SQL Server 2017, in addition to processing relational data, now fully integrates with graph database models, all on the same familiar system. This will bring clarity to the increasing amounts of data businesses generate every day.
What’s the difference between graph and relational databases?
Relational databases, like SQL Server, use foreign keys to manage relationships between entities and tables. Foreign keys adequately query one-to-many relationships; however, as relationships between various data entities become more complex, queries also become more complex and performance may diminish as a result.
In these cases, developers may opt for graph database models to manage complex relationships and enable operational agility. In a graph database, edges are heterogeneous in nature—a single edge can be used to connect different type of nodes to each other. This is not easy to achieve using foreign keys in a relational database. For example, consider a social graph where a person (node) likes another person (edge) or organization (node) or restaurant (node). Here the same ‘likes’ edge is used to connect three different types of nodes and entities to each other—person to person; person to organization; and person to restaurant.
So, how do graph databases work?
Graph databases are comprised of a
This post was authored by the Azure Bot Service and Language Understanding Team.
Microsoft brings the latest advanced chatbot capabilities to developers’ fingertips, allowing them to create apps that see, hear, speak, understand, and interpret users’ needs — using natural communication styles and methods.
Today, we’re excited to announce we’re making generally available Microsoft Cognitive Services Language Understanding service (LUIS) and Azure Bot Service, two top notch AI services to create digital agents that interact in natural ways and make sense of the surrounding environment.
Think about the possibilities: all developers regardless of expertise in data science able to build conversational AI that can enrich and expand the reach of applications to audiences across a myriad of conversational channels. The app will be able to understand natural language, reason about content and take intelligent actions. Bringing intelligent agents to developers and organizations that do not have expertise in data science is disruptive to the way humans interact with computers in their daily life and the way enterprises run their businesses with their customers and employees.
Through our preview journey in the past two years, we have learned a lot from interacting with thousands of customers undergoing digital transformation. We highlighted
Conversational AI, or making human and computer interactions more natural, has been a goal since technology became ubiquitous in our society. Our mission is to bring conversational AI tools and capabilities to every developer and every organization on the planet, and help businesses augment human ingenuity in unique and differentiated ways.
Today, I’m excited to announce Microsoft Azure Bot Service and Microsoft Cognitive Services Language Understanding (LUIS) are both generally available.
Azure Bot Service enables developers to create conversational interfaces on multiple channels while Language Understanding (LUIS) helps developers create customized natural interactions on any platform for any type of application, including bots. Making these two services generally available on Azure simultaneously extends the capabilities of developers to build custom models that can naturally interpret the intentions of people conversing with bots.
This announcement delivers on our AI Platform approach, providing developers and data scientists with all the tools they need to create AI applications in the cloud and on mobile devices. In November, at Connect(); 2017, we released tools to infuse AI into new and existing applications quickly and easily with updates to Azure Machine Learning (AML) including Azure IoT Edge integration, as well as new Visual Studio Tools
As an update to the Reporting APIs for Enterprise customers we are releasing an updated usage details API. This is a first step in the consolidation of Azure cost and usage based APIs in the ARM (Azure Resource Manager) model. The updated usage details API will support:
Migrating from a key based authorization model to ARM based authentication. The benefits of this authorization mode are an improved security posture and the ability to utilize ARM RBAC for authorization. Adding support for Web Direct subscriptions, with a few exceptions documented below. The ability to use filters and expand usage details. Call the API for either a subscription scope, or a subscription and billing period scope. All calls for a subscription will return data for the current billing period. Filter criteria will support dates, resource groups, resources, and instances. Additional details on the filters are available in the Swagger.
For Enterprise customers, reporting at a grain higher than the subscription is a work in progress and until released, you will need to continue to use the existing API. The consumption ARM API is the area we continue to invest in for cost related APIs with the goals of normalizing our APIs across
Enterprise customers choose Azure because of the unique value it provides as a productive, hybrid, intelligent and trusted cloud. Today I’m excited to announce four new management and cost savings capabilities. Azure Policy, now in public preview, provides control and governance at scale for your Azure resources. Azure Cost Management is rolling out the support for Azure Virtual Machine Reserved Instances management later this week to help you maximize savings over time.. To continue our commitment to making Azure cost-effective, we are reducing the prices of up to 4% on our Dv3 Series in several regions in the coming days, and making our lowest priced Storage tier Azure Archive Storage generally available today.
Simple ways to ensure a secure and well-managed cloud infrastructure
Azure is committed to providing a secure cloud foundation, while making available a comprehensive set of services to ensure that your cloud resources are secure and well-managed. Cloud security and management is a joint responsibility between Microsoft and the customer. We recommend that customers follow secure and well-managed cloud best practices for every production virtual machine. To help you achieve this goal, Azure has built-in services that can be configured quickly, are always up to date and
I am excited to announce the general availability (GA) of the Azure Site Recovery Deployment Planner for VMware and Hyper-V. This tool helps VMware and Hyper-V enterprise customers to understand their on-premises networking requirements, Microsoft Azure compute and storage requirements for successful Azure Site Recovery replication, and test failover or failover of their applications.
Apart from understanding infrastructure requirements, our customers also needed a way to estimate the total disaster recovery (DR) cost to Azure. In this GA release, we have added detailed estimated DR cost to Azure for your environment. You can generate a report with the latest Azure prices based on your subscription, the offer that is associated with your subscription, and the target Azure region for the specified currency. The Deployment Planner report gives you cost for compute, storage, network, and Azure Site Recovery licenses.
Key features of the tool The Deployment Planner can be run without having to install any Azure Site Recovery components to your on-premises environment. The tool does not impact the performance of production servers, as no direct connection is made to them. All performance data is collected from the Hyper-V server or VMware vCenter Server/VMware vSphere ESXi Server, which hosts the production
Today we’re excited to announce the general availability of Archive Blob Storage starting at an industry leading price of $0.002 per gigabyte per month! Last year, we launched Cool Blob Storage to help customers reduce storage costs by tiering their infrequently accessed data to the Cool tier. Organizations can now reduce their storage costs even further by storing their rarely accessed data in the Archive tier. Furthermore, we’re also excited to announce the general availability of Blob-Level Tiering, which enables customers to optimize storage costs by easily managing the lifecycle of their data across these tiers at the object level.
From startups to large organizations, our customers in every industry have experienced exponential growth of their data. A significant amount of this data is rarely accessed but must be stored for a long period of time to meet either business continuity or compliance requirements; think employee data, medical records, customer information, financial records, backups, etc. Additionally, recent and coming advances in artificial intelligence and data analytics are unlocking value from data that might have previously been discarded. Customers in many industries want to keep more of these data sets for a longer period but need a scalable and cost-effective solution
This post is authored by Alan Yu, Program Manager, SQL Server.
We are excited to announce the Public Preview release of mssql-cli, a new and interactive command line query tool for SQL Server. This open source tool works cross-platform and is a proud member of the dbcli community.
See the install guide to download mssql-cli and get started.
Read on to learn more about mssql-cli features, how to submit feature requests or issues, and our open source collaboration story to bring you this great tool.
mssql-cli auto-completion that is context aware
Mssql-cli is a new and interactive command line tool that provides the following key enhancements over sqlcmd in the Terminal environment:
T-SQL IntelliSense Syntax highlighting Pretty formatting for query results, including Vertical Format Multi-line edit mode Configuration file support
Mssql-cli aims to offer an improved interactive command line experience for T-SQL. It is fully open source under the BSD-3 license, and a contribution to the dbcli organization, an open source suite of interactive CLI tools for relational databases including SQL Server, PostgresSQL, and MySQL. The command-line UI is written in Python and the tool leverages the same microservice backend (sqltoolsservice) that powers the VS Code SQL extension,
https://powerbi.microsoft.com/en-us/blog/bookmarking-contest-dec-12th-dec-27th/Source: https://powerbi.microsoft.com/en-us/blog/bookmarking-contest-dec-12th-dec-27th/ Earlier this week, we announced an update to the new “Bookmark” feature in Power BI Desktop, and we’re excited to see what you do with it! So, today we’re kicking off our “Bookmark” READ MORE
A few years ago, the Microsoft SQL Server product team introduced a new cloud Platform-as-a-Service (PaaS), Azure SQL Database, which shares the SQL Server code base. Running a cloud-first service required significant changes to the legacy SQL Server engineering model which took years of investment in order to fully enable. With these engineering model changes came big benefits which positively impacted both Azure SQL Database and SQL Server.
Even if you are a SQL Server database administrator who isn’t using Azure SQL Database today, you’ll still be seeing benefits from Microsoft’s investments in the cloud. This blog post will review how engineering model transformations, driven by cloud requirements, resulted in several improvements in how we build, ship and service SQL Server.
Features arrive faster
In the earlier days of SQL Server (2005 through 2012), SQL Server had roughly three-year long engineering cycles. For each planned release of SQL Server, a significant amount of planning would go into the up-front design, using a waterfall-like software development process coordinated across different teams. This included the generation of functional specification documentation by program managers, design specifications by developers and automated testing code developed by testers.
Once SQL Server finally shipped, customers could take years to