Why IaaS on Azure Resource Manager
It has been more than 2 years since we launched IaaS on Azure Resource Manager. Since then, we’ve been busy adding awesome features to this new stack in addition to all the features of the classic stack. Below are some of the features and benefits you get by deploying your infrastructure on Azure Resource Manager:
Compute Managed Disks – Simplify your storage management by exposing disks as a top level resource. In addition, Managed Disks are designed to improve the availability of your virtual machines. Learn more about the other benefits of using Managed Disks. Virtual machine scale sets – Provide a great blend of IaaS like control with PaaS like manageability. Scale sets allow you to reliably deploy and update a large set of virtual machines at large scale. Availability zones – Have peace of mind knowing that your mission critical applications can withstand datacenter-level failures. Instance metadata service – Provides a RESTful endpoint that allows virtual machines instances to get information regarding its compute, network, and upcoming maintenance events from within the virtual machine. Reserved instances – Allows reservation of virtual machines in advance, and significantly reduce costs compared to pay-as-you-go prices.
At Microsoft Ignite, we announced new adaptive applications controls that protect your applications from malware by using whitelisting rules. Today, we are excited to share that these capabilities are available for public preview in Azure Security Center.
Application controls, such as whitelisting, can help limit exposure to malicious and vulnerable applications. Instead of trying to keep pace with rapidly evolving malware and new exploits, application control simply blocks all but known good applications. For purpose-built servers that typically run a fixed set of application, whitelisting can add significant protection. Application control solutions have existed for some time now, but organizations usually find it too complex and hard to manage, especially when unique rules are required per server or group of servers, and in large scale.
Adaptive Application Controls leverages machine learning to analyze the behavior of your Azure virtual machines, create a baseline of applications, group the virtual machines, and recommend and automatically apply the appropriate whitelisting rules. You can view, modify, and receive alerts for these rules in Azure Security Center.
Adaptive application controls are currently available for Windows virtual machines running in Azure (all versions, classic or Azure Resource Manager). To get started, open Security Center and select
In case you missed them, we’ve posted the Azure Cosmos Database and Azure SQL Database webinars for on-demand viewing. The first webinar takes a closer look at Azure Cosmos DB, a globally distributed, multi-model database service that enables scaled throughput and storage across many geographical regions. The second webinar shares how you can make the most of Azure SQL Database’s machine learning features to deliver intelligent apps to your customers.
Azure Cosmos DB – Easily build globally distributed, highly scalable applications
Building successful apps depends on having well-indexed and formatted data—regardless of how or where data is stored. With Azure Cosmos DB, you can build globally distributed applications without the hassle of complex, multi-datacenter configurations.
Tune in to this webinar and learn how you can leverage Azure Cosmos DB to:
Create lightning-fast globally distributed apps Model your app’s data using familiar tools and APIs Easily distribute data across multiple regions Fine-tune performance based on your application’s needs. Register to watch this webinar on demand. Build intelligent apps faster with Azure SQL Database
Applications benefit when machine learning intelligence is applied to its underlying databases to optimize performance. Intelligent apps can spot trends, react to unusual events, and make useful predictions
https://powerbi.microsoft.com/en-us/blog/on-premises-data-gateway-december-update-is-now-available/Source: https://powerbi.microsoft.com/en-us/blog/on-premises-data-gateway-december-update-is-now-available/ We are happy to announce that we have just released the December update for the On-premises data gateway. This month’s Gateway update includes an updated version of the Mashup Engine,…
This blog post was co-authored by Kiran Madnani, Principal PM Manager, AIM India.
Alerts are an important mechanism for customers to get notified about issues early and to take automated actions to resolve them quickly. With Azure Monitoring Services, you can set up alerts to monitor the metrics and log data for the entire stack across your infrastructure, application, and platform. Given how integral it is to the monitoring experience, we are excited to announce the preview of a new re-imagined user interface to create and manage alerts for any resource from a single location in the Azure Monitor blade in the Azure portal.
You can access the new Alerts (preview) experience by visiting the Azure portal. You will see:
1. A single consolidated view with an aggregated summary of fired alerts triggered by metrics and logs at a glance.
2. A single consolidated view for viewing and managing the underlying alert rules across metrics and logs across multiple subscriptions.
3. A single simplified authoring experience for creating an alert across resources.
In addition to the new alerting experience, we are also pleased to announce the preview of alerting on log search in Azure. Log
This blog post is authored by Dotan Patrich, Senior Software Engineer, Azure Security Center and by Yossi Weizman, Security Software Engineer Intern, Azure Security Center.
Earlier this year, Rob Mead wrote a great article on the techniques used at scale by Azure Security Center to detect threats. In this post, we’ll go into the details on one such example, enabling Azure Security Center to detect usage of backdoor user account creation.
Backdoor user accounts are those accounts that are created by an adversary as part of the attack, to be used later in order to gain access to other resources in the network, open new entry points into the network as well as achieve persistency. MITRE lists the create account tactic as part of the credentials access intent of stage and lists several toolkits that uses this technique.
While it might seem at first glance that detecting such malicious account creation actions is easy, it is not often the case as creation of new accounts are mostly part of a legitimate administrative operation. Therefore, security products usually won’t alert on it as most organizations will have hard time coping with the volume of alerts to be triaged. This makes the
Fast SQL query processing at scale is often a key consideration for our customers. In this blog post we compare HDInsight Interactive Query, Spark, and Presto using the industry standard TPCDS benchmarks. These benchmarks are run using out of the box default HDInsight configurations, with no special optimizations. For customers wanting to run these benchmarks, please follow the easy to use steps outlined on GitHub.
Summary of the results HDInsight Interactive Query is faster than Spark. HDInsight Spark is faster than Presto. Text caching in Interactive Query, without converting data to ORC or Parquet, is equivalent to warm Spark performance. Interactive query is most suitable to run on large scale data as this was the only engine which could run all TPCDS 99 queries without any modifications at 100TB scale. Interactive Query preforms well with high concurrency. About TPCDS
The TPC Benchmark DS (TPC-DS) is a decision support benchmark that models several generally applicable aspects of a decision support system, including queries and data maintenance. According to TPCDS, the benchmark provides a representative evaluation of performance as a general purpose decision support system. A benchmark result measures query response time in single user mode, query throughput in multi-user mode and
We keep enriching the breadth of connectivity in Azure Data Factory to enable customers to ingest data from various data sources into Azure when building modern data warehouse solutions or data-driven SaaS applications. Today, we are excited to announce that Azure Data Factory newly enabled copying data from the following data stores using Copy Activity in V2. You can always find the full supported connector list from supported data stores, and click into each connector topic there to learn more details.
Amazon Marketplace Web Service (Beta) Azure Database for PostgreSQL Concur (Beta) Couchbase (Beta) Drill (Beta) Google BigQuery (Beta) Greenplum (Beta) HBase Hive HubSpot (Beta) Impala (Beta) Jira (Beta) Magento (Beta) MariaDB Marketo (Beta) Oracle Eloqua (Beta) Paypal (Beta) Phoenix Presto (Beta) QuickBooks (Beta) SAP Cloud for Customer (C4C) ServiceNow (Beta) Shopify (Beta) Spark Square (Beta) Xero (Beta) Zoho (Beta)
If you are using PowerShell or .NET/Python SDK to author, make sure you upgrade to the December version to use these new features. And for hybrid copy scenario, note these connectors are supported since Self-hosted Integration Runtime version 3.2.
You are invited to give them a try and provide us feedback. We hope you find them helpful in your scenario.
This post is authored by Alan Yu, Program Manager, SQL Server.
We are excited to announce the December release of SQL Operations Studio is now available.
SQL Operations Studio is a data management tool that enables you to work with SQL Server, Azure SQL DB and SQL DW from Windows, macOS and Linux. To learn more, visit our GitHub.
SQL Operations Studio was announced for Public Preview on November 15th at Connect(), and this December release is the first major update since the announcement.
The December release includes several major repo updates and feature releases, including:
Migrating SQL Ops Studio Engineering to public GitHub repo Azure Integration with Create Firewall Rule Windows Setup and Linux DEB/RPM installation packages Manage Dashboard visual layout editor “Run Current Query with Actual Plan” command
For complete updates, refer to the Release Notes.
Migrating SQL Ops Studio Engineering to public GitHub repo
To provide better transparency with the SQL Operations Studio community, we have decided to migrate the Github internal branch to the public repo. This means any bug fixes, feature developments, or even test builds can be publicly viewed before an
We recently concluded the Fall 2018 edition of the Machine Learning, AI & Data Science (MLADS) conference, Microsoft’s largest internal gathering of employees focused specifically on these areas. This latest edition was the eighth in a popular series that we launched back in 2014. Over 3,500 employees tuned into the sold-out conference, both in person in Redmond and over livestream throughout the world, and thousands more will tune into MLADS session recordings over coming weeks and months.
As application of AI and ML explode both within Microsoft and in our external products and services, the growth of our community interest groups catering to these areas has been very rapid. The MLADS conference itself is unique in that it is almost entirely driven by enthusiastic community volunteers – a band of employees unified in its passion for AI and ML, and a desire to network and learn from one another. The “call for content” that goes out for this conference series routinely gets several hundreds of submissions, and our volunteer team helps triage these submissions and curate the best ones for our event.
The fall 2018 conference featured over 95 talk sessions, 20 tutorials and 65 poster/demo sessions covering a gamut of