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Sql server on azure familiarize yourself
- 2. Waktu Session Topik Presenter 09.00-10.30 Session Opening SQL Server and Cloud Kiki Rizki Noviandi Microsoft Data Platform- MVP SQL Server Indonesisa User Group Leader Kwad5.com 09:30 - 10:20 Session 1: Migration to Modern SQL Server Platform from Oracle, MySQL Indra Dharmawan Astra Graphia Information Technology 10:20 - 11:10 Session 2: Design for Big Data with Microsoft Azure SQL Data Warehouse Cahyo Listyanto Head of Data Analytics Bizzy.co.id 11:10 - 12:00 Session 3: Power BI Analytics on Azure Albert Kartawijaya PT. Ebiz Cipta 12:00 -13:00 Break Lunch 13:00 - 13:50 Session 4: Utilizing Azure Machine Learning Agus Kurniawan Microsoft Data Platform- MVP Samsung Research Indonesia 13:50 - 14 : 40 Session 5: Cloud Database with Hybrid Scenario Anugerah Sentot Sudono Learning Development Manager PT Executrain Nusantarajaya
- 4. PT. Kwadran Lima Indonesia http://www.Kwad5.com Kiki.Rizki.Noviandi @KikiNoviandi krn@kwad5.com 12x recipient Founder & CEO https://www.facebook.com/groups/sqlserverindonesia/ Co-Founder & Group Leader https://mvp.microsoft.com/en-us/PublicProfile/33869?fullName=Kiki%20Rizki%20Noviandi
- 5. • Microsoft Data Platform Landscape • Cloud and Data Platform Continuum • SQL Server in Azure VM
- 7. Business intelligence Advanced Analytics & AI DATA INSIGHTSDATA MANAGEMENT Big data processing Data warehousing Operational data Power BI Azure Machine Learning Azure Stream Analytics Azure Cognitive Services SQL Server Reporting Services SQL Server Analysis Services, R Services Azure HDInsight Azure SQL Data Warehouse Azure Data Lake Azure Document DB Azure SQL Database SQL Server SQL Server Apache Hadoop ON-PREM CLOUD
- 8. The data platform continuum One consistent platform with common tools. CloudOn Premises 8
- 9. Hybrid Cloud On premises Shared Lower cost Dedicated Higher cost Higher administration Lower administration Off premises
- 10. Spin up powerful virtual machines in less than 5 minutes. Bring your own software or use many existing images. Create specific development environments for different projects. Use the full power of Visual Studio from other OSs, low-power devices, or tablets. Access your development environment from anywhere, anytime, any device. Easily create Hadoop clusters before deploying on Hortonworks on-premises. SQL Server on Virtual machines Hadoop on HDInsight
- 11. Global scale Provide high-performance to applications with seamless scalability, load balancing and performance features across the application and data tiers. Near-zero maintenance Drive fast time-to-market and unprecedented efficiencies with familiar management tools, developer platforms, and APIs and a near-zero maintenance service. PaaS Services Scale and Streamline Development
- 12. Data Services support multi-tenancy and SaaS application development through logical pricing and consumption units. Use Azure Active Directory with your multi-tenant app for simple, self-service, single sign on. Data Applications
- 13. Enterprise-ready platform that integrates modern mobile experiences with existing business assets and faster development cycles Mobile apps are part of a broader modernization…
- 15. Web Front End Transactions Product Catalog Search Experience Shopping Cart Sentiment Analysis User Profiles
- 16. fully featured RDBMS transactional processing rich query managed as a service elastic scale internet accessible http/rest schema-free data model arbitrary data formats
- 17. fully featured RDBMS transactional processing rich query managed as a service elastic scale internet accessible http/rest schema-free data model arbitrary data formats
- 18. 18 Key Benefits Reduce project overhead Speed time to market Secure, redundant source code “Telenor saved 70% on test, development and demo that could be turned off when finished to minimize their capital outlays,” Marius Pedersen, Telenor Group 70% savings Ready in hours, not weeks No resource limits SQL Server Dev Tools On-Premises Development Work Stations SQL Server On-Premises Deploy SQL Server in a Windows Azure Virtual Machine Test TFS in Windows Azure
- 19. fully featured RDBMS transactional processing rich query managed as a service elastic scale internet accessible http/rest schema-free data model arbitrary data formats
- 20. A relational database-as-a-service, fully managed by Microsoft. For cloud-designed apps when near-zero administration and enterprise-grade capabilities are key. Perfect for organizations looking to dramatically increase the DB:IT ratio. Best for… TCO benefits SQL Server in a VM Azure SQL Database Scalability Resources
- 21. Increased from 99.9% to 99.99% uptime SLA New service design point enables scale up of resources, delivering predictable throughput & performance SLA Performance Point-in-time-restore, geo-restore, and standard and active geo- replication protect against human & environmental-initiated events Azure certifications: ISO, HIPAA BAA, EU Model Clause Auditing on SQL Database Protection Compliance Hourly billing & broad set of price pointsFlexibility
- 22. DEMO
Hinweis der Redaktion
- SQL Server is our mission critical OLTP database with industry-leading TPC-E performance benchmarks. We’ve been recognized by Gartner as a leader in Operational Database Management four years in a row. Azure SQL Database is the cloud answer to managing your operational data - built on SQL Server, so your existing applications and skills transfer. And as a managed database service, so it can save you time and money in both set-up and administration. DocumentDB is our database-as-a-service NoSQL database service designed for modern mobile and web applications. SQL Server continues to be viable for data warehousing. Today we have companies doing petabyte scale data warehousing in a scale up SMP architecture just with SQL Server. Azure SQL Data Warehouse is the industry’s first enterprise-class cloud data warehouse that can grow, shrink, and pause in seconds. Azure Data Lake - Offers data management and analytics at scale to customers, as Azure Data Lake. It is composed of three parts: Azure Data Lake Store, Azure Data Lake Analytics, and HD Insight. Together with these products, you can store big data of any size and type, and analyze it with familiar open-source tools – all while getting a leading, enterprise-class SLA.
- In this session we will take a closer look at the third pillar or key investment area for SQL Server 2014, which is build a data platform for hybrid cloud. One of the key design points we have taken to approaching cloud computing is to drive towards a consistent data platform with common tools from on premises to cloud. When it comes to Microsoft’s cloud, we have two offerings that can be used to run relational databases in the cloud depending on your application needs. Lets take a closer look at both of these Windows Azure options.
- 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)
- Presenter guidance: Explain why Dev/Test is a great entry level scenario for the cloud particularly for SQL Server and Hadoop which have on-premises technologies. Slide talk track: Implementing and maintaining a dev and test infrastructure—the four building blocks—in physical, on-premises hardware is inherently costly and can suffer from delays due to long procurement cycles and increased set-up and management effort required. Additionally, according to industry reports, development and test infrastructure typically runs at a low utilization rate of approximately 20 percent, which leads to over-provisioning of costly resources when buying hardware exclusively for dev and test purposes. Ultimately, the lack of a scalable, agile dev and test infrastructure prevents businesses from validating and executing on promising ideas and limits innovation. Dev and test environments in the cloud are a combination of virtual machines (VMs), virtual networks (VPNs), cloud storage, SQL Server databases, and other cloud services offered by Microsoft Azure. You can provision any number and combination of VMs and services in minutes, network them together, and install and configure any software that your application needs in order to run. For an individual developer workstation, you can easily and quickly provision a single VM of appropriate size and compute power and install required software. VMs are just like physical computers and you can do anything with them. Configuring a VM is the exact same process as configuring a physical machine. Today, we’ll mention HDInsight in two capacities – first (in this example) as it relates to Map/Reduce and other data transformation jobs. Second, we’ll dive into a bit of detail into Hbase on HDInsight which is a database that runs as part of the service.
- Presenter guidance: Explain why Migrating existing apps, in particular LOB apps, makes so much sense because of PaaS – reduced OpEx and CapEx. Slide talk track: Forklift Migration – in IaaS Bring entire application and all dependencies Hybrid Migration – it depends Bring portion of application to the cloud while some resources stay on-premises IaaS to PaaS Migration Migrating application to PaaS - web or worker roles + data - that work better on a VM
- Presenter guidance: Explain why SaaS apps benefit from being built on Azure – multi-tenancy through Slide talk track: The pricing and licensing for data services on Azure has been designed with SaaS or mutli-tenant application models in mind. There are various architecture patterns that you could use but this is a key scenario for Azure When your SaaS app is built on Azure, you can also take advantage of adjacent and complementary services such as Azure Active Directory.
- Presenter guidance: Mobile apps are very important for mobile workforces and mobile customers. Why not modernize to extend to mobile devices. Slide talk track: Use your existing assets and skills and extend to the cloud to create modern mobile experiences. Mobile apps should be part of a broader modernization strategy.
- Presenter guidance: Born in the cloud apps are where you get to be creative. Slide talk track: Let’s talk about two types of born in the cloud apps. A hybrid app that complements your existing app And then on the next slide, we can talk about something that is completely net new.
- Presenter guidance: Born in the cloud apps are where you get to be creative. Slide talk track: It’s simple to get started with building an app in the cloud. You take your pick of data service and then add on a front end such as Websites. As you get more sophisticated, you just layer on additional services like HDInsight and Machine Learning.
- Presenter guidance: Introduce the family portrait. Slide talk track: This is how we think about the core differences across the data services for capturing and managing data On the left, you have more database imposed structure on the left and this loosens as you move to the right, ending in blobs which is just large containers of binary data.
- Presenter guidance: Transition slide to SQL in a VM Slide talk track: Let’s start with SQL in a VM which is the same SQL Server you use on-premises running in a virtual machine on Azure instead of on a virtual machine or on bare metal in your datacenter.
- Presenter guidance: Communicate that SQL Server running in a VM is just like SQL Server running on-premises but you’re being billed just for what you use. Slide talk track: Dev/Test with SQL Server running in a VM is one of the most common scenarios we see. Saves time and money
- Presenter guidance: Transition slide to SQL DB Slide talk track: SQL Server in a VM is great when you need 100% feature compatibility with SQL Server running on-premises, but what happens when you want to eliminate CapEx and reduce OpEx? This is exactly where SQL DB comes in.
- Presenter guidance: The gap between SQL Server and SQL DB is diminishing so the real long-term differentiation is on the DB:IT ratio. Just like with all PaaS services, you don’t want to talk about putting people out of a job, but about allowing people to focus on adding value. Slide talk track: “If you went to work in the tech industry and thought you’d never have to learn anything new, well then you went into the wrong industry” SQL DB reduces OpEx and eliminates CapEx Near-zero administration and enterprise grade capabilities
- Slide talk track: This is the journey that SQL DB has been on over the last year
- Presenter guidance: Microsoft is not just about relational. We have powerful NoSQL options as well. Without going deep on NoSQL and the different flavor of it, there are 4 major categories – key-value (Tables), document (DocumentDB), column (HBase), and graph. There’s a great David Chappell paper for getting up to speed on NoSQL - http://azure.microsoft.com/en-us/documentation/articles/fundamentals-data-management-nosql-chappell/ Slide talk track: NoSQL’s popularity originally started because of limitations of relational stores. Many of these limitations have since been addressed, but there are still a lot of compelling reasons to choose a NoSQL store. In the case of DocumentDB, we saw an unaddressed gap in the market place for Apps produce and consume data at a staggering rate while requiring instantaneous response times to match user expectations Developed iteratively with many versions supported concurrently and continuously evolving data models Increasingly complex, compensating for the lack of query and transactional processing in existing NoSQL database systems Experience unpredictable and explosive growth We heard from internal and external customers that they didn’t want to run and manage NoSQL document stores especially at massive scale. Customers also told us they needed more of the query and transaction processing capabilities that are inherent to relational systems. So we considered what it would take to build a schema-free database with robust query and transaction processing using modern data formats, languages and protocols – the result of this effort is DocumentDB
- Presenter guidance: Overview of DocumentDB and key points of differentiation Slide talk track: So what is DocumentDB? DocumentDB is a NoSQL document database-as-a-service, fully managed by Microsoft Azure. It is for cloud-designed apps when query over schema-free data; reliable and predictable performance; and rapid development are key. It’s the first of its kind database service to offer native support for JavaScript, SQL query and transactions over schema-free JSON documents. The key benefits of DocDB can be broken down into three pillars. Rich query and transaction over schema-free data – which includes the concepts of query, automatic indexing, transactions, sql-like query language Reliable and Predictable Peformance – built for the cloud, tunable consistency, elastic Rapid Development – benefits of being part of Azure, build with familiar tools (so you can bring your JSON data and take it away) Together, you have a service that is perfect for cloud architects and developers who need an enterprise-ready NoSQL document database.
- Presenter guidance: Tying DocDB back to scenarios and adding in some customers. Slide talk track: DocDB is particularly suited for web and mobile application development of all types, but a few scenarios seem to be great fit for the service and we’ve seen these scenarios in the private preview. 1st is User Generated Data – these are apps that need to be able to store, roam, and query user created data across a wide range of applications. An example of this would be comments in an ecommerce application. 2nd are gaming apps – we’ve seen a good amount of interest from gaming companies in the private preview. Games are increasingly leveraging telemetry and behavioral data to adjust the application experience in real time. This data could be used to determine trending content that would be offered to more users, effectiveness of in-app marketing, or queried to provide real-time application analytics. And as I mentioned in the beginning Xbox is using DocDB. 3rd is the social category – we’ll add an additional label to this category of activity and awareness. Social applications create, store, and query a high volume of data. They integrate with one or more social networks and need to keep pace with the changing demands of an active user base. In many cases the user data stored is meta-data that represents content or activity events for common media types such as photos, books or videos. 4th is content data exchange – this has to do with data from applications such as internet of things applications but its not just about storing telemetry data for processing but also storing and serving the data to other applications that may need it MSN has ~440MM users. Only a slice of that uses the Health and Fitness app which is what is running on DocumentDB. For this they have provisioned 150 CUs. If this was one of your customers, that would equal over $80 per year just to support a single app. Xomni is an omni-channel retail platform in the cloud. They really appreciate the fact that DocDB is a fully managed service on Azure that helps them expand their offerings by letting them focus on writing code instead of managing systems.
- Presenter guidance: Last but not least is Azure Storage – specifically Tables and Blobs.
- Blob Storage For users with large amounts of unstructured data to store in the cloud, Blob storage offers a cost-effective and scalable solution. You can use Blob storage to store content such as: Documents Social data such as photos, videos, music, and blogs Backups of files, computers, databases, and devices Images and text for web applications Configuration data for cloud applications Big data, such as logs and other large datasets Every blob is organized into a container. Containers also provide a useful way to assign security policies to groups of objects. A storage account can contain any number of containers, and a container can contain any number of blobs, up to the 500 TB capacity limit of the storage account. Blob storage offers two types of blobs, block blobs and page blobs (disks). Block blobs are optimized for streaming and storing cloud objects, and are a good choice for storing documents, media files, backups etc. A block blob can be up to 200 GB in size. Page blobs are optimized for representing IaaS disks and supporting random writes, and may be up to 1 TB in size. An Azure virtual machine network attached IaaS disk is a VHD stored as a page blob. For very large datasets where network constraints make uploading or downloading data to Blob storage over the wire unrealistic, you can ship a hard drive to Microsoft to import or export data directly from the data center using the Azure Import/Export Service. You can also copy blob data within your storage account or across storage accounts. Table Storage Modern applications often demand data stores with greater scalability and flexibility than previous generations of software required. Table storage offers highly available, massively scalable storage, so that your application can automatically scale to meet user demand. Table storage is Microsoft’s NoSQL key-value store – it has a schemaless design, making it different from traditional relational databases. With a schemaless data store, it's easy to adapt your data as the needs of your application evolve. Table storage is easy to use, so developers can create applications quickly. Access to data is fast and cost-effective for all kinds of applications. Table storage is typically significantly lower in cost than traditional SQL for similar volumes of data. Table storage is a key-value store, meaning that every value in a table is stored with a typed property name. The property name can be used for filtering and specifying selection criteria. A collection of properties and their values comprise an entity. Since Table storage is schemaless, two entities in the same table can contain different collections of properties, and those properties can be of different types. You can use Table storage to store flexible datasets, such as user data for web applications, address books, device information, and any other type of metadata that your service requires. You can store any number of entities in a table, and a storage account may contain any number of tables, up to the capacity limit of the storage account. Like Blobs and Queues, developers can manage and access Table Storage using standard REST protocols, however Table Storage also supports a subset of the OData protocol, simplifying advanced querying capabilities and enabling both JSON and AtomPub (XML based) formats. For today's Internet-based applications, NoSQL databases like Table storage offer a popular alternative to traditional relational databases.