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.
See how to analyze images in your Data Lake with Azure Data Lake Analytics, U-SQL and custom models
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
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.
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. Azure Databricks is a fast, easy and collaborative Apache Spark-based analytics service that can be used for big data analytics and artificial intelligence (AI) solutions. This session will cover the architecture patterns for using the two in synergy.
The Microsoft Power BI and Analytics team present an interactive Q&A session
Azure Stream Analytics is a fully managed serverless offering that enables customers to perform real-time data transforms and hot-path analytics using a simple SQL language. In this session, we will show how to combine SQL reference data to augment data coming from devices and create real-time alerts, leverage partitioning to write data to SQL at high speed, and create real-time dashboards.
Azure introduces a range of new services for transaction processing and analytics solutions which mean we don’t need to deploy virtual machines. This session provides insight into how we see customers deploying evergreen and futureproof solutions.
Why do you need anything more than SQL Server? We will discuss what factors influence platform choices, such as data and processing scaling and open source tool chains that include Hive and Spark.  This session will include a quick overview of HDInsight and some of the tools that SQL Developers can use to interact with it, as well as some best practices and gotchas. There will be a short demo on some of the tool choices.
Learn about how to future-proof your modern data warehousing environment to meet the needs of the business for the long term; as well as how to overcome common data warehousing challenges, the related must-have technology solution.
A little bit of knowledge about how SQL Server works can go a long way towards making large data engineering queries run faster.
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
If you are a DBA and want to get started with Data Science, then this session is for you. This demo-packed session will show you an end-to-end Data Science project covering the core technologies in Microsoft Data + AI stack.
We will showcase the latest feature of SSIS 2017 such as connectors for Azure Data Lake Store (ADLS), Azure SQL Data Warehouse (SQL DW), SSIS Scale-Out at package level for the box product as well as the SSIS package execution on Azure Data Factory
This session takes a closer look at Azure Stream Analytics, and how you can make it work in your Projects.
Selecting the right PaaS components in Azure
Common performance issues with clustered columnstore index experienced by customers and strategies to address them.
See the Magic of high-end analytics on any device, on any data source, using any database. Built in machine learning makes sophisticated analytics simple. Collaborate across the enterprise with easy implementation and real self-service analytics.
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.
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