Sessions, Agenda & Schedule

Important Notes. Please Read Carefully.
1. Final sessions schedule & agenda is released. Session Count: 104.
2. Browse the interactive dashboard to explore DPS 2018 content – its AMAZING. Click Here.
3. Final Schedule & Agenda: (Excel | PDFHTML)
4. Download Session List: (Excel | PDF | HTML)
5. Breakout sessions are of 75 minutes each. Chalk-Talks & Open-Talks are 30 mins each.
6. Data Platform Summit 2018 has multiple parallel tracks with specific abbreviations: Administration / Database Administration (DBA), Development / Data Driven Development / Application Development (DEV), Architecture (ARCH), Business Intelligence & Advanced Analytics (BIA), Big Data (BD), Data Science / Artificial Intelligence / Machine Learning (DS), IoT, NoSQL & Open Source (IoT, NoSQL, OSS), & Career Growth (CG)
7. Use the Search box on the top right corner of the table. You can search on any keyword including session type, speaker name, session title, track, level, etc. The search is instant and also searches within the abstract.
For example, if you type “Azure”, all sessions that have “Azure” in their title or abstract will be displayed.
8. There might be minor adjustments to the sessions, agenda & schedule without any prior notice.
9. Errors/Corrections? Write to admin[at] & satya[at]
10. Learn more about DPS 2018 registration.
11. The pre-cons (full-day in-depth classroom training) has limited seats now. Learn more about Pre-Cons.

Download Session List: (Excel | PDF | HTML)  |  Download Agenda: (Excel | PDFHTML)

Break-OutAbhishek NarainBIA/BDBuilding your Big Data and Advanced Analytics Pipeline on Azure using Azure Data FactoryIntermediateAbstract: In this session, we will focus on Azure Data Factory's orchestration capabilities and how it could meet the ETL needs for Big Data and Advanced Analytics projects. We will use User Interface to create ETL/ ELT pipelines and use Azure Databricks for transforming the data. We will see some end-to-end demos on how Customer's leverage Azure Data Factory's Control flow and Data flow in their Data pipelines. Security and Performance will be the major consideration in this session. Key Learning: - ETL/ ELT pipeline orchestrating service on Azure (Azure Data Factory) - Operationalizing Databricks on Azure Demos: - End-to-end Pipeline pulling data from on-premise and Transforming it on Cloud and loading it in a DW - UI authoring in ADF - Databricks notebook orchestration in ADF - Parameterization support and control flow in ADF
Open-TalkAbhishek NarainBIA/DEVAzure Data Factory – Customer stories and RoadmapN/AOpen-Talks are 30 minutes free-flowing discussion on a specific topic. No laptops, no PPTs, no demos – only discussion and Q & A
Break-OutAjay JagannathanDBA/ARCHDeep Dive into Azure SQL Database Managed InstanceAdvancedAbstract: Azure SQL Database Managed Instance is a new capability of Azure SQL Database, providing near 100% compatibility with SQL Server on-premises (Enterprise Edition). Managed Instance allows you to fast track your existing SQL Server workloads to the cloud through lift and shift of on-premises applications with minimal application and database changes. In this session, we will deep dive into various capabilities of Managed Instance through demos covering how to provision, scale-up/down, point-in-time restore, linked servers, SQL Server Agent and SSIS ETL using ADF which are otherwise not possible in single/pools deployment options of Azure SQL Database.
Break-OutAjay JagannathanARCH/DBAAzure SQL Database – the intelligent cloud database on autopilot that lets you focus on your businessAdvancedAbstract: Azure SQL Database is Microsoft's fully managed, database-as-a-service offering based on the world’s best relational database management system, SQL Server. It consists of three deployment options - managed singleton database, managed elastic pools and managed instances that will help you with your data estate modernization. In this session you will see how Azure SQL Database, uses machine learning and best practices to ensure your database is always performing at its best. In this session you will learn about features like Adaptive Query Processing, Auto-tuning and Performance Recommendations, to see how Azure SQL Database can help you spend more time developing applications and less time managing your databases. You will also learn about new security features like Vulnerability Assessment, Information Protection, Thread Detection and Always Encrypted to see how Azure SQL Database is securing your data in the most secure database on the planet. With Azure SQL Database, you can focus on your business and leave the rest to us. Key Learning: 1. Learn how performance capabilities such as Adaptive query processing, auto tuning and performance recommendations help alleviate common database administration headaches 2. Learn how to easily secure your database using features like Vulnerability Assessment, Information Protection, Threat detection and Always Encrypted. Demos: 1. Auto tuning 2. Performance recommendations 3. Vulnerability Assessment 4. Information Protection 5. Always Encrypted
Break-OutAjay Jagannathan / Sudhakar SannakkayalaARCH/OSSModernize your data estate on Microsoft’s Azure Relational Database Platform (Azure SQL Database, PostgreSQL, MySQLIntermediateAbstract: Azure Relational Database Platform- Microsoft's fully managed, database-as-a-service offering solves the demands of today’s data estate involving omnipresence, heterogeneous and explosion. Built on world’s top relational database management system, SQL Server, as well as popular Community Editions of OSS databases PostgreSQL and MySQL, the platform offers multiple choices to our customers to meet their data needs – it consists of three options of Azure SQL Database (singleton, elastic pools and managed instances) as well as Azure Database for PostgreSQL and MySQL. In this session, you will learn about the latest innovations in Microsoft’s Azure relational database family and how customers are using this to modernize their applications in a number of ways, from simply re-hosting applications and corresponding databases to Azure to refactoring or in some cases re-architecting the application stack in order to take full advantage of the benefits of our managed services in the long run. Our most recent version of Azure SQL Database, PostgreSQL and MySQL combines advanced intelligence, enterprise-grade performance, high-availability, and industry-leading security in one easy-to-use database. Thanks to innovations such as In-Memory OLTP, Columnstore indexes, and our Intelligent Query Processing feature family, customers can rely on Azure Relational Database for their relational data management needs, from managing just a few megabytes of transactional data to driving the most data-intensive, mission-critical applications requiring advanced data processing at global scale. You will also learn how to quickly and seamlessly migrate and modernize your datacentre to the cloud with Azure Database Migration Service. With Microsoft’s Azure Relational Database Platform, you can focus on your business and leave the rest to us. Key Learning: 1. New offerings in Azure Relational Database Platform such as Managed Instance, PostgreSQL and MySQL 2. Capabilities provided by the new services 3. Simplified migration of database and applications from on-premise datacentre to Azure Demos: Migration of databases from on-prem to Azure SQL Database Managed Instance, PostgreSQL and MySQL
Break-OutAmit BansalDEV/DBASQL Server Developer TricksIntermediateAbstract: If you are programming with SQL Server, you ought to know how your query is being treated inside the engine. Join this session to learn some real-world developer tricks that will improve performance of your SQL workload. Understand the DO’s and DONT’s and be a better SQL Developer.
Break-OutAnand RamanDS/DEVAI is the New NormalIntermediateAbstract: AI is now becoming the part of every software workload. Widespread use of mobile devices and powerful personal computing have driven a major shift among organizations of all types to adopt Artificial Intelligence (AI) using scalable, cost-efficient cloud computing infrastructure. In this talk, you will learn about the application of AI technologies in the cloud. We will help you understand how to add pre-built AI capabilities like object detection, face understanding, translation and speech to applications. We will show how developers can build Cognitive Search applications that understand deep content in images, text and other data. We will also show how the platform can be used to build your own custom AI models for predictive applications and how to use the Azure platform to accelerate machine learning. Key Learning: By the end of this session, developers will know how to leverage new tools and resources to build intelligent apps and customize those apps on Azure.
Break-OutAndy LeonardBIA/DEVFaster SSISIntermediateAbstract: Ever wonder why SSIS runs so slow? Watch SSIS author Andy Leonard as he runs test loads using sample and real-world data and shows you how to tune SQL Server 2016 Integration Services (SSIS 2016) packages. We'll start by experimenting with SSIS design patterns to improve performance loading AdventureWorks data. We will implement different change detection patterns and compare execution performance for each. Then, we'll explain a Data Flow Task's bottleneck when loading binary large objects - or Blobs. Finally, we'll demonstrate a design pattern that uses a Script Component in a Data Flow to boost load performance to MySql, whether on-premises or in the cloud. Key Learning: SSIS Data Flow Internals and tips on performance-tuning SSIS Data Flow Tasks. Demos: There are three demos in this session: 1. Change detection patterns for performance 2. Blob load performance 3. Improve MySql load performance using the SSIS Data Flow Script Component.
Break-OutAndy LeonardARCH/BIADesigning an SSIS FrameworkAdvancedAbstract: In this “demo-tastic” presentation, SSIS trainer, author, and consultant Andy Leonard explains the what, why, and how of a custom SSIS framework that delivers metadata-driven package execution. Key Learning: Key takeaways: 1) Attendees will learn a metadata-driven approach to SSIS package execution. 2) Attendees will learn a method for executing packages stored in any Catalog Folder or Catalog Project. Demos: We will "hack" the SSIS Catalog to generate a stored procedure used to execute SSIS packages stored anywhere in the SSIS Catalog.
Open-TalkAnupama NatarajanDS/DEVHow Artificial Intelligence (AI) will transform modern workspace?N/AOpen-Talks are 30 minutes free-flowing discussion on a specific topic. No laptops, no PPTs, no demos – only discussion and Q & A
Break-OutAnupama NatarajanDS/DEVCreating your first intelligent BotBasicAbstract: What are bots? Why there is so much talk about bots? How do they work? How can I build an Intelligent bot? Come to this session to get answers for the above questions. In this session you will get to learn on how to build a Bot using Azure Bot Service and add intelligence using Cognitive Services and Machine Learning. You will also get to learn how to integrate your bot with Skype, Slack, Microsoft Teams and other platforms. Finally we will secure the bot using Azure Active Directory. Key Learning: Audience can come and get to learn about how to develop an intelligent bot using Azure Demos: Develop a appointment booking bot and train the bot using Azure Machine Learning and adding intelligence to it using Cognitive Services
Break-OutAnupama NatarajanBIA/DEVDeep Dive into SSRS2017 REST APIIntermediateAbstract: The SSRS 2017 REST API provides programmatic access to the objects in a SQL Server 2017 Reporting Services report server catalog. The REST API exposes endpoints to navigate the folder hierarchy, discover the contents of a folder, or download a report definition. In this session you will learn a) How to connect to SSRS Report Server catalog using REST API? b) How to use the different REST API endpoints to access Datasets, Paginated reports, Mobile Reports, Folders, Comments, Subscriptions? c) How to test API Requests and Responses using Postman? d) How to embed SSRS reports in ASP.NET MVC Core applications? Key Learning: Learn how to query and use the new SSRS 2017 REST API in your applications Demos: Demo a ASP.NET Core Web Application that loads all SSRS 2017 reports using the SSRS 2017 REST API
Break-OutArun Khetarpal / Kapil RajaDEV/ARCHHow to take advantage of scale out graph in Azure Cosmos DBBasicAbstract: Real-world data is naturally connected. Learn how to create graph database applications on Azure Cosmos DB and explore the different solutions that it provides to common data scenarios in the enterprise. We will also cover customer cases that currently leverage graph databases in their day-to-day workloads.
Break-OutAvanish PanchalDBA/ARCHAzure ARM & Azure PowerShell for SQL Server AutomationIntermediateAbstract: Microsoft Azure cloud adoption is on accelerating fast. Azure Automation plays a key role in building a sustainable and repeatable framework for creating and managing SQL Server in Azure. Automation is very much in demand compared to monolithic deployment models to maximize the ROI (return on investment). Session is suitable for Cloud DBA, support engineers & IT Strategists.
Break-OutBen WeissmanBIA/DBAThe Self-Tuning SSIS PackageBasicAbstract: There is a natural limit to how many dataflows you can run in parallel in SSIS. Regardless of whether your limit is on the source or destination side, you will eventually reach those limits. You might have set up all your package orchestration in a way that made perfect sense at that time, but over time, some tables grow faster than expected and others don’t grow at all. Due to foreign key relationships, you may not be able simply to shuffle the dataflow tasks around to maximize throughput. Manual reengineering along these lines would potentially be very time consuming, and even worse, the result would be obsolete shortly thereafter. This session is about using the Business Intelligence Markup Language (Biml) to monitor and control your orchestration patterns. By automatically analyzing the results in ETL logs, we’ll be able to automate our staging orchestration! Key Learning: Audience will get a basic understanding of Biml (the Business Intelligence Markup Language), then we'll focus on how we can use it to automatically improve SSIS performance Demos: - Optimizing unit of work (how many Containers to run) - Pattern Evaluation (which connector etc. is best) - Dependencies (get FK Constraints and automatically lay the package out based on it)
Break-OutBen WeissmanBIA/ARCHETL and DWH Design with MetadataIntermediateAbstract: This session is all about getting away from manual SSIS packages. Instead of reinventing the wheel every time you need to change or extend a package, let’s talk about metadata models and how we can use them to design and describe our data warehouses and packages. We will cover the gamut from initial design, to maintenance and support, and even documentation and compliance. In addition, we’ll see how the Business Intelligence Markup Language (Biml) can help us translate our metadata into a ready-to-use SSIS solution! Key Learning: good understanding about the importance of metadata, especially in a world where more and more Scenarios are migrated from on premise to the Cloud Basic understanding of Biml Tips and Tricks on how to automate SSIS and ADF development based on Meta Data Demos: Build an on premise SSIS solution from metadata, then build the same solution for ADF from the same metadata
Chalk-TalkBen WeissmanBIA/DEVWhat’s in an ensemble? An intro to Data VaultN/AChalk-Talks are 30 minutes’ sessions focussing on conceptual & architectural understanding, that too with only whiteboard and marker. No Laptops, no PPTs, no demos – only whiteboard-ing!
Break-OutCasey KarstBD/DEVAzure SQL Data Warehouse and Azure Databricks: Integration for the Modern Data WarehouseIntermediateAbstract: As your cloud data warehouse grows, you need to make sure that your implementation is future proof. In this session, you will learn how customers are using Azure SQL Data Warehouse and Azure Databricks to tackle some of the most complicated problems in the industry at scale. Key Learning: After this talk the audience will know: – Overall architecture of SQL Data Warehouse – Architecture of Azure DataBricks – Implementation details on the Azure SQL DW and Databricks Connector. – Use cases and scenarios for how and when to use both services to create Modern Data Warehouse Demos: I will have two demos: The first will be an ETL pipeline using Databricks to clean data and write it into SQL DW. The second will be using the connector to pull data from SQL DW and use databricks to do Machine Learning over the data.
Chalk-TalkDamian WideraDBA/BIARow Level Security now and in the past - my working solutions N/AChalk-Talks are 30 minutes’ sessions focussing on conceptual & architectural understanding, that too with only whiteboard and marker. No Laptops, no PPTs, no demos – only whiteboard-ing!
Break-OutDamian WideraDBA/DEVTravelling in time with SQL Server 2016IntermediateAbstract: SQL Server 2016 comes up with a very exciting feature called Temporal tables. You can make queries to historical data lot easier by using this feature. The mechanism is very simple however you all should know it in depth to make sure you can use it efficiently. And this is exactly what I am going to do during this session – show you how to create temporal tables, how to use and manage them Key Learning: WHat is Temporal Tables How to use them How to use them in Hybrid scenario How to manage & optimize Demos: I do ONLY demos, no slides (almost)
Break-OutDamian WideraBIA/DEVU-SQL in great depthIntermediateAbstract: I would like to invite to the session about Microsoft Azure Data Lake and the USQL. I would like to show how quickly you can do data analysis using traditional C# and a new language that is a bit similar to the TSQL. I will also show more complicated things -how to run Python and R scripts to perform even more robust analysis Key Learning: USQL - how to start, how to use it, how to manipulate a large datasets, how to play wirh R and python Demos: I do demos, no slides
Break-OutDenny CherryDBA/ARCHStorage for the DBAIntermediateAbstract: One of the biggest issues in database performance centers around storage. It’s also one of the hardest places to troubleshoot performance issues because storage engineers and database administrators often do not speak the same language. In this session, we’ll be looking at storage, both on premises as cloud, from both the database and storage perspectives.
Break-OutDenny CherryDBA/ARCHHow to Maintain the Same Level of utilities in Cloud Deployments - Securability, Reliability and Scalability”.IntermediateAbstract: In this session we will review the differences between deploying Microsoft SQL Server in Microsoft Azure and on-premises from a Security, Reliability and Scalability perspective. Join us for this fun session and learn how to improve the security, reliability and scalability of your Azure deployments of SQL Server Key Learning: We'll review the common mistakes which people make when deploying SQL Server Virtual Machines to Azure which can lead to security problems including data breaches. We'll review the common performance problems which people encounter, and how to resolve them. We'll review the common scalability misunderstandings of Azure and SQL Server Virtual Machines.
Break-OutDevashish SalgaonkarDBA/ARCHDeploy and Manage SQL Server Configuration as a CODEIntermediateAbstract: Deploying and managing SQL Servers configuration on a large scale can be a daunting task even for the most experienced of DBA's. In today's cloud world, where speed and agility is a key driver, less manual touch and more automation is the way to go forward. Azure Automation Desired State Configuration (DSC) provides a highly available configuration management solution. It allows DBA's and DevOps Engineers to manage SQL Server configuration as a Code and opening possibilities to automate and reduce unwanted change in their environment. Key Learning: In this session we will explore, how you can consistently deploy, reliably monitor, and automatically update the desired state of all your SQL Servers, at scale from the cloud. It will help the audience to answer the below questions - How do I ensure all of my SQL Servers are matching their intended configuration and remain in the correct state? How do I prevent SQL Server configuration from drifting from the desired state, due to changes made by people, process, and programs? How do I ensure new SQL Server deployments match the configuration of existing deployments? How do I do all of the above, consistently, across on-premises SQL Servers and those in public clouds? Demos: Demos Overview and setup of Azure Automation DSC Deploying SQL Server using Azure automation DSC Managing configuration drift using Azure automation DSC
Open-TalkDinesh PriyankaraBIA/ARCHWhy should we focus more on logical data warehouse than physical data warehouse?N/AOpen-Talks are 30 minutes free-flowing discussion on a specific topic. No laptops, no PPTs, no demos – only discussion and Q & A
Break-OutDinesh PriyankaraBD/DEVMaking unstructured data analysis-ready using Data Lake AnalyticsIntermediateAbstract: Unstructured data is not something infrequent now and not ignorable, it is becoming a part of almost all data-oriented solutions. Therefore, we just cannot ignore them and continue our journey, we should learn how to process them and make them as part of our data solutions. However, unfamiliar platform and technologies such as Hadoop, Hive, Pig, even HDInsight slow down our joining with this arena. But no need to worry, Microsoft has given us a new facility to work with unstructured data using familiar technologies, yes, it is Azure Data Lake Analytics. Join this session to understand Data Lake Analytics, its internal work, difference between Hadoop and ADLA and, how it can be used for processing text-heavy row-oriented unstructured files and images. Key Learning: What is semi-structured and unstructured data What is Hadoop and related sub projects What is Azure Data Lake Analytics and how it helps on processing data What is U-SQL and how to use it for processing data Demos: Creating an Azure Data Lake Analytics account- this shows the way of creating it and areas to be considered Processing unstructured text files using U-SQL, along with Processing images using U-SQL, along with ML libraries
Chalk-TalkDmitri KorotkevitchDBA/DEVTips and tricks for successful In-Memory OLTP implementationN/AChalk-Talks are 30 minutes’ sessions focussing on conceptual & architectural understanding, that too with only whiteboard and marker. No Laptops, no PPTs, no demos – only whiteboard-ing!
Break-OutDmitri KorotkevitchDBA/DEVData Partitioning for Database Architects and Mere MortalsIntermediateAbstract: Data partitioning is the valuable technique that may dramatically simplify database administration and improve system availability and performance. Contrary to popular believe, it is not limited to partitioned tables and may be implemented with any version and edition of SQL Server. This session demonstrates the data partitioning techniques available in the various editions of SQL Server and discusses benefits, downsides and best use-cases for them. The session will also show how data partitioning helps boost performance of systems handling a mixed workload, improves cardinality estimations with large tables, and reduces the system’s storage cost. Demos: Multiple demos (40-50% of content)
Break-OutDmitri KorotkevitchDBA/DEVDeep Dive into Blocking and Deadlocks TroubleshootingIntermediateAbstract: SQL Server Concurrency Model is, perhaps, the most confusing and least understood part of SQL Server Internals. Blocking issues and deadlocks occur unexpectedly and negatively impact performance and user experience in the systems. Nevertheless, this model is well-structured and easy to understand when you analyze it from lock types and their lifetime and compatibility standpoint. This, two-part session will explain why blocking and deadlocks occur and how to troubleshoot them in your environments. First, it will provide the overview of SQL Server Concurrency Model and describe SQL Server locking behavior and root-causes of typical blocking issues. Next, the session will discuss how to capture and troubleshoot them using standard SQL Server tools, and how to simplify the analysis using Blocking Monitoring Framework developed by Dmitri.
Break-OutEdwin SarmientoDBA/DEVLeveraging Microsoft PowerShell for Managing SQL Server VMs on Amazon AWSIntermediateAbstract: Managing SQL Server VMs on Amazon AWS can be cumbersome if you simply rely on the graphical user interface. Imagine having to manage multiple SQL Server VMs as part of your day-to-day operations. You can leverage Microsoft PowerShell to automate repetitive tasks that involve managing SQL Server VMs on Amazon AWS. In this session, learn how to use the AWS Tools for Windows PowerShell, the different PowerShell cmdlets for managing SQL Server VMs on Amazon AWS and how to write scripts to automate DBA repetitive tasks. Key Learning: 1) Explore the AWS Tools for Windows PowerShell 2) Learn how to manage SQL Server VMs on Amazon AWS using PowerShell 3) Learn how to automate operational SQL Server administrative tasks using PowerShell scripting Demos: Walkthrough of using AWS Tools for Windows PowerShell to deploy and manage a SQL Server VM on AWS
Break-OutEdwin SarmientoDBA/OSSGetting Started with Linux for the SQL Server DBAIntermediateAbstract: With the introduction of SQL Server 2017 for Linux, there is no escaping the fact that SQL Server DBAs need to be familiar with the Linux operating system. So, how do you start? In this session, you will learn the most valuable fundamentals and commands that are important to the DBA when managing a database in a Linux environment. You will learn what is common between Windows and Linux so you can leverage information that you already know to get comfortable with managing SQL server on Linux. Key Learning: 1) Learn the fundamentals of the Linux operating system 2) Learn the differences between Windows and Linux that apply to SQL Server 3) Learn the most common commands that SQL Server DBAs need to know in order to perform administrative tasks in Linux Demos: Managing SQL Server on Red Hat Linux via PuTTy and PowerShell
Open-TalkEdwin SarmientoCG/DBATransitioning from a DBA to a ManagerN/AOpen-Talks are 30 minutes free-flowing discussion on a specific topic. No laptops, no PPTs, no demos – only discussion and Q & A
Break-OutErika MenezesDS/DEVDeep Learning for data geeksBasicAbstract: Deep Learning is a sub-field of machine learning that revolves around the use of artificial neural networks. Deep learning is being used in self-driving cars, medical diagnosis and even music generation. Deep learning needs a lot of data and who better than data geeks? This session will cover: - What is deep learning? - Why deep learning? - How to do deep learning: tools to get started with neural networks - Applications of deep learning – enterprise and beyond - Build a custom deep learning model from scratch No pre-requisites required. Key Learning: - Introduction to deep learning - Tools to get started with neural networks - How to build custom deep learning models
Chalk-TalkErika MenezesDS/DEVBabysitting for machine learning models – how do you know it’s not workingNULLChalk-Talks are 30 minutes’ sessions focussing on conceptual & architectural understanding, that too with only whiteboard and marker. No Laptops, no PPTs, no demos – only whiteboard-ing!
Break-OutErika MenezesDS/DEVAI for FashionAdvancedAbstract: Transfer Learning is a machine learning method where a model developed for one task is reused for other tasks and saves users a lot of time by not having to training deep learning models from scratch. For many computer vision tasks, it is becoming increasingly common to begin with pre-trained models and then fine-tune them to the task in question. This has the benefit of allowing you to get relatively good accuracy without having to pay its high set-up cost — namely, training effort. In this session we will go over how to use transfer learning used for computer vision tasks related to fashion. We will do a deep dive on how to build a fashion matching application. That is, if a user were to take a photo of an outfit, can we find that product in a product catalog? Key Learning: - Basics of transfer learning - Use-cases for AI in the fashion industry
Break-OutHamish WatsonDEV/ARCHBuilding a Database DevOps Pipeline in the Cloud under 59 minutesBasicAbstract: DevOps processes encompass both Continuous Integration and Continuous Delivery. Continuous integration is based around automated builds and tests. Continuous Delivery allows both application developers, database developers and DBAs alike to deploy better quality code/software. In this DEMO heavy session, we will start off with source code only. Over the course of 59 minutes we will build a deployment pipeline that starts off with pushing our database and application code to source control. We will utilize Visual Studio Team Services to automate our build and tests and perform Continuous Integration operations. The output of these operations will be a standardized package of our built solution. Utilizing PowerShell in the form of Infrastructure as Code, we will spin up multiple Azure based environments that will be deployed to using automated Continuous Delivery processes. The finished result will be an automated and reliable deployment pipeline that was built under 59 minutes. Key Learning: The audience will learn the fundamentals of DevOps. The DEMO will be instructive and will showcase the presented philosophies of DevOps by using tools and techniques to build a deployment pipeline for our database. The audience will learn how to use Infrastructure as Code, Azure Resource Manager and Azure PowerShell to automate the setup, configuration and deployment of database/application resources used in a cloud based DevOps pipeline. The audience will gain knowledge around vital steps in a DevOps pipeline - namely: Build and Test Automation (Continuous Integration) Deployment Automation (Continuous Delivery) Demos: The DEMO will take an on premises database and source code for a web app and create a deployment pipeline using git source control, SSDT or Redgate tools, VSTS , powershell and Azure. The outcome is a fully deployable pipeline for our database and applications - all under 59 minutes.
Chalk-TalkHamish WatsonDEV/DBAWays to get your database into Source Control for DevOps deploysN/AChalk-Talks are 30 minutes’ sessions focussing on conceptual & architectural understanding, that too with only whiteboard and marker. No Laptops, no PPTs, no demos – only whiteboard-ing!
Break-OutHamish WatsonDEV/DBATest Driven Development in SQL Server – how to deploy database code saferIntermediateAbstract: Test Driven Development (TDD) is a design approach which has enabled application developers to write cleaner code. It is relevant for database development as it ensures that code produced using TDD and unit tests will be of higher quality which means interactions with data will be safer. TDD is not a new method of unit testing, it is an essential design practice for improving the quality of your deployed code. In this session it will be shown how we can use TDD to design and write better unit tests using open-source frameworks and industry standard tools. These tools can be run within SQL Server Management Studio which means DBAs can also take advantage of TDD and unit test, to ensure more reliable code is deployed to databases. TDD can result in code that can be deployed more reliably and faster when using DevOps processes such as Continuous Integration and Continuous Delivery. A comprehensive DEMO will reveal how Test Driven Development can help you deploy database code safer. Key Learning: The audience will learn what unit tests are and how they can assist with raising the quality of deployed code to SQL Server databases. Being introduced to unit testing is fundamental to learning what TDD can do for us. The audience will learn what Test Driven Development is, how it is a method of both designing better unit tests and running better tests. The DEMO will show real life TDD being used and will result in cleaner code being deployed to a LIVE system. The audience will learn about Continuous Integration (CI) processes and how automated builds and automated testing will make their deployed code safer and of a higher quality. Demos: The DEMO will showcase Unit testing, Test Driven Development and complete Continuous Integration and Continuous Delivery processes that will make deployment of database code changes much easier and safer.
Break-OutHarsh Raj Thakur / Rahulinder SinghBD/DEVBig Data on Microsoft AzureIntermediateAbstract: This session will focus on our learnings from using the Microsoft Azure stack for big data workloads. Currently, Azure Data Lake is the de facto storage for all big data workloads. In the session you will be able to understand how compute and storage are separated, what options are available for compute, how can we do data loads in Azure Data lake using Azure data Factory. and enable insights using Power BI from azure data lake. All this with demos of real life scenarios at Microsoft. Key Learnings/Technologies Covered: Azure Data lake (Gen1 and Gen2) Cloud Design patterns using U SQL and Spark. Azure Data Factory. Power BI
Break-OutHarsh Raj Thakur / Rahulinder SinghBIA/DEVEvolution of the Enterprise Data Platform: Leveraging Azure to get the most out of your dataAdvancedAbstract: Are you interested in understanding how Microsoft builds it’s enterprise data platforms? From SQL Server to Azure Data Lake to Machine Learning, the storage and compute options have evolved over the years. The session will focus on a real life scenario on how we use Azure Databricks along with Azure Data Lake Storage to ingest, store and process a large amount of data and build insights using machine learning techniques. After ingesting, preparing and processing it using databricks, it can be funneled out to other services like Cosmos DB and Power BI. We will also cover how using databricks we can get quick access to Spark and readily take advantage of MLlib libraries without having to set up a Hadoop cluster. We will talk about how data bricks has high-speed access to Azure Data Lake Store, and is integrated with Power BI for query along with a variety of Azure databases (Azure SQL Database, Azure SQL Data Warehouse, and Cosmos DB) for further consumption like machine learning. Also we will cover how Databricks is interwoven to other Azure services, such as Azure Machine Learning, Azure IoT, Data Factory etc. Key Learnings/Technologies Covered: Enterprise data lake Architecture Azure Data bricks Azure Data Factory Azure Machine Learning Azure Data Lake Power BI Azure SQL DW
Break-OutIlyas FDS/ARCHMoving Intelligence to IoT EdgeBasicAbstract: Edge or Fog Computing in the IoT world is not new they have been here for many decades. Edge computing has a significant role in the IoT systems, the connected devices has a massive potential to generate millions of events in terabyte sizes, obviously pushing all this data to cloud for analysis can cause significant bandwidth & storage cost, time etc. The ultimate purpose of connecting these smart devices to cloud is to analyze the big data and apply machine learning models and help us predict and optimize the operations, however connecting to cloud over internet and getting the prediction in an time sensitive environment where milli-nano seconds matter, ML in cloud is not a wise option. In this demo-centric session, we will look at how we can build edge machine using Azure IoT edge (an open source edge middle-ware from Microsoft) and connect, collect, store sensors data at the edge, build machine learning models using Azure ML and deploy those MLs to the edge and predict results closer to the sensors.
Break-OutJanakiram MSVDS/DEVMachine Learning is not Magic - Getting Started with Azure MLIntermediateAbstract: Learning the concepts of Machine Learning and Artificial Intelligence can get overwhelming for developers. The sheer number of ML frameworks, tools, and more importantly the prerequisite of maths and stats can be confusing for beginners. This session will demystify ML for developers through a simple but a real-world use case. We will start with the most familiar tool - Microsoft Excel to build an intuitive ML model before exploring preferred frameworks tools. You will learn the key terminology such as learning rate, cost function, training, and inference involved in applying ML. The session aims to cover everything you need from data acquisition to deploying a scalable ML model in Microsoft Azure.
Chalk-TalkJanakiram MSVDS/DEVWhat is Edge Computing??NULLChalk-Talks are 30 minutes’ sessions focussing on conceptual & architectural understanding, that too with only whiteboard and marker. No Laptops, no PPTs, no demos – only whiteboard-ing!
Chalk-TalkJoe YongBIA/DEVHow we designed Azure SQL DW to support PB-scale data warehousesN/AChalk-Talks are 30 minutes’ sessions focussing on conceptual & architectural understanding, that too with only whiteboard and marker. No Laptops, no PPTs, no demos – only whiteboard-ing!
Break-OutJoe YongBD/ARCHAzure SQL Data Warehouse: your PB scale cloud data warehouse and big data platformBasicAbstract: Azure SQL Data Warehouse is the first truly elastic, Petabyte-scale cloud data warehouse that can be deployed in minutes. This session will walk through key architecture and design concepts that enable these capabilities. We then review some common usage scenarios along with tried and tested design patterns from actual customer solutions. Finally, we will walk through recommended practices for deploying, managing and performing fundamental optimizations that you can employ immediately in your own SQL Data Warehouse solution. Key Learning: Azure SQL Data Warehouse overview and architecture Common solution patterns for building large scale cloud data warehouses How to architect, design and build a Petabyte scale data warehouse How a cloud data warehouse fits in a big data solution Demos: Deploying, managing and monitoring Azure SQL DW
Break-OutJoe YongBD/DBAAzure SQL Data Warehouse: best practices and lessons learnedAdvancedAbstract: Whether you just started with SQL DW or have been running your enterprise data warehouse on it for some time, you may have spent time and effort trying to figure out how to load Terabytes of data per hour or support thousands of concurrent users. You have spent countless hours investigating why do some queries run faster than others though all are accessing the same table. This session will share some of the lessons learned from enteprise customer engagements by the SQL DW product group and SQL Customer Advisory Team (CAT). It will also provide practical recommendations on how to optimize your data warehouse design, monitoring and troubleshooting processes so you can benefit from our experience in working with some of the largest enterprise data warehouses. This session will cover some advanced topics but will be delivered in a way that anybody with intermediate database knowledge will be able to grasp. Key Learning: Key design considerations for performance and scale Common problems and how to avoid or mitigate them Best practices and lessons learned from the largest, most complex deployments around the world Demos: Performance impact of poorly designed/managed tables Troubleshooting techniques High performance data loading
Break-OutJoey DántoniARCH/DBAIntroduction to Azure InfrastructureBasicAbstract: In this session, we’ll review all the various infrastructure components that make up the Microsoft Azure platform. When it comes to moving SQL Server systems into the Azure platform having a solid understanding of the Azure infrastructure will make migrations successful and making support solutions much easier. Designing your Azure infrastructure properly from the beginning is extremely important. An improperly designed and configured infrastructure will provide performance problems, manageability problems, and can be difficult to resolve without downtime. As Azure scales around the world many more companies, no matter where they are located, will be begin moving services from on-premises data centers into the Azure Cloud, and a solid foundation is key to successful migrations. Key Learning: Overview of Azure Infrastructure--storage, networking and VMs, and how to configure for different tups of platforms. Demos: Demo of using Azure Portal.
Break-OutJoey DántoniDBA/DEVCloud Application Development & DeploymentIntermediateAbstract: Containers have quietly been taking over the world of infrastructure, especially amongst developers and CI/CD practitioners. However, in the database space, container adoption has been lower. SQL Server 2017 introduced the concept of deploying databases into Docker containers. In this session, you will learn the fundamentals of creating containers, learning about Kubernetes for management, and how to further your learning in this new and emerging space. Key Learning: Learn about containers, and how they work with databases. Demos: Demonstrate deploying SQL Server on Kubernetes, deploy failover using Kubernetes, and demo a rolling upgrade using Kubernetes
Open-TalkKane Conway / Khushboo GuptaBIA/DEVAnalysis Services Troubleshooting MethodologyN/AOpen-Talks are 30 minutes free-flowing discussion on a specific topic. No laptops, no PPTs, no demos – only discussion and Q & A
Break-OutLeila EtaatiDS/ARCHEnd To End Data Science Solution: Azure ML WorkbenchIntermediateAbstract: Azure Machine Learning is integrated, end-to-end data science and advanced analytics solution. It enables data scientists to prepare data, develop experiments, and deploy models at cloud scale. In this session, the audience will learn how to install the Azure ML workbench and how to set it up. Moreover, an introduction on how to write Python code inside it and what packages can be used will be presented. Next, the process of importing data from different resources will be explained. The audience will learn how to deploy and how to check the run log inside Azure ML workbench. Two main components of Azure ML workbench: Azure Machine Learning Experimentation and Management Services will be explained. This session contains demos for each practice and all steps will be shown. Key Learning: The audience will learn about how to set up Azure ML work Bench, How to get data, How to clean data, how to write Python code and so forth. Demos: This session contains 75% demo, from Azure Ml workbench environment to import data, clean data, modeling, and deploy the models.
Chalk-TalkLeila EtaatiDS/DEVWhat Microsoft AI tools for what use caseN/AChalk-Talks are 30 minutes’ sessions focussing on conceptual & architectural understanding, that too with only whiteboard and marker. No Laptops, no PPTs, no demos – only whiteboard-ing!
Break-OutLeila EtaatiDS/DEVMachine Learning Revolution with Azure DatabricksIntermediateAbstract: Azure Databricks is an Apache Spark-based platform designed for the Microsoft Azure cloud services platform and easy to set up with one-click. It has an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts. Azure Databricks notebook unify all the process from data gathering, model creation, deployment. It easy to connect to spark environment and access to the variety of data stores and services in Azure such as Azure SQL Data Warehouse, Azure Cosmos DB, Azure Data Lake Store, Azure Blob storage and Azure Event Hub. Moreover, the possibility to add advanced analytics capabilities will be available instantly and developer able to share their insights via Power BI. A brief introduction and demonstration on how to work with Azure Databricks will be presented. A demo of doing machine learning project with the help of Databricks on Azure, from getting data from data stores and modeling, deploying algorithm will be presented. Key Learning: what is Azure Databricks, how we can use it for machine learning and connecting it with other Azure components? Demos: all the process of setting Azure Databrick up will be presented, how to use it for machine learning and how to use it will be presented
Break-OutMadhan GajendranARCH/DEVTechnical Overview of Azure Cosmos DBBasicAbstract: In this technical overview of Azure Cosmos DB you will learn how easy it is to get started building planet-scale applications with Azure Cosmos DB and benefit from the platform’s turn key global distribution, guaranteed low latency access and elastic scale. We’ll then take a closer look at important design aspects around global distribution, consistency, and server-side partitioning. How to model your data to fit your app’s needs using tools and APIs you love.
Chalk-TalkManish SharmaNoSQL/ARCHHow to do infinite Scale in Azure CosmosDB?N/AChalk-Talks are 30 minutes’ sessions focussing on conceptual & architectural understanding, that too with only whiteboard and marker. No Laptops, no PPTs, no demos – only whiteboard-ing!
Break-OutManish SharmaARCH/DEVHow to migrate your existing MongoDB/ SQL to Azure Cosmos DBBasicAbstract: Bring your Mongo DB applications to Azure Cosmos DB and benefit turnkey global distribution, guaranteed low latency for cloud scale. Learn how easy it is to migrate your existing NoSQL applications to Azure Cosmos DB by using the MongoDB API. In this sessions you will find out about the tools used in the process, see the code that leverages Azure Cosmos DB, and learn about techniques for working around the differences between the products.
Break-OutManohar PunnaBIA/DEVReal-Time Analytics with Power BIIntermediateAbstract: Power BI helps you build appealing visualizations of your data. With ever growing footprint of data it is equally important to get real-time insights into your data. It can be as simple as monitoring a single metric or viewing real-time sales performance across multiple locations. Power BI real-time streaming enables you to stream data and update dashboards in real-time. Any time sensitive data can be a source of streaming data set like IoT sensor devices, social media sources, service usage metrics etc. In this session 1. I will dive deep into real-time analytics with Power BI. 2. How to build streaming tiles in Power BI Dashboards. 3. Demo using Power BI REST APIs. 4. Demo using Azure Event Hub and Azure Stream Analytics. Key Learning: Audience will learn and explore different approaches to create live tiles in Power BI Demos: 1. Demo using Power BI REST APIs. 2. Demo using Azure Event Hub and Azure Stream Analytics.
Chalk-TalkManohar PunnaDBA/DEVHow the Data is Stored in a Page in SQL ServerN/AChalk-Talks are 30 minutes’ sessions focussing on conceptual & architectural understanding, that too with only whiteboard and marker. No Laptops, no PPTs, no demos – only whiteboard-ing!
Break-OutNagaraj VenkatesanDBA/DEVAdaptive Query Processing and Automatic TuningBasicAbstract: Automatic Tuning and Adaptive Query Processing are perhaps the two key features of SQL Server 2017 and Azure SQL Database which would influence query optimizer’s plan choices. The following session will provide an overview of the features and explain how it can help to address performance issues like parameter sniffing, plan regressions, lack of indexes or too many indexes without any manual intervention and change of code. Key Learning: Understanding on Adaptive Query Processing scenarios -> Adaptive Joins, Memory Grant Feedback, Interleaved execution Understanding on Automatic Index Correction, Automatic Index Tuning Demos: Demo on Adaptive Joins, Memory Grant Feedback, Interleaved execution,Automatic Index Correction
Break-OutNagaraj VenkatesanDBA/DSR for Database AdministratorsIntermediateAbstract: "R", the latest buzzword in data platform, has all along been viewed as programming language for data scientists. However, "R" can be a handy tool for database administrators too. The presentation will show case how "R" can be used innovatively by Database Administrators to handle some of their common problems like tracking query regressions, capacity planning and detecting performance abnormalities. The session will introduce few algorithms of "R" and showcase how it can be used in DBA's world. The session would serve as a good starting point for DBA's exploring the world of "R"
Break-OutNagaraj VenkatesanDBA/ARCHStretching using PolybaseIntermediateAbstract: To archive older data and to seamlessly query it, SQL Server 2017 offers "Stretch" database as a solution. However, "Stretch" database is an expensive solution which not many can afford. Can "Polybase", the feature to query external data can be used as a cost-effective alternative for archival? Join this session to find out how "Polybase" can be used for archival and what are the pros and cons between "Stretch" Database and "Polybase" Key Learning: Overview on "Polybase" Understand how to archive using "Polybase" Compare Stretch database against "Polybase" Understanding the cost of both the solutions Demos: * Polybase configuration * Archival using Polybase * Using Stretch SQL Database to query archived data
Break-OutNikhil GaekwadARCH/BIADistributing content with Power BIBasicAbstract: Effective ways of distributing content in Power BI using Apps, Mobile, Embed, and SharePoint. Key Learning: Learn about the different tools in Power BI that allow end users to consume content. Focused on Mobile, Embed, Apps, etc. Demos: Power BI Mobile, SharePoint Embed.
Break-OutNikhil GaekwadBIA/ARCHEnterprise BI Deployments and Governance with Power BIIntermediateAbstract: Whether you're planning an enterprise-wide reporting deployment or providing structure to self-service BI activities within teams, Power BI has you covered. Learn about tools for developing, publishing, and managing your BI assets. This session will cover the data gateway, managing report lifecycle, publishing options, administration and governance contols, and end-user capabilities across devices and platforms. Key Learning: Managing and deploying Power BI within an organization. Demos: Power BI service and administration tools.
Open-TalkNikhil GaekwadBIA/DEVEmpowering end users in Power BI – getting the most of out your dataN/AOpen-Talks are 30 minutes free-flowing discussion on a specific topic. No laptops, no PPTs, no demos – only discussion and Q & A
Break-OutNilabja BallDS/DEVBusiness Analytics with Microsoft RIntermediateAbstract: SQL Server Machine Learning Services has the power of proprietary packages to deliver advanced analytics at scale, and the ability to bring calculations and processing to where the data resides. In this session you will learn how to install and configure of Machine Learning Server and R language concepts. It also covers Machine Learning features in SQL Server such as Real Time scoring, in-database processing etc and how to run R scripts from Power BI to data visualization.
Open-TalkPatrick FlynnDBA/ARCHBackup and Restore – Test your knowledgeN/AOpen-Talks are 30 minutes free-flowing discussion on a specific topic. No laptops, no PPTs, no demos – only discussion and Q & A
Break-OutPatrick FlynnDBA/DEVSupercharge your Database Management with DBAToolsIntermediateAbstract: Need to constantly scale your work across more and more Servers? Required to monitor and maintain an increasing array of database instances and versions? Struggling with being able to accurately measure and provide the metrics and reports required by Management? In this session we will investigate how to supercharge the management of your Database Environment via the use of the open source module dbatools.. Used and endorsed many of the Worlds top DBAs , the dbatools collection of 400+ functions can be used to supercharge management of any sized environment and give you back time to concentrate on delivering true Business value! Key Learning: How to use PowerShell and the dbatools module to automate and scale management of Database environment from 1 to 100's of Servers Demos: Following Scenarios will be demonstrated (a) Migration of entire SQL Server instance to new Server and Instance with single line of PowerShell (b) Automated testing of Backups by restoring to new instance and running DBCC CheckDB (c) Validate of Database environment using
Break-OutPatrick FlynnDBA/DBADatabase Corruption - Advanced Recovery TechniquesAdvancedAbstract: Being able to monitor, diagnose and recover from Database Corruption is a critical skill for any SQL Server DBA. In this session we will walk through the techniques required to detect and repair various forms of Corruption Using a number of example corrupted database we will explore how to determine and fix corruption while avoiding some of the common mistakes. This is 400+ level session that uses knowledge of the internal storage structures of SQL Server to Key Learning: Demonstrate Techniques to detect and recover data from damaged or corrupted databases Demonstrate how knowledge of SQL Server internals and physical storage structures can be used to perform data recovery from Damaged databases Demos: Session is based around multiple demos showing (a) Impact of Page Checksums on Backup and Restore Integrity (b) Use of Advanced restore options to recover with minimal data loss or downtime (c) Use of DBSS Page and DBCC Ind to restore data directly from Damaged Pages (d) Use of fn_dblog and log file internals to recover data directly from log file
Break-OutPeter MyersBIA/ARCHThe A-Z of Power BI DashboardsBasicAbstract: Power BI dashboards enable the display of a consolidated view across the organization, regardless of where the data is stored. A dashboard consists of tiles, with each displaying a value or data visualization. At last count, there were 10 different ways to add a tile to a dashboard. In this session, everything you need to know about Power BI dashboards will be covered. This includes describing and demonstrating each of techniques used to add tiles, and how to ensure that dashboard data remains current. In addition, techniques to share and integrate dashboards, and to deliver real-time dashboard tiles will be introduced. Key Learning: In this session, you will learn: • How to create dashboards • How to add dashboard tiles • How to effectively share dashboards • Tips and tricks to produce and manage great dashboards Demos: A demonstration of each of the 10 “add tile” techniques.
Open-TalkPeter MyersBIA/ARCHHow Best to Share Power BI ContentN/AOpen-Talks are 30 minutes free-flowing discussion on a specific topic. No laptops, no PPTs, no demos – only discussion and Q & A
Break-OutPeter MyersBIA/DEVPower BI Embedded AnalyticsIntermediateAbstract: Power BI supports embedding content into your apps. Content can consist of reports, specific report visuals, dashboards, specific dashboard tiles, and the Q&A experience. In this session, you will learn about the potential to embed Power BI analytics, and see a series of demonstrations that produce a single solution. This session is targeted at experienced application developers. Key Learning: In this session, you will learn: • How to embed Power BI content • How to leverage features supported by the Power BI JavaScript API • How to apply row-level security (RLS) • About Power BI licensing options for Power BI embedding Demos: A series of demonstrations will show embedding, implementing JavaScript API functionality, and securing data.
Break-OutPhilip SeamarkBIA/DEVPractical DAX for Power BIIntermediateAbstract: A walkthrough showcasing a wide variety of practical tips and tricks using DAX with Power BI.  This will cover data-generation, summarisation, pivots, complex joins, calculation optimisation and much more.   Key Learning: Practical tips and tricks by well known author on how to improve your calculations Demos: Not sure what to add here
Break-OutPhilip SeamarkBIA/DEVData modelling for Power BI using brand new Analysis Services featuresAdvancedAbstract: A session covering some of the new data modelling features for Power BI .  These will likely be public come the summit, so a session to demo and cover these in more detail.  Features such as incremental refresh, aggregations and more. Key Learning: Do see how some of the fantastic new features look Demos: Not sure what to put here
Open-TalkPraveen SrivatsaDS/DEVMonetizing AI ServicesN/AOpen-Talks are 30 minutes free-flowing discussion on a specific topic. No laptops, no PPTs, no demos – only discussion and Q & A
Break-OutPraveen SrivatsaDS/DEVMachine Learning in Medical RehabilitationIntermediateAbstract: One of the key areas that machine learning can make a big impact is in the area of medical rehabilitation. In this session, we will look at the complete life-cycle of a medical rehabilitation platform and how it uses IoT data, VisionAPI and sentiment analysis to provide a comprehensive patient view to the doctors. Key Learning: In this session, the audience will learn how technologies like IoT, VisionAPI and sentiment analysis can be put together to analyze a patient’s rehabilitation in the real world.
Chalk-TalkRaj Pochiraju / Mukesh KumarDBA/DEVDeep dive to Azure Database Migrations (DMS) service architectureN/AChalk-Talks are 30 minutes’ sessions focussing on conceptual & architectural understanding, that too with only whiteboard and marker. No Laptops, no PPTs, no demos – only whiteboard-ing!
Break-OutRaj Pochiraju / Nikhil PatelDBA/ARCHMigrating to Azure: Moving from on-premises SQL Server and Oracle databases to AzureAdvancedAbstract: Azure SQL Database and Managed Instances allows you build globally scalable applications with extremely low latency. Azure SQL Database is the best cloud database offering in market. In this session, we will take a detailed look at the migration life cycle and show you how we have made it easy to migrate SQL Server and Oracle instances to Azure by using the Azure Database Migration Service and related tools. We will also cover most commonly seen migration blocking scenarios and demonstrate how our service can unblock your migration to Azure SQL Databases. We will give you a deep dive how to perform scale migrations using our CLI components. After completing the session, you will understand how easy it is to migrate from SQL Server and Oracle to Azure database platforms when empowered with the right tools, services, and best practices. We will also showcase lift and shift migrations from open source databases MySQL and PostgreSQL to our Azure Database platform. Key Learning: 1. You get proficient with our new DMS service, use in your upcoming upgrade/migration projects. 2. You will be able to pinpoint breaking and behavior changes and deprecated features upgrading to Azure SQL Database. 3. Execute the end-to-end database migration seamlessly, includes schema and data Demos: 1. Migrating databases from SQL Server and Oracle to Azure SQL Databases and Managed Instances using our DMS service 2. Migrating databases from Oracle to Azure SQL Databases. 3. Perform minimal downtime migrations to Azure using DMS service. 4. Perform minimal downtime migrations from MySQL to Azure Database for MySQL using DMS 5. Perform scale migrations using our DMS CLIs.
Break-OutReza RadBIA/ARCHPower BI On Premises - Report ServerIntermediateAbstract: Come to this session to learn all about using Power BI in an on-premises solution named Power BI report server. you will learn about all limitations and advantages of Power BI report server through many live demos. Key Learning: Using Power BI on-premises Demos: many live demos
Open-TalkReza RadBIA/DEVStarting Power BI? Come and have a chatN/AOpen-Talks are 30 minutes free-flowing discussion on a specific topic. No laptops, no PPTs, no demos – only discussion and Q & A
Break-OutReza RadBIA/DEVM and Power Query Beyond LimitsAdvancedAbstract: Power Query is the transformation engine in Power BI. This is the engine that you do all data preparation before loading data into the model. This is the keystone of your Power BI solution. In this session, you will learn what things Power Query can do. M is the language behind the scene of Power Query, you will learn how M can be more beneficial than the Power Query Graphical interface. You will see demos of transformations that you can do with M Code. This session is full of live demos. Prepare to be amazed with what Power Query can do with heaps of demos in this session. Key Learning: Power Query Power BI Data Preparation with Power BI M Demos: heaps of live demos through the session
Break-OutSandeep AlurDS/DEVDeep Neural Networks – Let us build a Image/Currency ClassifierAdvancedAbstract: Data comes in various shapes and sizes, and so are expected outcomes from a business standpoint. Given the fact that ‘Artificial Intelligence’ has stood up to solving complex industry problems, computer vision(image) is one area where significant innovation is underway. This is where Deep Learning architectures such as Deep Neural Networks step in and give us techniques to build a model targeting a use case under consideration. Join this session to understand the basic building blocks of a Neural network and peek into building a model that helps one classify images/currency. Quite a few techniques are involved in the process and we will delve into technicalities of building such a solution. Critical component of this journey is the tool set and the compute environment required to run the neural network. You will get acquainted with what we have on Azure in building AI solutions. Key Learning: Below are the key take away for the audience - Understanding of Deep Learning - Peek into an image classifer
Break-OutSandip PaniDS/DEVBuild Predictive model using Azure Machine learning StudioBasicAbstract: If you are new to Machine learning, don;t know where to start, what is the difference between ML and AI, and you want to learn how to start, Then this session is for you. In this session I will explain what is machine learning, What is AI? Life cycle of a machine learning project. How to train a predictive model using Azure Machine learning studio. If you think you need deep understanding of statistics to start your Machine learning project then don't worry I will cover minimum important and fundamental key component of Statistics which is sufficient to build your first predictive model.
Break-OutSandy WinarkoBIA/ARCHEmbrace and Extend: First-Class Activity and 3rd Party Ecosystem for SSIS in ADFIntermediateAbstract: This session focuses on the deeper integration of SQL Server Integration Services (SSIS) in Azure Data Factory (ADF) and the broad extensibility of Azure-SSIS Integration Runtime (IR). We will first show you how to provision Azure-SSIS IR – dedicated ADF servers for lifting & shifting SSIS packages – and extend it with custom/3rd party components. Preserving your skillsets, you can then use the familiar SQL Server Data Tools (SSDT)/SQL Server Management Studio (SSMS) to design/deploy/configure/execute/monitor your SSIS packages in the cloud just like you do on premises. Next, we will guide you to trigger/schedule SSIS package executions as first-class activities in ADF pipelines and combine/chain them with other activities, allowing you to inject/splice built-in data transformations in your ETL/ELT workflows, automatically provision Azure-SSIS IR on demand/just in time, etc. And finally, you will learn about the licensing model for ISVs to develop paid components/extensions and join the growing 3rd party ecosystem for SSIS in ADF. Key Learning: Learning how to lift & shift their traditional ETL workloads and create modern ETL/ELT workflows with SSIS in ADF Demos: - Provision Azure-SSIS IR dedicated for SSIS package executions using ADF app, join it to a VNet to enable data access on premises, and customize it by installing additional (un)licensed components/extensions - Using SSMS, deploy existing SSIS projects/packages into a catalog (SSISDB) that is hosted by Azure SQL Database/Managed Instance and attached to Azure-SSIS IR - Trigger/schedule SSIS package executions on Azure-SSIS IR as first-class activities in ADF pipelines using SSMS/ADF app - Chain/combine SSIS activities with other activities in ADF pipelines to create modern ETL/ELT workflows
Chalk-TalkSatya RameshDBA/DEVSQL Server Indexing BasicsN/AChalk-Talks are 30 minutes’ sessions focussing on conceptual & architectural understanding, that too with only whiteboard and marker. No Laptops, no PPTs, no demos – only whiteboard-ing!
Chalk-TalkSourabh AgarwalDEV/DBAIntelligent Database - Database Intelligence built InN/AChalk-Talks are 30 minutes’ sessions focussing on conceptual & architectural understanding, that too with only whiteboard and marker. No Laptops, no PPTs, no demos – only whiteboard-ing!
Break-OutSourabh AgarwalDBA/DEVTroubleshooting Always On Availability Group FailoversAdvancedAbstract: Unexpected failovers or Unsuccessful failovers are by far the most common problem surrounding Always On Availability Groups. Troubleshooting failover related issues can be a challenge and time consuming. In order to effectively troubleshooting these issues data needs to be collected and analyzed from different servers and replicas. Things get further complicated by the fact that there are multiple logs which needs to be looked at and that these logs are in different time zone formats. In this session we will look at how to easily identify the reasons for failover of an Availability Group and what new capabilities are being introduced to help with the analysis of AG failovers. Key Learning: Tools and Techniques to troubleshoot Always On Availability Groups. Demos: Tips to troubleshoot Always On Availability Groups.
Break-OutSourabh Agarwal / Tejas ShahDBA/ARCHSQL Server vNext - Whats NewIntermediateAbstract: In this session we will talk about the new capabilities which will be introduced in SQL Server vNext in the High Availability and Disaster Recovery space. Get a sneak peak into how HA/DR works when SQL Server in running in containers, in VMs or on physical nodes on either Windows or Linux. Key Learning: New HA and DR functionalities coming to SQL Server VNext. Design for a Highly Availabille SQL Server running in either containers, VM's or physical servers Demos: In this session we will demo 1) Setting up HA for SQL Server running on Containers 2) Setting up HA for SQL Server Running on Linux VM's
Chalk-TalkSravani SaluruDBA/DEVTemp db contention - Important things to optimizeN/AChalk-Talks are 30 minutes’ sessions focussing on conceptual & architectural understanding, that too with only whiteboard and marker. No Laptops, no PPTs, no demos – only whiteboard-ing!
Break-OutSravani Saluru / Swetha ReddyDBA/ARCHAlways On Availability Groups on SQL Server on LinuxIntermediateAbstract: This session is targeted for high availability techniques on Linux for SQL Server. we will demonstrate how to enable and use availability groups for Linux. Manage the failover using pacemaker cluster. Troubleshooting scenarios like automatic failover issues , databases not synchronizing and performance issues. This session walks you through recommendations for configuration settings at pace maker cluster level , availability group resource , SQL Server settings to get the optimum performance . We will also talk about automatic and manual failover of Availability groups between replicas with and without external cluster . The demos will be based on Red hat Linux and SQL Server 2017. Key Learning: Configure and troubleshooting Always On Availability Groups on SQL Server on Linux Demos: Configure availability groups for SQL server on Linux
Break-OutSteph LockeDS/DBAProbability & Statistics 1010BasicAbstract: We can be better at our jobs if we have a good grasp of basic statistics. It doesn’t matter if you’re a DBA looking to understand query plan performance, a data warehouse person needing to come up with ETL load time estimates, or an analyst needing to report figures to managers. Statistics can help you all. If only maths classes hadn’t been so darn boring! Instead of going all mathsy, we’ll be doing some real-time data capture and taking an intuitive and visual approach through summary statistics right up to understanding how to produce simple predictive models. By the end of the session, you’ll understand concepts like sampling, error, regression, and outliers – important day-to-day stuff and a great base upon which to build. By the end of the session, you’ll wonder how people could have it made seem so hard for so many years. Key Learning: - Understand and correctly utilise descriptive statistics - Understand simple regression concepts - Understand hypothesis tests Demos: - Interactive application with an associated survey that people will fill in realtime
Break-OutSteph LockeDEV/ARCHAnchor Modelling: Agile databasesIntermediateAbstract: Anchor Modelling is a fantastic database modelling paradigm that uses sixth normal form (6NF) to store data and provides third normal form (3NF) views for ease of use. This session deep dives into all the concepts behind Anchor Modelling (and indeed databases generally!) and then takes you through how Anchor Modelling uses these concepts to move away from the traditional data warehouse paradigm to deliver a purely additive, agile database. Key Learning: - Understand normalisation - A high-level understanding of anchor models - Able to make architectural decisions about the appropriateness of Anchor models Demos: - Normalising data - Building an anchor model - Adding to an anchor model - Generating deployment code
Break-OutSudhir RawatNoSQL/ARCHAzure CosmosDB UnderneathIntermediateAbstract: Azure Cosmos DB is Microsoft’s proprietary globally-distributed, multi-model database service "for managing data at planet-scale". It's a fastest growing database in NoSQL world. This session will focus on some of the key areas of features provided by this database like partition, Consistency, globally distribution, throughput etc. This session will help you understand how to design database in CosmosDB, setting right knobs for designing application friendly database and how to reduce the cost but maintain good database standard. . Key Learning: > Design optimized DB > Understanding advance concept > Tips and tricks Demos: The demos will be around the proposed topics.
Break-OutSudhir RawatBD/ARCHUse Cases & Best Practices of Big Data on Microsoft AzureBasicAbstract: The world is changing rapidly and the big data ecosystem is enriching their capabilities even more, now there are a set of scalable languages that you can use in the Microsoft Azure environment to tackle and solve different problems, in this training you will learn the best practices, common scenarios, and use cases, in the end of the day you will be able to understand the best fit for each one of those scalable languages and how this new way of work with data can enrich your way of thinking. Key Learning: 1. This course will explain Big Data Features from the very beginning with a short overview and intro about those. 2. At this course, we will touch base each one of the listed areas in a basic to an intermediate level. 3. The intention of this training is to show to DBA’s / Developers about new ways to threat and work with data. Demos: HDInsight :- Allowing users to gain insight from TB’s of data. Guess how much time and join me to find out the answer.
Break-OutSunil AgarwalDBA/ARCHStrategies to certify your SQL Server application in the Cloud eraIntermediateAbstract: Application certification requires extensive testing for functional correctness, performance and scale that can take few weeks to months. Many IT organizations are struggling in managing legacy applications certified on older versions of SQL Server. Now, with the frequent releases of SQL Server and the advent of Azure SQL Database, many application vendors (ISV) and even custom developed application owners are challenged to minimize the effort required for application certification but still enabling customers to run the application on the latest SQL Server version or in SQL Azure Database. Come to this session to learn a new way to certify your application on SQL Server in a version agnostic way. Key Learning: Database compatibility level has been supported in SQL Server for a long time as a way to simplify database upgrade to new version of SQL Server. We will show how you can leverage DBCompat and the DMA tool to certify your application in SQL Server version agnostic way. Demos: Demo show casing (a) DMA tool on assessing the source database that can be upgraded to SQL Server 2017 (b) a source database that can't be upgraded to SQL 2017 due to functional incompatibilities
Open-TalkSunil AgarwalDEV/IOTClustered Columnstore Index: How did I scale dataload for IOT scenarioN/AOpen-Talks are 30 minutes free-flowing discussion on a specific topic. No laptops, no PPTs, no demos – only discussion and Q & A
Break-OutSunil AgarwalDBA/DEVMaximizing the Query Performance with Columnstore Indexes in SQL ServerAdvancedAbstract: Columnstore index can speed up the performance of analytics queries significantly but are you getting the best performance possible? Come to this session on to learn how to diagnose performance issues in queries accessing columnstore index and the steps you can take to troubleshoot. Some of the techniques we discuss here are rowgroup elimination, statistics, partitioning, improving the query plan quality, tweaking the schema, and creating one or more nonclustered btree indexes. Familiarly with columnstore index is required. Key Learning: Most people know that columnstore index can deliver significant query performance boost but many don't realize that they are not getting the optimal performance possible. This session will allow attendees to learn how to diagnose the query performance and take steps to fix it Demos: Demo heavy session. I present 5 common issues that lead to poorly performing queries on columnstore index and then show how you can diagnose and fix it. One demo per query performance scenario
Open-TalkSwetha ReddyDEV/DBAWhy don't I see SSL certificate listed under Certificates Tab in SQL Server Configuration Manager?N/AOpen-Talks are 30 minutes free-flowing discussion on a specific topic. No laptops, no PPTs, no demos – only discussion and Q & A
Open-TalkTejas ShahDBA/DEVSpinlocks, latches, locks – differentiation between them and how to plan troubleshooting themN/AOpen-Talks are 30 minutes free-flowing discussion on a specific topic. No laptops, no PPTs, no demos – only discussion and Q & A
Break-OutTejas ShahDBA/OSSThe Do's and Don'ts of Running SQL Server On LinuxAdvancedAbstract: In this session we will cover the learnings from actual customer deployments about running SQL Server on Linux and ensuring that you get optimal performance from your SQL Server Deployments. We will share tips and tricks and demos on how certain settings on both SQL/Linux OS can impact performance. Key Learning: Deploying SQL Server on Linux Best practices for running SQL Server on Linux. Performance impact of OS/SQL properties when running SQL Server on Linux. Demos: Demos would include performance impact of because of incorrect or wrong settings on OS or SQL Server.
Break-OutWilliam DurkinDBA/DEVQuery Store without SQL 2016 = Open Query StoreIntermediateAbstract: When SQL Server 2016 was released, it offered a fantastic new feature with the Query Store. Long term, statistics based, query tuning became a reality. But what about the thousands of servers that aren't upgrading to SQL 2016 or newer? The open source project Open Query Store is designed to fulfill that need. This session will give a short introduction to the Query Store feature in SQL 2016 and then dive into the Open Query Store (OQS) solution. William (co-creator of the OQS project) will explain the design of OQS and demonstrate the features. You will leave this session with an understanding of the features of Query Store and Open Query Store, and a desire to implement OQS in your systems when you return to the office. Key Learning: The audience will understand that the Query Store feature is and see how Open Query Store can provide a similar experience for SQL Server instances on versions where "real" Query Store are not available (versions below SQL Server 2016). This is a key administration tool for many thousands of SQL Server instances across the world. Demos: A full demo of how "real" Query Store works: collecting a workload and understanding how Query Store can be used to analyse this A second full demo of how Open Query Store works: collecting the same workload and understanding how Open Query Store can be used to analyse the data An explanation of how Open Query Store offers *more* data control over data collection than "real" Query Store.
Chalk-TalkWilliam DurkinDBA/DEVDBAtools – making DBA life simpleN/AChalk-Talks are 30 minutes’ sessions focussing on conceptual & architectural understanding, that too with only whiteboard and marker. No Laptops, no PPTs, no demos – only whiteboard-ing!
Break-OutWilliam DurkinDBA/ARCHSQL Server Replication: What, How, WhyIntermediateAbstract: Replication is one of the oldest data distribution technologies inside SQL Server (available since version 6.0). The age of replication shows in the management and troubleshooting tools (or lack thereof). In this session, we will dig into some real-world implementations and see how to manage deployments, performance problems and troubleshooting scenarios. We will look at: - Considerations for topology choices in a replication deployment - How to keep replication running smoothly - How to identify internal performance issues in replicating data - How to approach schema modifications in a replicated database - Approaches for troubleshooting errors - Uses for replication in modern SQL Server environments You will leave this session with a deeper understanding of the internals of replication. You will also be confident in identifying and triaging issues connected with replication systems. This session will cover features that are available in all versions of SQL Server from 2005 up to the latest and greatest release. Key Learning: Replication is powerful, but not well understood. This session will explain how it works and when it can/should be used. Key topics are - Considerations for topology choices in a replication deployment - How to keep replication running smoothly - How to identify internal performance issues in replicating data - How to approach schema modifications in a replicated database - Approaches for troubleshooting errors - Uses for replication in modern SQL Server environments Demos: 1. How to build a replication topology 2. How to measure performance 3. How to modify a topology 4. How to troubleshoot common problems