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.
A technical overview of Azure SQL Data Warehouse Gen 2. SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that leverages Massively Parallel Processing (MPP) to quickly run complex queries across petabytes of data.
Customers have feelings and by harnessing the power of deep neural networks, we can derive emotional insight from their data and use this to improve. Attendees will leave understanding how to connect different types of data to cognitive services.
This talk will address how to add the unit testing framework tSQLt to the database deployment pipeline. The purpose is to reduce the cost of validate every change in the database with a fully automated pipeline.
We introduce the concept of aggregation, we show several examples of their usage understanding the advantages and the limitations of aggregations, with the goal of building a solid understanding on how and when to use the feature in data models.
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
This session will take a look at better Unicode support, query processing improvements for row store tables, secure enclaves, and other neat things you'll find useful as a modern database administrator or developer.
A walk-through on what is possible analyzing your data with the "R" language.
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.
Find out how a major UK hotel chain unified their wildly different sources of data to build a supercharged analytics and pricing engine to power their business. Find out how Cosmos and Databricks helped them get to know their customers, how best to retain them, and how best to keep them happy, all while ensuring GDPR compliance and the right to be forgotten.
Do you want to know why customers chose Cosmos DB? Come learn about the business goals and technical challenges faced by real world customers, and learn about key Cosmos DB features so you can help your customers deliver their high-performance business-critical applications on Cosmos DB.
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.
See how to analyze images in your Data Lake with Azure Data Lake Analytics, U-SQL and custom models
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.
Azure Machine Learning is a platform for developing and deploying your machine learning models on Azure. We will look at the life cycle of ML projects: from data, to model, to consumption. This will include Automated Machine Learning capabilities.
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.
Learn in 75 Minutes what Batch Execution Mode is, when & how it will affect your workloads (in upcoming SQL Server 2019 & Azure SQLDB) on the traditional Rowstore Indexes.
Look inside Query Store to see what it does and how it works
Join me in this session and learn how to capture a production workload, replay it to your cloud database and compare the performance. I will introduce you to the methodology and the tools to bring your database to the cloud without breaking a sweat.
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