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.
Text analysis of financial documents using R & Power BI.
I walk through the database services in Azure and how to characterize a workload to make the right DBaaS selection for your use case.
Pyramid Analytics BI Office’s Enterprise BI Platform and PrecisionPoint’s data warehousing solution for MS Dynamics ERP systems combine to deliver simplified access to key Business and Financial data.
Learn the ins and outs of Azure Data Lake in this deep dive
Industry independent analysts say “Pyramid Analytics is essentially the best-in-class, on-premises offering for Microsoft BI today and tomorrow” What does this mean for you and PowerBI?
Demonstrating an end to end IoT solution providing real-time sensor data from a Raspberry Pi into an Azure IoT Hub, through Stream Analytics, then with outputs to Power BI and SQL DB. Learn how to build this simplified IoT solution from scratch.
The world’s largest enterprises run their critical business operations on SAP. See a demo of how to replicate SAP data to the Microsoft platform, avoiding the challenges of the complex underlying data models and structures.
SQL Server vNext R Services and the new MicrosoftML package
We have assembled a hub of technical experts from SQLCAT, Tiger team, Product Group, Customer Support Services (CSS) and others. We'll have folks from across all our data, BI and Advanced Analytics teams plus some managers - ask us what you want.
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
See how Azure Data Factory orchestrates the movement of data in Azure, with a case study from a Microsoft partner.
Loved by business users, yet loathed by IT, Excel is the world’s most popular analytics tool by far. See how XLCubed helps users work smarter in data-connected Excel and Web, while addressing IT concerns about Excel’s role in the BI landscape.
Join Joseph for a fascinating look into SQL Server 2016 and its unmatched innovation across on-premises and the cloud to help you turn data into intelligent action
Analytics is all about having a good set of analytical tools. Last year at Sqlbits I outlined 4 such tools, this session will expand on that set. We’ll cover Dark Data, Probability calculations, RFI
Buck Woody will lead you around the Cortana Intelligence Suite showing you how to create intelligent actions from data.
There's base R, modern R, the Hadleyverse, R in SQL, and more - understanding where to begin can be an exercise in frustration and cost you valuable time. This session takes you through the R you need to be productive quickly.
Lindsey Allen will peel back the curtain on SQL Server 2016 new feature Operational Analytics. We continue to invest heavily in in-memory technology and have now introduced one of the more important features we are releasing as part of SQL Server.
Learn from a real customer how they developed and deployed a series of BI Office solutions to support various business functions including: Finance, Web Analytics Supply, Pricing.
With R in SQL Server 2016, data engineers (DBAs, developers) are expanding their horizons and deriving more value for the business from their data. Come see how this is brining people in business together.
Come to this session to learn how to turn your existing applications into intelligent applications using the R integration into the SQL Server engine.
Learn how BI Office from Pyramid Analytics can turn your company into a data driven organisation heading for the stars, whilst combining the best of PowerBI on Premises.
This sessions covers the SQL Server 2016 Hero Features and you will see a real customer scenario demo showcasing the Full Lifecycle of Financial Data.
In this session, we will look at Predictive Analytics using SQL Server 2016 and R, using our boozy day at the Guinness factory as a backdrop to understanding why statistical learning is important for analytics today. Drinking Guinness is optional!
In this session we will look in detail into some of the features for the Power BI designer and take it for a spin and investigating some of the new capabilities it has to offer for different relationships and new DAX formulas.
There are some little-known but very useful ways of extracting information from data. This session will cover: • Monte Carlo simulations • Nyquist’s Theorem • Simpson’s paradox • Benford’s Law These will rock your world (they certainly rocked mine).
Azure stream analytics is a new Azure service for analyzing streaming data.
Azure Machine Learning is a fully managed cloud service for predictive analytics. In this 1hour session, we will run through; ML Studio,using data, Creating and running Experiments, visualising results, R , publishing and using experiments.
Come to this session to see how to build a scalable analytical solution on Microsoft Azure with Elastic Search and Kibana.
Does the concept of analytics make you nervous? Are you being asked to do more advanced analysis with existing tools? Come learn how to take your predictive capabilities to the next level with new azure technologies.
According to The New York Times, data scientists may spend as much as 80% of their time on “data janitor work.” Attend this session and discover five ways you can become the data hero in your organisation.
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