Azure data platform overview

Editor's Notes

• #2: Data Platform Overview Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
• #4: http://www.ispot.tv/ad/7f64/directv-hang-gliding Mention WWF, then WWE What is Randy Savage’s nickname (Macho Man)? Who won the final match of the first WrestleMania Hulk Hogan and Mr. T def. Roddy Piper and Paul Orndorff. Who won in the final match for WrestleMania III: Hulk Hogan (c) def André the Giant for the WWF World Heavyweight Championship. What was the name of the show starring "Rowdy" Roddy Piper: Piper’s Pit. What was the nickname for announcer Gene Okerlund: Mean Gene. Who did Hulk Hogan defeat in 1984 to become World Champion? The Iron Sheik Who did Andre the Giant eliminate to win the battle royal at WrestleMania 2? Bret "The Hitman" Hart What was the nickname of Jimmy Snuka? "Superfly"
• #5: One of the first things to understand in any discussion of Azure versus on-premises SQL Server databases is that you can use it all. Microsoft’s Data Platform leverages SQL Server technology and makes it available across physical on-premises machines, private cloud environments, third party hosted private cloud environments, and public cloud. This enables you to meet unique and diverse business needs through a combination of on-premises and cloud-hosted deployments, while using the same set of server products, development tools, and expertise across these environments. As seen in the diagram, each offering can be characterized by the level of administration you have over the infrastructure (on the X axis), and by the degree of cost efficiency achieved by database level consolidation and automation (on the Y axis). When designing an application, four basic options are available for hosting the SQL Server part of the application: SQL Server on nonvirtualized physical machines SQL Server in on-premises virtualized machines (private cloud) SQL Server in Azure Virtual Machine (public cloud) Azure SQL Database (public cloud)
• #6: Before the advent of the cloud. FInd someone to provision physical hardware, servers on the hardware, and applications on those physical servers. So many! Lots of steps, parts, counter productive.    All good things begin with a story. So before I talk to you about serverless, I wanted to discuss some history of how apps have been built through the years and how that has led us to the computing options we have today. Buy your own hardware and software Run it in your own data-centers Deal with a multitude of questions # of servers Physical security Hardware failure Software patching Backup Essentially a lot of questions, not all of which might be directly related to running your business. For example, if you are running an insurance company, you want to spend your time growing and innovating in the insurance domain, not worrying about how to handle servers.
• #7: Along came the cloud, way faster than provisioning hardware. So much innovation and rapid appilcation building. BUt Still have questions about servers and scaling. Along came the cloud, and provided some relief from some of those questions. IaaS in the cloud, allowed you to forget about managing all that physical hardware. Instead of hosting it yourself, you could now utilize servers hosted by someone else. While the worries around managing physical infrastructure are gone, there are a number of questions that still need to thought about: How many virtual machines do I need? How many would I need in future when my usage grows? How to handle updating the servers for the latest security patches How to think about deploying my code and the various dependencies, such as application runtimes, to the server Who monitors my apps and so on This means that you still haven’t reached that ideal state where you could focus purely on your business and its problems.
• #8: Trying to remove infrastructure. Don't care about patching OS or manging instance of your VM. All you care about is your web app – still have chores / non-business questions. Well, the cloud continue to help with that problem and with the advent of PaaS, there was even more simplification.  PaaS allows you to just worry about your app, and let the cloud vendor take care of not only the physical hardware, but also the ongoing management of the operating environment like Operating Systems, Virtual Machines or Containers, as well as the various application runtimes, whether it is Java Runtime, .NET, Node.js, and so on. You are not completely free from thinking about servers however, since you still have to worry about how to scale your app and accordingly specify the number and size of the various compute resources your apps would need at various times.
• #9: The server exists, it's not yours. NO questions about the server.  The latest in this series of evolution is Serverless – which further abstracts the underlying infrastructure from you, and allows you to focus only on a single question – how are you going to build your app to achieve the best results for your business, or whatever it is that you are trying to do with your apps. Whether it is the code to delight your customers through a great experience, or the logic to communicate securely with a business partner, that is where most organization want to spend their creative energies on. And serverless allows you to do just that, focus on your code and forget about the infrastructure. This is why we believe it is the platform for next generation of apps. Now before we jump into explaining the benefits of serverless, let’s call out what it’s not. It’s not the total lack of servers, that would a bit difficult. Instead, it’s a system where you don’t have to think about Servers at all, so for you, they are invisible.
• #10: 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.
• #13: With digital transformation in mind, let’s focus on how SQL Database provides the low-cost, low-friction option to migrating your SQL Server data at scale to SQL Database – without having to re-architect your apps. Introducing Azure SQL Database Managed Instance SQL Database Managed Instance is an expansion of the existing SQL Database service designed to enable database lift-and-shift to a fully-managed PaaS, without re-designing the application. SQL Database Managed Instance provides high compatibility with the on-premises SQL Server programming model and out-of-box support for the large majority of SQL Server features and accompanying tools and services. It’s important to note that Managed Instance isn’t a new service – it is a third deployment option within Azure SQL Database, sitting alongside single databases and elastic pools. As part of Azure SQL Database, Microsoft’s fully managed cloud database service, it inherits all its built-in PaaS features.
• #15: The Hyperscale service tier is a highly scalable performance tier that leverages the Azure architecture to scale out resources for SQL Database substantially beyond the limits available for the General Purpose and Business Critical service tiers. Hyperscale provides the following additional capabilities: Reliable and available Because it’s part of SQL Database, it has multiple levels of redundancy and no single points of failure. It also provides a 99.99% availability SLA. Scalable Support for up to 100 TB of database size Compute and storage scale independently Nearly instantaneous database backups (based on file snapshots stored in Azure Blob storage) regardless of size with no IO impact on Compute High performance Fast database restores (based on file snapshots) in minutes rather than hours or days (not a size of data operation) Higher overall performance due to higher log throughput and faster transaction commit times regardless of data volumes Rapid Scale up - you can, in constant time, scale up your compute resources to accommodate heavy workloads when needed, and then scale the resources back down when not needed. Rapid scale out - you can provision one or more read-only nodes for offloading your read workload and for use as hot-standbys
• #17: Azure Database for MySQL provides fully managed enterprise ready community MySQL database as a service for service for app development and deployment. Being community MySQL allows you to easily lift and shift to the cloud and use languages and frameworks for your choice. On top of that you get built-in high availability and capability to scale in seconds, helping you easily adjust to changes in customer demands. Additionally, you benefit from the unparalleled security and compliance, including Azure IP advantage, as well as Azure’s industry leading reach with more datacenters than any other cloud provider. All this with a flexible pricing model so you can choose resources for your workload with no hidden cost. Languages and Frameworks of your choice Being based on the community-editions of MySQL, PostgreSQL and MariaDB mean you can use the existing development languages frameworks and tools you already use for your apps. In addition, Azure Database Services are deeply integrated with Azure Web Apps to provide a streamlined provisioning and management experience for common frameworks (like WordPress, Drupal, Joomla) and languages (PHP, Node.js, Ruby) to provide a best-in-class PaaS experience. Scale in Seconds with built-in HA Azure Database Services are built upon the same service fabric framework that has been powering SQL Database for years. Unlike an VM-based PaaS offering like AWS RDS, Azure Database Services do not have the overhead a full VM stack has (e.g.; Linux OS + DB). Running on in a secured container implementation (SQLPAL, a very light-weight SQL OS), Azure Database Services can provision a new server in seconds in the event that a primary server hangs or crashes whereas in a traditional VM-based implementation the entire Linux (or Windows) OS stack has to bootstrap before the DB service loads. This means the entire experience of a failover can happen in as little as 30-45 seconds – and most importantly WITHOUT the need for a replica. AWS RDS requires deployment in Multi-AZ in order to achieve 99.95% SLA, which doubles your costs as you have 2 DB servers running at all times. With Azure Database Services, no replicas are needed which means no additional cost, or maintenance, by the customer. Additionally, this HA infrastructure enables the ability for Azure Database Services to scale performance on the fly. When a customer needs to scale-up for workload spikes, by simply changing a slider in the portal, a new server is provisioned at a higher performance level and the previous server’s DNS name and storage is connected to the new instance. Scaling can take a little time as 20 seconds meaning customers can scale performance, up or down, with little/no downtime to the application. Secure and Compliant “Secure by default” is the standard for any Azure service, meaning elements such as SSL encryption between the database and application are turned on by default. Additionally, all data at-rest is encrypted by default in Azure storage using AES 256 bit encryption. And since Azure Database Services are using OSS database engines, the Azure IP Advantage means that customers do not have to worry about litigation using an OSS product in Azure. Microsoft provides indemnification for any OSS first party workload in Azure. Industry-leading global reach With more regions across the globe than any other public cloud provider, Azure offers the ability to have the most globally distributed MySQL, PostgreSQL or MariaDB-based application in the world.
• #18: SMP is one server where each CPU in the server shares the same memory, disk, and network controllers (scale-up). MPP means data is distributed among many independent servers running in parallel and is a shared-nothing architecture, where each server operates self-sufficiently and controls its own memory and disk (scale-out).
• #19: SMP is one server where each CPU in the server shares the same memory, disk, and network controllers (scale-up). MPP means data is distributed among many independent servers running in parallel and is a shared-nothing architecture, where each server operates self-sufficiently and controls its own memory and disk (scale-out).
• #20: 19
• #23: LAMP stack (Linux, Apache, MySQL, PHP/ Python/ Perl)
• #24: Azure Cosmos DB offers the first globally distributed, multi-model database service for building planet scale apps. It’s been powering Microsoft’s internet-scale services for years, and now it’s ready to launch yours. Only Azure Cosmos DB makes global distribution turn-key. You can add Azure locations to your database anywhere across the world, at any time, with a single click. Cosmos DB will seamlessly replicate your data and make it highly available.   Cosmos DB allows you to scale throughput and storage elastically, and globally! You only pay for the throughput and storage you need – anywhere in the world, at any time.
• #25: 24
• #27: https://azure.microsoft.com/en-us/support/legal/sla/storage/v1_3/
• #29: Transfer options: https://www.microsoft.com/en-us/garage/profiles/fast-data-transfer/ Azure data box
• #31: Also called bit bucket, staging area, landing zone or enterprise data hub (Cloudera) http://www.jamesserra.com/archive/2014/05/hadoop-and-data-warehouses/ http://www.jamesserra.com/archive/2014/12/the-modern-data-warehouse/ http://adtmag.com/articles/2014/07/28/gartner-warns-on-data-lakes.aspx http://intellyx.com/2015/01/30/make-sure-your-data-lake-is-both-just-in-case-and-just-in-time/ http://www.blue-granite.com/blog/bid/402596/Top-Five-Differences-between-Data-Lakes-and-Data-Warehouses http://www.martinsights.com/?p=1088 http://data-informed.com/hadoop-vs-data-warehouse-comparing-apples-oranges/ http://www.martinsights.com/?p=1082 http://www.martinsights.com/?p=1094 http://www.martinsights.com/?p=1102
• #32: https://www.sqlchick.com/entries/2017/12/30/zones-in-a-data-lake https://www.sqlchick.com/entries/2016/7/31/data-lake-use-cases-and-planning Question: Do you see many companies building data lakes? Raw: Raw events are stored for historical reference. Also called staging layer or landing area Cleansed: Raw events are transformed (cleaned and mastered) into directly consumable data sets. Aim is to uniform the way files are stored in terms of encoding, format, data types and content (i.e. strings). Also called conformed layer Application: Business logic is applied to the cleansed data to produce data ready to be consumed by applications (i.e. DW application, advanced analysis process, etc). This is also called by a lot of other names: workspace, trusted, gold, secure, production ready, governed, presentation Sandbox: Optional layer to be used to “play” in.  Also called exploration layer or data science workspace
• #35: Azure Databricks features – Enhance your teams’ productivity Get started quickly by launching your new Spark environment with one click. Share your insights in powerful ways through rich integration with PowerBI. Improve collaboration amongst your analytics team through a unified workspace. Innovate faster with native integration with rest of Azure platform. Build on the most compliant and trusted cloud Simplify security and identity control with built-in integration with Active Directory. Regulate access with fine-grained user permissions to Azure Databricks’ notebooks, clusters, jobs and data. Build with confidence on the trusted cloud backed by unmatched support, compliance and SLAs. Scale without limits Operate at massive scale without limits globally. Accelerate data processing with the fastest Spark engine.
• #37: https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-component-versioning https://azure.microsoft.com/en-us/blog/enterprises-get-deeper-insights-with-hadoop-and-spark-updates-on-azure-hdinsight/ https://azure.microsoft.com/en-us/blog/top-8-reasons-to-choose-azure-hdinsight/
• #38: Key points: Summarize key benefits for Azure Analysis Services Azure Analysis Services helps you transform complex data from different data sources into a BI semantic model, so users in your organization can easily gain insights by connecting to the data models using tools like Excel, Power BI, and others to create reports and perform ad hoc data analysis Talk Track Transform Complex Data into rich BI semantic models: Azure Analysis Services Analysis Services helps you transform complex data into a single business user friendly data model making it easy for business users to understand and analyze data across different data sources. Gain instant insights with in-memory cache using your preferred visualization tools : Not only can business users get insights from data easily using their preferred data visualization tool, whether it is Power BI, Excel or other major data visualization tools, but with the in-memory cache capabilities of Azure Analysis Services, users can gain insights over billions of rows of data at the speed of thought Proven Technology: 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. Analytics engine as-a-service (provision fast, scale faster): The same proven enterprise grade BI platform is now available as a fully managed service in Azure. With the power of the trusted Microsoft Cloud, you do not need to manage infrastructure on-premises and can benefit from the scalability of the cloud. Additionally you can use Azure Resource Manager to create and deploy an Azure Analysis Services instance within seconds, and use backup restore to quickly move your existing models to Azure Analysis Services and take advantage of the scale, flexibility and management benefits of the cloud. Scale up, scale down, or pause the service and pay only for what you use. Azure Analysis Services is built for Hybrid BI - 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 - you do not have to move on-premises data to the cloud. 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.
• #40: Increase analytics and apps performance with scale out data marts
• #46: 3/17/2019
• #53: Microsoft Azure supports other services like Azure HDInsight, Azure Data Lake, Azure IoT Hub, Azure Events Hub in various layers of the architecture above to allow customers a truly customized solution.
• #58: 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