Microsoft cloud big data strategy

Editor's Notes

• #2: Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.
• #3: Fluff, but point is I bring real work experience to the session
• #6: Key goal of slide: To convey what every IT person knows: The data warehouse and what’s it for. Then we set-up the Gartner quote to say that there is a tipping point. End the slide with a question: Why is it at a tipping point?   Slide talk track: What is the “traditional” data warehouse? IT professionals know this well. A data warehouse or an enterprise data warehouse is a database that was designed specifically for data analysis. It is the single source of truth or the central repository for all data in the company. This means disparate data in the company coming from your transactional systems, your ERP, CRM or Line of Business applications would all be extracted, transformed, and cleansed and put into the warehouse. It was built so that the people who is accessing the warehouse using BI tools will be accessing data that has been provisioned by IT and represent accurate data sanctioned by the company. However, this traditional data warehouse is reaching an inflection point. Gartner in their analysis of the state of data warehousing noted that it is reaching the most significant tipping point since it’s inception. The question is why? What is going on?
• #10: Data is now the key strategic business asset. Every device, every customer, every activity – everything that’s happening in the world around us - is producing incredibly rich data that can help us create new experiences, new efficiencies, new business models and even new inventions. Leveraging this data can be the differentiator for your business. For example, IDC estimates companies that are leaders in using data assets to their advantage will capture $1.6 trillion more in business value than those that lag behind.   While data is pervasive, actionable intelligence from data is elusive. Our customers want to transform data to intelligent action and reinvent their business processes. To do this they need to more easily analyze massive amounts of data – so they can move from seeing “what happened” and understanding “why it happened” to predicting “what will happen” and ultimately, knowing “what should I do”. Only then can they create the intelligent enterprise.
• #12: Result: Used across Microsoft in Office, Xbox Live, Azure, Windows, Bing and Skype Supports ten thousand developers running experimentations Manages exabytes of data https://www.microsoft.com/en-us/Investor/earnings/FY-2016-Q4/press-release-webcast
• #13: Everything: technology, cost, culture
• #17: Our portfolio of products provides customers with the power to deploy the solution that suits their business needs. Your choice of platform, whether on-premises, hybrid or private or public cloud, doesn’t limit you now or in the future. Migrating or expanding becomes an easy process and doesn’t require excessive downtime or introduce potential threats to your business success. With Microsoft, you can seamlessly scale up to larger processing and storage capabilities, or scale out by adding additional servers in parallel arrangement. T: SQL Server is a trusted market leader, and it’s the cornerstone of our data warehouse offering.
• #18: Reliable Open Source analytics with an Industry leading SLA HDInsight allows you to easily spin up enterprise-grade open source cluster types guaranteed with the industry’s best 99.9% SLA and 24/7 support. We guarantee this SLA for the entire big data solution, not just the VM instances. HDInsight is architected for full redundancy and high availability including head node replication, data geo-replication, and built-in standby NameNode making HDInsight resilient to critical failures not addressed in standard Hadoop implementations. Azure also offers cluster monitoring and 24x7 enterprise support backed by Microsoft and Hortonworks with 37 combined committers for Hadoop core, more than all other managed cloud providers combined to support your deployment and the ability to fix and commit code back to Hadoop. Enterprise Grade Security & Monitoring HDInsight protects your data assets and easily extends your on-premise security and governance controls to the cloud. We feature single sign-on (SSO), multi-factor authentication and seamless management of millions of identities through Azure Active Directory. You can authorize users and groups with fine-grained access control policies over all your enterprise data with Apache Ranger. HDInsight meets HIPAA, PCI, SOC compliance, ensuring your enterprise data assets are always protected with the highest security and regulatory compliance. To ensure the highest level of business continuity, HDInsight extends capabilities for alerting, monitoring, defining pre-emptive actions, and enhanced workload protection through native integration with Azure Operations Management Suite (OMS). Most Productive platform for developers and scientists HDInsight offers developers tailored experiences through rich productivity suites for Hadoop & Spark with integrated development environments using Visual Studio, Eclipse, and IntelliJ supporting Scala, Python, R, Java, and .Net. HDInsight gives data scientists the ability to create narratives that combine code, statistical equations, and visualizations that tell a story about the data through integration to the two most popular notebooks: Jupyter and Zeppelin. HDInsight is also the only managed cloud Hadoop solution with integration to Microsoft R Server. Multi-threaded math libraries and transparent parallelization in R Server means handling up to 1000x more data and up to 50x faster speeds than open source R—helping you train more accurate models for better predictions than previously possible. Cost effective cloud scale HDInsight has decoupled compute and storage, enabling you to cost-effectively scale workloads up or down, independent of storage. Local storage can still be used for caching and fast I/O. Spark and interactive Hive users can choose SSD memory for interactive performance; while Kafka users can retain all streaming data in premium managed disks. You only pay for the compute and storage you use and are given the ability to choose any Azure VM types that enables the best utilization of resources. A recent study showed HDInsight delivering 63% lower TCO than deploying Hadoop on premises over 5 years.* Integration with leading Productivity Applications In the broader ecosystem for Hadoop, there is a thriving market of independent software vendors (ISVs) who provide value added solutions. Through a unique design where every cluster is extended with edge nodes and script action, HDInsight lets customers spin up Hadoop and Spark clusters pre-integrated and pre-tuned with any ISV application out-of-the-box. Datameer, Cask, AtScale, StreamSets are few such applications, which are very popular on the HDInsight platform today. Easy for administrators to manage With HDInsight, administrators can deploy Hadoop in the cloud without buying new hardware or incurring other up-front costs. There’s also no time-consuming installation or set up. There is also no need to patch the operating system or upgrade the Hadoop versions. Azure does it for you. Launch your first cluster in minutes.
• #20: Petabyte size files and Trillions of objects:With Azure Data Lake Store your organization can analyze all of its data in a single place with no artificial constraints. Your Data Lake Store can store trillions of files where a single file can be greater than a petabyte in size which is 200x larger than other cloud stores. This makes Data Lake Store ideal for storing any type of data including massive datasets like high-resolution video, genomic and seismic datasets, medical data, and data from a wide variety of industries. Scalable throughput  for massively parallel analytics:Without redesigning your application or repartitioning your data at higher scale, Data Lake Store scales throughput to support any size of analytic workload. It provides massive throughput to run analytic jobs with 1,000+ concurrent executors that read and write hundreds of terabytes of data efficiently. HDFS for the Cloud:Microsoft Azure Data Lake Store supports any application that uses the open Apache Hadoop Distributed File System (HDFS) standard. By supporting HDFS, you can easily migrate your existing Hadoop and Spark data to the cloud without recreating your HDFS directory structure. Always encrypted, Role-based security & Auditing:Data Lake Store protects your data assets and extends your on-premises security and governance controls to the cloud easily.  Data is always encrypted; in motion using SSL, and at rest using service or user managed HSM-backed keys in Azure Key Vault.  Capabilities such as single sign-on (SSO), multi-factor authentication and seamless management of millions of identities is built-in through Azure Active Directory. You can authorize users and groups with fine-grained POSIX-based ACLs for all data in the Store enabling role-based access controls. Finally, you can meet security and regulatory compliance needs by auditing every access or configuration change to the system. Enterprise-grade Support:We guarantee a 99.9% enterprise-grade SLA and 24/7 support for your big data solution.
• #21: Start in seconds, Scale instantly, Pay per job:Our on-demand service will have you processing Big Data jobs within 30 seconds. There is no infrastructure to worry about because there are no servers, VMs, or clusters to wait for, manage or tune. You can instantly scale the analytic units (processing power) from one to thousands for each job. You only pay for the processing used per job. Develop massively parallel programs with simplicity:U-SQL is a simple, expressive, and extensible language that allows you to write code once and automatically have it be parallelized for the scale you need. You can process petabytes of data for diverse workload categories such as ETL, machine learning, cognitive science, machine translation, imaging processing, and sentiment analysis by using U-SQL and leveraging existing libraries written in .NET languages, R, or Python.. Debug and Optimize your Big Data programs with ease:Debugging failures in cloud distributed programs are now as easy as debugging a program in your personal environment. Our execution environment actively analyzes your programs as they run and offers recommendations to improve performance and reduce cost. For example, if you requested 1000 AUs for your program and only 50 AUs were needed, the system would recommend that you only use 50 AUs resulting in a 20x cost savings. Virtualize your analytics:The power to act on all your data with optimized data virtualization of your relational sources such as Azure SQL Server on VMs, Azure SQL Database, and Azure SQL Data Warehouse. Queries are automatically optimized by moving processing close to the source data, without data movement, thereby maximizing performance and minimizing latency. Enterprise-grade Security, Auditing and Support:Extend your on-premises security and governance controls to the cloud for meeting your security and regulatory compliance needs. Capabilities such as single sign-on (SSO), multi-factor authentication and seamless management of millions of identities is built-in through Azure Active Directory. Role Based Access control, and the ability to audit all processing and management operations are on by default. We guarantee a 99.9% enterprise-grade SLA and 24/7 support for your big data solution.
• #23: 22
• #24: We are planning to release a preview of this functionality early next year as part of SQL Server V.Next CTPs, exact release dates are still in flux. By preview early next year PolyBase will support Teradata, Oracle, SQL Server, MongoDB, Hadoop and Azure blob storage (not MySQL!). We will continue to add more sources until GA. http://demo.sqlmag.com/scaling-success-sql-server-2016/integrating-big-data-and-sql-server-2016 When it comes to key BI investments we are making it much easier to manage relational and non-relational data with Polybase technology that allows you to query Hadoop data and SQL Server relational data through single T-SQL query. One of the challenges we see with Hadoop is there are not enough people out there with Hadoop and Map Reduce skillset and this technology simplifies the skillset needed to manage Hadoop data. This can also work across your on-premises environment or SQL Server running in Azure.
• #25: Comparison of IoT Hub and Event Hubs: https://azure.microsoft.com/en-us/documentation/articles/iot-hub-compare-event-hubs/
• #26: Azure Stream Analytics is a cost effective event processing engine that helps uncover real-time insights from devices, sensors, infrastructure, applications, and data. It will enable various opportunities including Internet of Things (IoT) scenarios such as real-time fleet management or gaining insights from devices like mobile phones and connected cars. Deployed in the Azure cloud, Stream Analytics has elastic scale where resources are efficiently allocated and paid for as requested. Developers are given a rapid development experience where they describe their desired transformations in SQL and the system abstracts the complexities of the parallelization, distributed computing, and error handling from them. Looking forward into H2 FY15, Stream Analytics will become generally available after previewing at TechEd EMEA 2014.
• #27: Microsoft’s Big Data vision in the cloud is to enable organizations to solve large, complex problems end-to-end, from storing and managing TBs of data without investing in hardware and software, to seamless integration with the 1 billion users of Excel. As part of this vision, Microsoft offers Azure Machine Learning, designed to democratize the complex task of advanced analytics. Advanced analytics is using products like Azure Machine Learning to find new and actionable insights that traditional approaches to business intelligence are unlikely to discover. An easy way to think about this is thinking about a dashboard. Today when confined by only BI tools without a connection to machine learning, it is solely the job of the human looking at the spreadsheet to gain insights and react to the data. But a human can only consume so many variables. A computer, on the other hand, can consume a great deal more variables to provide much deeper insight on the data. Humans can then react to the data to make decisions that drive competitive advantage, as well as program the computer further to recognize important patterns in the future. This is why we say beyond business intelligence – machine intelligence. The accessibility of our solution starts with set up. Previously you needed to provision your workspace on-premises for machine learning, also thinking about server space and a host of other considerations. Today you can get started with just a browser. With only an Azure subscription, you can take advantage of the full functionality of Azure Machine Learning within minutes. Taking a test drive is even easier, click Get Started off azure.com/ml and with simply a Microsoft ID you’re off to the races. Another limit with other machine learning solutions are siloed environments that only allow for one programming language or make changing from one algorithm to another time consuming and complex. With Azure ML, you can experience the power of choice. That choice expands to language, with both Python and R being first class citizens of Azure ML, or algorithm. You can choose from hundreds of algorithms, including business-tested ones running our Microsoft businesses today. And swapping out algorithms to land on the right one for you is done with a click. Additionally you can drop in custom R and Python code – your “special sauce” – and mix and match that with the other options in the tool.  Most revolutionary of all you can deploy solutions in minutes as a web service, which is simply a url which can connect to any data, anywhere – including on-premises or in another cloud environment. The ability to put a model into production almost immediately, as well as revise it easily, is unique to Microsoft and allows companies to stay on top of the changing business landscape more effectively than is offered by any other provider today. We even take that a step further, allowing model developers to connect to the world with our Machine Learning Marketplace, where they can publish finished solutions and APIs with their own brand and business model. Developers can also discover machine learning solutions there without any machine learning skills needed – the data science is inside. Check it out at https://datamarket.azure.com/.
• #29: Azure Data Factory is a cloud service for creating, managing, and monitoring the production of trusted information from on-premises and cloud data sources using transformative analytics at scale. Data Factory can be used in solutions to gain insights from operational and service health telemetry data, analyze customer actions to determine an optimal targeted marketing strategy, or predict customer churn from customer profile and service log data. Instead of writing hard-to-manage custom code to wire together a data warehouse with Hadoop, NoSQL, and SaaS, use Data Factory to quickly create and deploy highly available data processing pipelines, significantly cutting your time to solution and your operational costs. Get a single monitoring view of all of your data processing pipelines along with data lineage and service health. Bring together on-premises data like SQL Server and cloud data like Azure SQL Database, Blobs, and Tables with the transformative analytics of HDInsight (Hive, Pig, MapReduce, custom .NET code), and even Azure Machine Learning, to produce trusted information that is easily consumed by BI tools or applications. Looking forward into H2 FY15, Data Factory will become generally available after previewing at TechEd EMEA 2014.
• #30: Power BI Desktop is a self-service BI tool designed to allow users to pull data together from multiple different data sources. Transform and clean that data. Model and add custom calculations. And then visually explore and create interactive reports that can be easily published and shared through the Power BI service. In addition – you can now create your own custom visualizations though our open source visualization framework. More information available at powerbi.com/visuals
• #32: Power BI dashboards With updates to Power BI customers can now see all their data through a single pane of glass. Live Power BI dashboards show visualizations and KPIs from data that reside both on-premises and in the cloud, providing a consolidated view across their business regardless of where their data lives. You can then explore their data further by drilling through the dashboard into the underlying reports, discovering new insights that they can pin back to the dashboard to monitor performance going forward.
• #33: Natural Language Interface - With Power BI we continue to find new ways to simplify how people analyze and gain insight from data, providing industry leading features such as natural language query. Natural language query provides users with an easier way to interact with their data, allowing them to type questions of their data and receive answers in the form of live visualizations. Power BI integration with Cortana allows you to now ask these question directly from Cortana and to have answers from your Power BI data surfaced to you by Cortana. These data driven answers can range from simple numeric values (“revenue for the last quarter”), charts (“revenue over time”), maps (“revenue by region”) or data represented through any of the other Power BI data visualizations. Combined with the Cortana Analytics suite, this opens up amazing new opportunities to use Cortana to enable your business, and your customers' businesses, to get things done in more helpful, proactive, and natural ways.   Quick Insights - providing a new ways to help users find hidden insights in their data. The new Quick Insights feature allows users to automatically scan and detect patterns and trends in the data that they publish to Power BI. Through a partnership with Microsoft Research, the Quick Insights feature uses a growing list of algorithms to automatically discover and visualize correlations, outliers, trends, seasonality, change points in trends, and other factors in your data in seconds.  
• #34: Animation set to loop (replace /Build walk in ?), Add session id to top Bot Framework provides everything you need to build and connect intelligent bots that interact naturally wherever your users are talking, from text/sms to Skype, Slack, Office 365 mail and other popular services. Bot Framework consists of three main components: Bot Connector, Bot Builder, and Bot Directory
• #35: At Microsoft, we’ve been offering APIs for a very long time across the company.  In delivering Microsoft Cognitive Services API, we started with 4 last year at /build (2015); added 7 more last December, and today (May 2016) we have 22 APIs in our collection.      Cognitive Services are available individually or as a part of the Cortana Intelligence Suite, formerly known as Cortana Analytics, which provides a comprehensive collection of services powered by cutting-edge research into machine learning, perception, analytics and social bots. These APIs are powered by Microsoft Azure. Developers and businesses can use this suite of services and tools to create apps that learn about our world and interact with people and customers in personalized, intelligent ways.  
• #36: Key points: Summarize key benefits for Azure Analysis Services Talk track: As already mentioned, Azure Analysis Services is based on the proven analytics engine in SQL Server 2016 Analysis Services, that has helped organizations turn complex data into a trusted, single source of truth for years. This means that BI professionals who are familiar with SQL Server Analysis Services, tabular models can get started quickly and do not need to learn new tools or skills. And with the power of the cloud, BI professionals do not need to manage infrastructure on-premises. They can easily deploy the BI solution and benefit from the scalability of the cloud. Organizations store data in the cloud and on-premises. Azure Analysis Services is built for hybrid data. Data can be access in the cloud, on-premises or a combination of both, enabling a hybrid solution. So - customers do not have to move on-premises data to the cloud. And last but not least. Azure Analysis Services enables interactive data visualization over billions of rows of data and as it supports BI industry standards such as XML/A and MDX, business users can access data using their preferred data visualization tool. Whether it is Power BI, Excel or other major data visualization tools. To summarize, Azure Analysis Services is simple to use – it is easy to get started, you can use your existing skills to create BI semantic models, and your favorite data visualizations tools to analyze your data.
• #37: Slide objective Show broad commitment to R by preserving freely available, enhanced editions, Windows and SQL Server editions and R Server editions for leading EDWs, Linux and Hadoop platforms. Differentiate free, open editions from commercial by mentioning availability of commercial 24x7 support, and enhancements to support very large scale data analytics at speed. Talking points Notes
• #38: Microsoft Azure DocumentDB is the highly-scalable NoSQL document database-as-a-service that enables query over schema-free data and multi-document transaction processing helps deliver configurable and reliable performance and enables rapid development DocumentDB is the right solution for applications that run in the cloud when predictable throughput, low latency, and flexible query are key. Fully managed PaaS database service backed by the power of Microsoft Azure. Unlike many other NoSQL offers, DocumentDB was built for the cloud to perform and scale in a multi-tenant environment. Cluster administration, replication, and other management functions are handled for the customer automatically. DocumentDB is backed by a 99.95% availability SLA (at GA) to provide consistent, reliable performance. Application controlled schema with massive scale-out enables iterative development and evolving data models. DocumentDB supports a schema-free data model where the application defines the data model. This supports modern application development scenarios where applications are developed iteratively with many versions supported concurrently and data models continuously evolve. Automatic indexing enables robust querying over schema-free data. DocumentDB is the first of its kind to offer SQL over schema-free JSON data and multi-document transactional processing. Integrated transactional JavaScript processing + tunable consistency enable high performance application experiences. DocumentDB supports stored procedures, triggers, and user-defined functions. It also supports tunable consistency with well-defined click stops to enable developers to tune database performance based on the application’s needs. The key scenarios for DocumentDB are the following: Emitting telemetry and logging data Storing/querying event and workflow data Persisting device and app configuration data User generated content Scalable, iterative app development
• #43: Petabyte size files and Trillions of objects:With Azure Data Lake Store your organization can analyze all of its data in a single place with no artificial constraints. Your Data Lake Store can store trillions of files where a single file can be greater than a petabyte in size which is 200x larger than other cloud stores. This makes Data Lake Store ideal for storing any type of data including massive datasets like high-resolution video, genomic and seismic datasets, medical data, and data from a wide variety of industries. Scalable throughput  for massively parallel analytics:Without redesigning your application or repartitioning your data at higher scale, Data Lake Store scales throughput to support any size of analytic workload. It provides massive throughput to run analytic jobs with 1,000+ concurrent executors that read and write hundreds of terabytes
• #45: Start in seconds, Scale instantly, Pay per job:Our on-demand service will have you processing Big Data jobs within 30 seconds. There is no infrastructure to worry about because there are no servers, VMs, or clusters to wait for, manage or tune. You can instantly scale the analytic units (processing power) from one to thousands for each job. You only pay for the processing used per job.
• #48: Debug and Optimize your Big Data programs with ease:Debugging failures in cloud distributed programs are now as easy as debugging a program in your personal environment. Our execution environment actively analyzes your programs as they run and offers recommendations to improve performance and reduce cost. For example, if you requested 1000 AUs for your program and only 50 AUs were needed, the system would recommend that you only use 50 AUs resulting in a 20x cost savings.
• #49: Develop massively parallel programs with simplicity:U-SQL is a simple, expressive, and extensible language that allows you to write code once and automatically have it be parallelized for the scale you need. You can process petabytes of data for diverse workload categories such as ETL, machine learning, cognitive science, machine translation, imaging processing, and sentiment analysis by using U-SQL and leveraging existing libraries written in .NET languages, R, or Python..
• #51: With Microsoft Azure HDInsight, Microsoft R Server is now available as an option when you create HDInsight clusters in Azure. This new capability provides data scientists, statisticians, and R programmers with on-demand access to scalable, distributed methods of analytics on HDInsight. Clusters can be sized to the projects and tasks at hand and torn down when they're no longer needed. Since they're part of Azure HDInsight, these clusters come with enterprise-level 24/7 support, an SLA of 99.9% uptime, and the flexibility to integrate with other components in the Azure ecosystem. R Server on HDInsight provides the latest capabilities for R-based analytics on datasets of virtually any size loaded to either Azure Blob or Data Lake storage. Since R Server is built on open source R, the R-based applications you build can leverage any of the 8000+ open source R packages, as well as the routines in ScaleR, Microsoft’s big data analytics package that's included with R Server. The edge node of a cluster provides a convenient place to connect to the cluster and to run your R scripts. With an edge node, you have the option of running ScaleR’s parallelized distributed functions across the cores of the edge node server. You also have the option to run them across the nodes of the cluster by using ScaleR’s Hadoop Map Reduce or Spark compute contexts. The models or predictions that result from analyses can be downloaded for use on-premises. They can also be operationalized elsewhere in Azure, such as through an Azure Machine Learning Studio web service.
• #54: https://azure.microsoft.com/en-us/regions/
• #55: Always encrypted, Role-based security & Auditing:Data Lake Store protects your data assets and extends your on-premises security and governance controls to the cloud easily.  Data is always encrypted; in motion using SSL, and at rest using service or user managed HSM-backed keys in Azure Key Vault.  Capabilities such as single sign-on (SSO), multi-factor authentication and seamless management of millions of identities is built-in through Azure Active Directory. You can authorize users and groups with fine-grained POSIX-based ACLs for all data in the Store enabling role-based access controls. Finally, you can meet security and regulatory compliance needs by auditing every access or configuration change to the system.
• #57: 1) Copy source data into the Azure Data Lake Store (twitter data example)2) Massage/filter the data using Hadoop (or skip using Hadoop and use stored procedures in SQL DW/DB to massage data after step #5)3) Pass data into Azure ML to build models using Hive query (or pass in directly from Azure Data Lake Store)4) Azure ML feeds prediction results into the data warehouse5) Non-relational data in Azure Data Lake Store copied to data warehouse in relational format (optionally use PolyBase with external tables to avoid copying data)6) Power BI pulls data from data warehouse to build dashboards and reports7) Azure Data Catalog captures metadata from Azure Data Lake Store and SQL DW/DB8) Power BI and Excel can pull data from the Azure Data Lake Store via HDInsight9) To support high concurrency if using SQL DW, or for easier end-user data layer, create an SSAS cube
• #58: Get it at //aka.ms/forresterwave