This blog post was co-authored by Erin Chapple, CVP, Microsoft Windows Server, and Rohan Kumar, CVP, Microsoft Data.
The beginning of a new year is always a time to reflect on our plans. At Microsoft, with the end of support for 2008 servers looming, we’ve been thinking about how we can help you with your server refresh journey. How can we enable you to take advantage of all the cutting-edge innovations available in Azure?
And as we take stock, we believe that the 3 reasons why Azure is the best place to transform your 2008 server applications are:
Security: With security threats becoming more and more sophisticated, increasing your organization’s security policies should be top of mind. The good news is that Azure is the most trusted cloud in the market with more certifications than any other public cloud. Innovation: We have an optimized, low-risk path to help you embrace Azure. And once you are there, you can continue to innovate with fully-managed services such as Azure SQL Database, Azure Cosmos DB and Azure AI. Cost savings: By taking advantage of Azure Hybrid Benefit and Extended Security updates, you can save significantly. For example, moving a hundred 2008 servers to
The year 2018 was a banner year for Azure AI as over a million Azure developers, customers, and partners engaged in the conversation on digital transformation. The next generation of AI capabilities are now infused across Microsoft products and services including AI capabilities for Power BI.
Here are the top 10 Azure AI highlights from 2018, across AI Services, tools and frameworks, and infrastructure at a glance:
3. Microsoft is first to enable Cognitive Services in containers.
4. Cognitive Search and basketball
AI tools and frameworks
7. Open Neural Network Exchange (ONNX) runtime is now open source.
10. Project Brainwave, integrated with AML.
With many exciting developments, why are these moments the highlight? Read on, as this blog begins to explain the importance of these moments.
These services span pre-built
On December 4, 2018 Microsoft’s Azure Database for open sources announced the general availability of MariaDB. This blog intends to share some guidance and best practices for alerting on the most commonly monitored metrics for MariaDB.
Whether you are a developer, a database analyst, a site reliability engineer, or a DevOps professional at your company, monitoring databases is an important part of maintaining the reliability, availability, and performance of your MariaDB server. There are various metrics available for you in Azure Database for MariaDB to get insights on the behavior of the server. You can also set alerts on these metrics using the Azure portal or Azure CLI.
With modern applications evolving from a traditional on-premises approach to becoming more hybrid or cloud native, there is also a need to adopt some best practices for a successful monitoring strategy on a hybrid/public cloud. Here are some example best practices on how you can use monitoring data on your MariaDB server and areas you can consider improving based on these various metrics.
Sample threshold (percentage or value): 80 percent of total connection limit for greater than or equal to 30 minutes, checked every five minutes.
Things to check
At Ignite 2018, Microsoft’s Azure Database for PostgreSQL announced the preview of Query Store (QS), Query Performance Insight (QPI), and Performance Recommendations (PR) to help ease performance troubleshooting, in response to customer feedback. This blog intends to inspire ideas on how you can use features that are currently available to troubleshoot some common scenarios.
A previous blog post on performance best practices touched upon the layers at which you might be experiencing issues based on the application pattern that you are using. This blog nicely categorizes the problem space into several areas and the common techniques to rule out possibilities to quickly get to the root cause. We would like to further expand on this with the help of these newly announced features (QS, QPI, and PR).
In order to use these features, you will need to enable data collection by setting pg_qs.query_capture_mode and pgms_wait_sampling.query_capture_mode to ALL.
You can use Query Store for a wide variety of scenarios where you can enable data collection to help with troubleshooting these scenarios better. In this article, we will limit the scope to regressed queries scenario.
One of the important scenarios that Query Store enables you to monitor is the
Connecting data from various sources in a unified view can produce valuable insights that are otherwise invisible to the human eye and brain. As Azure Cosmos DB allows for collecting the data from various sources in various formats, the ability to mix and match this data becomes even more important for empowering your businesses with additional knowledge and intelligence.
This is what Qlik’s analytics and visualization products, QlikView and Qlik Sense, have been able to do for years and now they support Azure Cosmos DB as a first-class data source. The following table summarizes the variety of connectivity options you have for to getting Azure Cosmos DB data in QlikView and Qlik Sense.
Azure Cosmos DB API
Qlik detailed instruction
Qlik live demo
Core (SQL) API
Core (SQL) API
Do you want to analyze vast amounts of data, create Power BI dashboards and reports to help you visualize your data, and share insights across your organization? Azure Data Explorer (ADX), a lightning-fast indexing and querying service helps you build near real-time and complex analytics solutions for vast amounts of data. ADX can connect to Power BI, a business analytics solution that lets you visualize your data and share the results across your organization. The various methods of connection to Power BI allow for interactive analysis of organizational data such as tracking and presentation of trends.
Simple and intuitive native connector
The native connector to Power BI unlocks the power of Azure Data Explorer in only a minute. In a very intuitive process, add your cluster name and let the connector take care of the rest. Provide the database and table name to focus your analysis on specific data. You can use import mode for snappy interaction with the data or direct query mode for filtering large datasets and near real-time updates. To use the native connector method read our documentation, “Quickstart: Visualize data using the Azure Data Explorer connector for Power BI.”
A specific Azure Data Explorer query can
We are excited to reveal a public preview of a new feature in Azure SQL Database, both in logical server and Managed Instance, called CLUSTERED COLUMNSTORE ONLINE INDEX build. This operation enables you to migrate your data stored in row-store format to the columnstore format and maintain your columnstore data structures with the minimal downtime on your workload.
Why columnstore format?
Azure SQL Database enables you to fine-tune and optimize data structures and indexes in your database to get the best performance of your queries depending on your workload and size of data. Relational data in Azure SQL Database can be organized in two formats:
Row-store format, which is an ideal option for OLTP workloads where the queries are accessing individual rows or set of rows in the table. This is the general-purpose table format used for most of the data in relational databases. Columnstore format, which is optimized for analytical queries and high compression of data (up to 100x). This format is perfect for the large data sets that can be efficiently compressed using this format and the analytical queries with complex calculations that use subset of the table columns.
In some cases, you might notice that your existing
We are excited to announce the public preview of Transparent Data Encryption (TDE) with Bring Your Own Key (BYOK) support for Microsoft Azure SQL Database Managed Instance. Azure SQL Database Managed Instance is a new deployment option in SQL Database that combines the best of on-premises SQL Server with the operational and financial benefits of an intelligent, fully-managed relational database service.
TDE with BYOK support has been generally available for single databases and elastic pools since April 2018. It is one of the most frequently requested capabilities by enterprise customers who are looking to protect data at rest, or meet regulatory and compliance obligations that require implementation of specific key management controls. TDE with BYOK support is offered in addition to TDE with service managed keys which is enabled on all new Azure SQL Databases, single databases, pools, and managed instances by default.
TDE with BYOK support uses Azure Key Vault, which provides highly available and scalable secure storage for RSA cryptographic keys backed by FIPS 140-2 Level 2 validated hardware security modules (HSMs). Azure Key Vault streamlines the key management process and enables customers to maintain full control of encryption keys, including managing and auditing key access.
Pharmaceutical companies need to meet demanding sales goals, manage intricate regulatory compliance, and maintain a competitive hold on the market. However, current sales force automation (SFA) solutions for the life sciences industry are focused primarily on sales reps, which leaves a large capability gap for sales operations departments and inhibits their ability to support the sales process.
Prescriber360 is a Microsoft Gold Partner with a comprehensive Pharma SalesOps solution designed specifically for the life sciences industry that can reduce, and even close, the capability gap.
Problem: Data spread out and unavailable
Pharma and biotech industries rely heavily on both internal and external data sources. However, most sales operations processes are currently performed manually in spreadsheets and without a centralized system of record. This makes it incredibly difficult to access and leverage that data.
The following problems often result:
Sales teams built around multiple therapeutic areas and prescriber specialties present a complex targeted environment requiring powerful systems and processes. Quarterly alignment processes require touching an entire customer universe, and it takes a lot of effort to update an SFA system with new information, maintain a history of changes to support incentive compensation (IC), and preview the impact of alignments on