Author : All posts by ilikesql

19

Jul

Announcing the New Filter Experience for Power BI Reports

https://powerbi.microsoft.com/en-us/blog/announcing-the-new-filter-experience-for-power-bi-reports/Source: https://powerbi.microsoft.com/en-us/blog/announcing-the-new-filter-experience-for-power-bi-reports/           This month we are incredibly excited to announce that the new filter experience is now generally available. We have updated the look and feel and added a ton of new functionality. The new READ MORE

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18

Jul

Power BI Premium – Know What Your Premium Capacity Can Handle

https://powerbi.microsoft.com/en-us/blog/power-bi-premium-know-what-your-premium-capacity-can-handle/Source: https://powerbi.microsoft.com/en-us/blog/power-bi-premium-know-what-your-premium-capacity-can-handle/           Introducing a tool for accurate load testing of premium capacities

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18

Jul

https://azure.microsoft.com/blog/how-hsbc-built-its-payme-for-business-app-on-microsoft-azure/Bank-grade security, super-fast transactions, and analytics  If you live in Asia or have ever traveled there, you’ve probably witnessed the dramatic impact that mobile technology has had on all aspects of day to day life. In Hong Kong in particular, READ MORE

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18

Jul

MileIQ and Azure Event Hubs: Billions of miles streamed
MileIQ and Azure Event Hubs: Billions of miles streamed

This post was co-authored by Shubha Vijayasarathy, Program Manager, Azure Messaging (Event Hubs)

With billions of miles logged, MileIQ provides stress-free logging and accurate mileage reports for millions of drivers. Logging and reporting miles driven is a necessity for independent contractors to organizations with employees who need to drive for work. MileIQ automates mileage logging to create accurate records of miles driven, minimizing the effort and time needed with manual calculations. Real-time mileage tracking produces over a million location signal events per hour, requiring fast and resilient event processing that scales.

MileIQ leverages Apache Kafka to ingest massive streams of data:

Event processing: Events that demand time-consuming processing are put into Kafka, and multiple processors consume and process these asynchronously. Communication among micro-services: Events are published by the event-owning micro-service on Kafka topics. The other micro-services, which are interested in these events, subscribe to these topics to consume the events. Data Analytics: As all the important events are published on Kafka, the data analytics team subscribes to the topics it is interested in and pulls all the data it requires for data processing. Growth Challenges

As with any successful venture, growth introduces operational challenges as infrastructure struggles to support the

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18

Jul

New ways to train custom language models – effortlessly!

Video Indexer (VI), the AI service for Azure Media Services enables the customization of language models by allowing customers to upload examples of sentences or words belonging to the vocabulary of their specific use case. Since speech recognition can sometimes be tricky, VI enables you to train and adapt the models for your specific domain. Harnessing this capability allows organizations to improve the accuracy of the Video Indexer generated transcriptions in their accounts.

Over the past few months, we have worked on a series of enhancements to make this customization process even more effective and easy to accomplish. Enhancements include automatically capturing any transcript edits done manually or via API as well as allowing customers to add closed caption files to further train their custom language models.

The idea behind these additions is to create a feedback loop where organizations begin with a base out-of-the-box language model and improve its accuracy gradually through manual edits and other resources over a period of time, resulting with a model that is fine-tuned to their needs with minimal effort.

Accounts’ custom language models and all the enhancements this blog shares are private and are not shared between accounts.

In the following sections I

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18

Jul

Silo busting 2.0—Multi-protocol access for Azure Data Lake Storage

Cloud data lakes solve a foundational problem for big data analytics—providing secure, scalable storage for data that traditionally lives in separate data silos. Data lakes were designed from the start to break down data barriers and jump start big data analytics efforts. However, a final “silo busting” frontier remained, enabling multiple data access methods for all data—structured, semi-structured, and unstructured—that lives in the data lake.

Providing multiple data access points to shared data sets allow tools and data applications to interact with the data in their most natural way. Additionally, this allows your data lake to benefit from the tools and frameworks built for a wide variety of ecosystems. For example, you may ingest your data via an object storage API, process the data using the Hadoop Distributed File System (HDFS) API, and then ingest the transformed data using an object storage API into a data warehouse.

Single storage solution for every scenario

We are very excited to announce the preview of multi-protocol access for Azure Data Lake Storage! Azure Data Lake Storage is a unique cloud storage solution for analytics that offers multi-protocol access to the same data. Multi-protocol access to the same data, via Azure Blob storage API

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18

Jul

Making it easier to bring your Linux based web apps to Azure App Service

Application development has radically changed over the years. From having to host all the physical hardware hosting the app and its dependences on-premises, to moving to a model where the hardware is hosted by external companies yet still managed by the users on to hosting your apps on a fully managed platform where all hardware and software management is done by the hosting provider. And then finally over to a full serverless solution where no resources need to be set up to run applications.

The perception of complexity in running smaller solutions in the cloud are slowly being eradicated due to moving solutions to a managed platform, where even non-technical audiences can manage their application in the cloud.

A great example in the managed platform realm is Azure App Service. Azure App Service provides an easy way to bring source code or containers and deploy full web apps in minutes, with the ease of configuration settings at the hands of the app owner. Built in features such as secure sockets layer (SSL) certificates, custom domains, auto-scaling, setting up a continuous integration and deployment (CI/CD) pipeline, diagnostics, troubleshooting, and much more, provides a powerful platform for full cycle build and

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18

Jul

Conversational AI updates for July 2019

At Build, we highlighted a few customers who are building conversational experiences using the Bot Framework to transform their customer experiences. For example, BMW discussed its work on the BMW Intelligent Personal Assistant to deliver conversational experiences across multiple canvases by leveraging the Bot Framework and Cognitive Services. LaLiga built their own virtual assistant which allows fans to experience and interact with LaLiga across multiple platforms.

With the Bot Framework release in July, we are happy to share new releases of Bot Framework SDK 4.5 and preview of 4.6, updates to our developer tools, and new channels in Azure Bot Service. We’ll use the opportunity to provide additional updates for the Conversational AI releases from Microsoft.

Bot Framework channels

We continue to expend channels support and functionality for Bot Framework and Azure Bot Service.

Voice-first bot applications: Direct Line Speech preview

The Microsoft Bot Framework lets you connect with your users wherever your users are. We offer thirteen supported channels, including popular messaging apps like Skype, Microsoft Teams, Slack, Facebook Messenger, Telegram, Kik, as well as a growing number of community adapters.

Today, we are happy to share the preview of Direct Line Speech channel. This is a new channel

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18

Jul

Azure Monitor for containers with Prometheus now in preview

Prometheus is a popular open source metric monitoring solution and is a part of Cloud Native Compute Foundation. We have many customers who like the extensive metrics which Prometheus provides on Kubernetes. However, they also like how easy it is to use Azure Monitor for containers which provides fully managed, out of the box monitoring for Azure Kubernetes Service (AKS) clusters. We have been receiving requests to funnel the Prometheus data into Azure Monitor and today, we are excited to share Prometheus integration with Azure Monitor for containers is now in preview and brings together the best of two worlds.

Typically, to use Prometheus you need to setup and manage a Prometheus server with a database. With the Azure Monitor integration, no Prometheus server is needed. You just need to expose the Prometheus end-point through your exporters or pods (application), and the containerized agent for Azure Monitor for containers can scrape the metrics for you. We have provided a seamless onboarding experience to collect Prometheus metrics with Azure Monitor. The example below shows how the coredns metrics, which is part of the kube-dns-metric, is collected into Azure Monitor for logs. 

You can also collect workload metrics from your

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18

Jul

Azure Marketplace new offers – Volume 41

https://azure.microsoft.com/blog/azure-marketplace-new-offers-volume-41/We continue to expand the Azure Marketplace ecosystem. For this volume, 109 new offers successfully met the onboarding criteria and went live. See details of the new offers below: Applications Active Directory Domain Controller 2019: This virtual machine comes pre-loaded READ MORE

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