https://azure.microsoft.com/blog/faster-and-cheaper-sql-on-azure-continues-to-outshine-aws/Over a million on-premises SQL Server databases have moved to Azure, representing a massive shift in where customers are collecting, storing, and analyzing their data. Modernizing your databases provides the opportunity to transform your data architecture. SQL Server on Azure READ MORE
With reserved capacity, you get significant discounts over your on-demand costs by committing to long-term usage of a service. We are pleased to share reserved capacity offerings for the following additional services. With the addition of these services, we now support reservations for 16 services, giving you more options to save and get better cost predictability across more workloads.
Blob Storage (GPv2) and Azure Data Lake Storage (Gen2). Azure Database for MySQL. Azure Database for PostgreSQL. Azure Database for MariaDB. Azure Data Explorer. Premium SSD Managed Disks. Blob Storage (GPv2) and Azure Data Lake Storage (Gen2)
Save up to 38 percent on your Azure data storage costs by pre-purchasing reserved capacity for one or three years. Reserved capacity can be pre-purchased in increments of 100 TB and 1 PB sizes, and is available for hot, cool, and archive storage tiers for all applicable storage redundancies. You can also use the upfront or monthly payment option, depending on your cash flow requirements.
The reservation discount will automatically apply to data stored on Azure Blob (GPv2) and Azure Data Lake Storage (Gen2). Discounts are applied hourly on the total data stored in that hour. Unused reserved capacity doesn’t carry over.
Azure Stream Analytics is a fully managed Platform as a Service (PaaS) that supports thousands of mission-critical customer applications powered by real-time insights. Out-of-the-box integration with numerous other Azure services enables developers and data engineers to build high-performance, hot-path data pipelines within minutes. The key tenets of Stream Analytics include Ease of use, Developer productivity, and Enterprise readiness. Today, we’re announcing several new features that further enhance these key tenets. Let’s take a closer look at these features:
Rollout of these preview features begins November 4th, 2019. Worldwide availability to follow in the weeks after.
In the past, changing Streaming Units (SUs) allocated for a Stream Analytics job required users to stop and restart. This resulted in extra overhead and latency, even though it was done without any data loss.
With online scaling capability, users will no longer be required to stop their job if they need to change the SU allocation. Users can increase or decrease the SU capacity of a running job without having to stop it. This builds on the customer promise of long-running mission-critical pipelines that Stream Analytics offers today.
Change SUs on a Stream Analytics job while it is running.
Access to Diagnostic Logs is essential for any healthcare service where being compliant with regulatory requirements (like HIPAA) is a must. The feature in Azure API for FHIR that makes this happen is Diagnostic settings in the Azure Portal UI. For details on how Azure Diagnostic Logs work, please refer to the Azure Diagnostic Log documentation.
At this time, service is emitting the following fields in the Audit Log:
Notes TimeGenerated DateTime Date and Time of the event.
String CorrelationId String RequestUri String The request URI. FhirResourceType String The resource type the operation was executed for. StatusCode Int The HTTP status code (e.g., 200). ResultType String The available value currently are ‘Started’, ‘Succeeded’, or ‘Failed.’ OperationDurationMs Int The milliseconds it took to complete the request. LogCategory String The log category. We are currently emitting ‘AuditLogs’ for the value. CallerIPAddress String The caller’s IP address. CallerIdentityIssuer String Issuer CallerIdentityObjectId String Object_Id CallerIdentity Dynamic A generic property bag containing identity information. Location String The location of the server that processed the request (e.g., South Central US). How do
This post was co-authored by Mike Emard Principal Program Manager, Azure Storage.
SQL Server on Azure virtual machines brings cloud agility, elasticity, and scalability benefits to SQL Server workloads. SQL virtual machine offers full control on the operating system, virtual machine size, storage subsystem, and the level of manageability needed for your workload. Preconfigured SQL Server image from Azure Marketplace comes with free SQL Server manageability benefits like Automated Backup and Automated Patching. If you choose to self-install SQL Server on Azure virtual machines then you can register with SQL virtual machine resource provider to get all the benefits available to SQL marketplace images and simplified license management.
Microsoft provides an availability SLA of 99.95 percent that covers just the virtual machine not SQL Server. For SQL Server high availability on Azure virtual machines, you should host at least two virtual machine instances in an availability set (for availability at 99.95 percent) or different availability zones (for availability at 99.99 percent) and configure a high availability feature for SQL Server, such as Always On availability groups or failover cluster instance.
Today, we are announcing a new option for SQL Server high availability with SQL Server failover cluster with Azure premium file shares. Premium file shares are solid-state drive backed consistent
This post was co-authored by Jamie Reding, Senior Program Manager, Sadashivan Krishnamurthy, Principal Architect, and Bob Ward, Principal Architect.
Today, most applications are running online transactional processing (OLTP) transactions. Online banking, purchasing a book online, booking an airline ticket, sending a text message, and telemarketing are examples of OLTP workloads. OLTP workloads involves inserting, updating, and/or deleting small amounts of data in a database and mainly deals with large numbers of transactions by large number of users. Majority of OLTP workloads are read heavy, use diverse transactions, and utilizes wide range of data types.
Azure brings many price-performance advantages for your workloads with SQL Server on Azure Virtual Machines (VM) with a wide range of Azure Virtual Machine series and Azure disk options. Memory optimized VM series like Intel based Es_v3 series or AMD based Eas_v3 series offer high virtual CPU (vCPU) to memory ratio at a very low cost. Constraint vCPU capable VM sizes offer reduced cost of SQL Server licencing by constraining the vCPU abailable to the VM, while maintaining the same memory, storage, and input or output (I/O) bandwidth. Premium Solid State Drives (SSDs) deliver high-performance and low-latency managed disks with high IOPS and throughput capabilities needed for SQL
More companies are choosing Azure for their SQL workloads, and it is easy to see why. Azure SQL Database is evergreen, meaning it does not need to be patched or upgraded, and it has a strong track record of innovation and reliability for mission-critical workloads. But, in addition to delivering unparalleled innovation, it is also important to provide customers with the best price-performance. Here, once again, SQL Database comes out on top.
SQL Database leads in price-performance for mission-critical workloads
GigaOm, an independent research firm, recently published a study where they tested throughput performance between Azure SQL Database and SQL Server on AWS RDS. SQL Database emerged as the price-performance leader for mission-critical workloads while costing up to 86 percent less than AWS RDS.1
The image above is a price-performance comparison from the GigaOm report. The price-performance metric is price divided by throughput (transactions per second, tps). Lower is better.
Customers like H&R Block found it easy to extend their on-premises experience to Azure, where they tapped into new levels of performance, scalability, and flexibility.
“SQL Database managed instance gives us a smooth migration path for moving existing workloads to Azure with minimal technical reengineering. All the applications
The tech world is fast-paced, and cloud services like Azure Cosmos DB get frequent updates with new features, capabilities, and improvements. It’s important—but also challenging—to keep up with the latest performance and security updates and assess whether they apply to your applications. To make it easier, we’ve introduced automatic and tailored recommendations for all Azure Cosmos DB users. A large spectrum of personalized recommendations now show up in the Azure portal when you browse your Azure Cosmos DB accounts.
Some of the recommendations we’re currently dispatching cover the following topics
SDK upgrades: When we detect the usage of an old version of our SDKs, we recommend upgrading to a newer version to benefit from our latest bug fixes and performance improvements. Fixed to partitioned collections: To fully leverage Azure Cosmos DB’s massive scalability, we encourage users of legacy, fixed-sized containers that are approaching the limit of their storage quota to migrate these containers to partitioned ones. Query page size: We recommend using a query page size of -1 for users that define a specific value instead. Composite indexes: Composite indexes can dramatically improve the performance and RU consumption of some queries, so we suggest their usage whenever our telemetry detects
Earlier this year, we announced a preview of built-in Jupyter notebooks for Azure Cosmos DB. These notebooks, running inside Azure Cosmos DB, are now available.
Cosmic notebooks are available for all data models and APIs including Cassandra, MongoDB, SQL (Core), Gremlin, and Spark to enhance the developer experience in Azure Cosmos DB. These notebooks are directly integrated into the Azure Portal and your Cosmos accounts, making them convenient and easy to use. Developers, data scientists, engineers and analysts can use the familiar Jupyter notebooks experience to:
Interactively run queries Explore and analyze data Visualize data Build, train, and run machine learning and AI models
In this blog post, we’ll explore how notebooks make it easy for you to work with and visualize your Azure Cosmos DB data.
Easily query your data
With notebooks, we’ve included built-in commands to make it easy to query your data for ad-hoc or exploratory analysis. From the Portal, you can use the %%sql magic command to run a SQL query against any container in your account, no configuration needed. The results are returned immediately in the notebook.
Improved developer productivity
We’ve also bundled in version 4 of our Azure Cosmos DB Python SDK
In the world of cloud database services, few things are more important to customers than having uninterrupted access to their data. In industries like online gaming and financial services that experience high transaction rates, even the smallest interruptions can potentially impact the end-user’s experience. Azure SQL Database is evergreen, meaning that it always has the latest version of the SQL Engine, but maintaining this evergreen state requires periodic updates to the service that can take the database offline for a second. For this reason, our engineering team is continuously working on innovative technology improvements that reduce workload interruption.
Figure 1 – This is what hot patching looks like under the covers. If you’re interested in the low-level details, see our technical blog post.
The SQL Engine we are running in Azure SQL Database is the very latest version of the same engine customers run on their own servers, except we manage and update it. To update SQL Server or the underlying infrastructure (i.e., Azure Service Fabric or the operating system), we must stop the SQL Server