to your business, and a look at options for streaming analytics in Azure
The new SQL Server tool designed to help manage your estate
Learn how dataflows can be used to load data in Power BI.
In this session we will learn which processes are required to build a real-time analytical model along with batch-based workloads, which are the foundation of a Lambda architecture.
In this session, we will discuss the strategies and thought process one should adopt for modeling and partition data effectively in Azure Cosmos DB. We will also briefly cover related topics such implementing optimistic concurrency control, transactions with stored procedures, batch operations, and tuning queries + indexing
PowerShell loves Power BI. In this session we will take a look at how to manage your datasets, security, monitor licensing and more, all through the ultimate administration interface: PowerShell!
Are you curious about Azure Cosmos DB? This is the session for you! Join us to find out what it is, how it compares to other database platforms, and what all the hype is about!
With GDPR and the number of data breaches we see in the news, encrypting sensitive data is incredibly important. In this talk we start with the basics of encryption, moving on to look at the ways we can encrypt data in SQL Server and Azure SQL DB.
Ever wondered how you can add the power of ML to your existing SQL estate without the need to invest in new services? Come to this session to learn about running Python and R workloads in SQL, the PREDICT function, and how to operationalise models in Azure SQL Database
You want to use all the new and sexy cloud based data services like PowerBI, Flow and hosted SSAS, but your data remains strictly on premise. Come along to see how to use the data gateway to connect Azure to your on-premise data.
This session will focus on the new features and capabilities that help you meet compliance and security needs with SQL Server on-premises as well as in Azure SQL Database. This includes the new Static Data Masking, new authentication capabilities, new functionalities in Vulnerability Assessment and Threat Detection as well as Always Encrypted. If you want to know about the latest developments in SQL Security, this session is for you.
In this talk you will learn how to use Power BI to prototype/develop a BI solution in days and then evolve it into a fully scalable Azure BI solution.
Are you still asking yourself, what is big data? What is HDInsight and how can I benefit from it? which type of HDInsight Cluster should I use? come to this session and find an answer
Learn how to build an Azure Machine Learning model, how to use, integrate and consume the model within other applications, and learn the basic principles and statistics concepts available in the different ML algorithms.
Businesses today require real-time information to make better-informed decisions, this requires a new set of tools. In this session you will learn about Azure Stream analytics and how it can help address real-time data scenarios
SQL Server and Azure are built for each other. New hybrid scenarios between on-premise SQL Server and Azure mean they don't have to exclude each other but instead you can have the best of both worlds, reducing operational costs.
Get the most from Data Catalog with patterns and practices.
In this talk we will discuss best practices around how to design and maintain an Azure SQL Data Warehouse for best throughput and query performance.
Learn how to build, debug and deploy a chatbot using the Azure Bot Service and the Microsoft Bot Framework.
This session takes a closer look at Azure Stream Analytics, and how you can make it work in your Projects.
How does Big Data with Hadoop, Cloud Computing in Azure, Self Service BI and “classic” BI with SQL 2016 interact with each other to build a modern enterprise BI architecture at InnoGames? Watch my talk and you will know!
Introducing the ROC curve and its use in Azure ML.
Selecting the right PaaS components in Azure
We live in a cloud first World, but many organisation still run largely on-premise. This session covers how these organisations can begin their journey and leverage Azure data services.
Deep learning is an essential component of an analytical toolbox. There are considerable challenges in training deep learning models and this presentation explores how to overcome these using Microsoft’s scalable ML offering, Azure Batch AI.
Azure SQL Database built-in intelligence features will help you improve performance and security of your database and dramatically reduce the overhead of managing thousands of databases.
SQL disk configuration and planning can really hurt you if you get it wrong in azure. There is a lot more to getting SQL right on Azure VMs than next-next-next.
If you have 100's SQL Servers, some legacy applications, running on older versions and no cloud subscription? Then the path is to cloud very different from a single click. We focus on the key steps needed for a cloud SQL data migration.
In this session we will discuss about the Azure SQLDW and how we can use it to help on our day to day.
See the latest features of SSIS in ADFv2, e.g. ARM VNet, Enterprise Edition, Custom Setup Interface, Execute SSIS Package Activity, etc.
This session will reveal what's new for Azure Analysis Services in the areas of performance, scalability, advanced calculations, model management, and monitoring.
An IoT analytics solution addressing real-life issues with SCADA data from a network of 600 Waste Water Treatment Plants and 100s of Pumping Stations. An end-to-end solution with Azure Data Warehouse, Azure Data Factory, Azure Analysis Services and PowerBI
Discover the Microsoft technologies that enable end-to-end data science, including SQL Server and Azure PaaS Services, along with practical scenarios and demos.
This talk takes a look at Cloud Computing – what it is, the types of Cloud available and their advantages and disadvantages along with what Microsoft Azure has to offer.
This session covers the more advanced aspects of development for Azure SQL Data Warehouse. Areas such as data movement, workload concurrency and resource management will all be covered during this intense 60 minute session.
Azure Data Factory and SSIS are both data movement tools, but built for different purposes. In this session you will learn pros and cons of using each technology, and best practices of using each in real world scenarios.
Let's talk about how you can get the most out of Azure DocumentDB. In this session we will dive deep in to the mechanics of DocumentDB and explain the various levers available to tune performance.
Join me for an overview of how Azure SQL DB customers can simply secure their data using our most advanced features to-date including Always Encrypted, Transparent Data Encryption, Dynamic Data Masking and SQL Threat Detection.
Virtualization has had a major impact on computing. While data professionals have adjusted to this for database servers, many BI workloads have moved there as well. Learn about the impact of virtualization on BI.
In this session, we are going to look at a typical machine learning process and how to apply it to real world data. We are going to use Azure Machine Learning to transform data and ideas into models that are production ready in minutes.
In this talk, you will get an overview on what to think about when storing data in a document database.
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