https://azure.microsoft.com/blog/hdinsight-support-in-azure-cli-now-out-of-preview/We are pleased to share that support for HDInsight in Azure CLI is now generally available. The addition of the az hdinsight command group allows you to easily manage your HDInsight clusters using simple commands while taking advantage of all READ MORE
https://azure.microsoft.com/blog/monitoring-on-azure-hdinsight-part-4-workload-metrics-and-logs/This is the fourth blog post in a four-part series on monitoring on Azure HDInsight. Monitoring on Azure HDInsight Part 1: An Overview discusses the three main monitoring categories: cluster health and availability, resource utilization and performance, and job status and READ MORE
Today we’re previewing the Azure HPC Cache service, a new Azure offering that empowers organizations to more easily run large, complex high-performance computing (HPC) workloads in Azure. Azure HPC Cache reduces latency for applications where data may be tethered to existing data center infrastructure because of dataset sizes and operational scale.
Scale your HPC pipeline using data stored on-premises or in Azure. Azure HPC Cache delivers the performant data access you need to be able to run your most demanding, file-based HPC workloads in Azure, without moving petabytes of data, writing new code, or modifying existing applications.
For users familiar with the Avere vFXT for Azure application available through the Microsoft Azure Marketplace, Azure HPC Cache offers similar functionality in a more seamless experience—meaning even easier data access and simpler management via the Azure Portal and API tools. The service can be driven with Azure APIs and is proactively monitored on the back end by the Azure HPC Cache support team and maintained by Azure service engineers. What is the net benefit? The Azure HPC Cache service delivers all the performance benefits of the Avere vFXT caching technology at an even lower total cost of ownership.
Azure HPC Cache works
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
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
Azure Stream Analytics is a fully managed PaaS offering that enables real-time analytics and complex event processing on fast moving data streams. Thanks to zero-code integration with over 15 Azure services, developers and data engineers can easily build complex pipelines for hot-path analytics within a few minutes. Today, at Inspire, we are announcing various new innovations in Stream Analytics that help further reduce time to value for solutions that are powered by real-time insights. These are as follows:
Bringing the power of real-time insights to Azure Event Hubs customers
Today, we are announcing one-click integration with Event Hubs. Available as a public preview feature, this allows an Event Hubs customer to visualize incoming data and start to write a Stream Analytics query with one click from the Event Hub portal. Once the query is ready, they will be able to operationalize it in few clicks and start deriving real time insights. This will significantly reduce the time and cost to develop real-time analytics solutions.
One-click integration between Event Hubs and Azure Stream Analytics
Augmenting streaming data with SQL reference data support
Reference data is a static or slow changing dataset used to augment real-time data streams to deliver more
Most modern-day businesses employ analytics pipelines for real-time and batch processing. A common characteristic of these pipelines is that data arrives at irregular intervals from diverse sources. This adds complexity in terms of having to orchestrate the pipeline such that data gets processed in a timely fashion.
The answer to these challenges lies in coming up with a decoupled event-driven pipeline using serverless components that responds to changes in data as they occur.
An integral part of any analytics pipeline is the data lake. Azure Data Lake Storage Gen2 provides secure, cost effective, and scalable storage for the structured, semi-structured, and unstructured data arriving from diverse sources. Azure Data Lake Storage Gen2’s performance, global availability, and partner ecosystem make it the platform of choice for analytics customers and partners around the world. Next comes the event processing aspect. With Azure Event Grid, a fully managed event routing service, Azure Functions, a serverless compute engine, and Azure Logic Apps, a serverless workflow orchestration engine, it is easy to perform event-based processing and workflows responding to the events in real-time.
Today, we’re very excited to announce that Azure Data Lake Storage Gen2 integration with Azure Event Grid is in preview! This means
https://azure.microsoft.com/blog/monitoring-on-azure-hdinsight-part-3-performance-and-resource-utilization/This is the third blog post in a four-part series on Monitoring on Azure HDInsight. Part 1 is an overview that discusses the three main monitoring categories: cluster health and availability, resource utilization and performance, and job status and logs. READ MORE
There is a lot more data in the world than can possibly be captured with even the most robust, cutting-edge technology. Edge computing and the Internet of Things (IoT) are just two examples of technologies increasing the volume of useful data. There is so much data being created that the current telecom infrastructure will struggle to transport it and even the cloud may become strained to store it. Despite the advent of 5G in telecom, and the rapid growth of cloud storage, data growth will continue to outpace the capacities of both infrastructures. One solution is to build stateful, data-driven applications with technology from SWIM.AI.
The Azure platform offers a wealth of services for partners to enhance, extend, and build industry solutions. Here we describe how one Microsoft partner uses Azure to solve a unique problem.
Shared awareness and communications
The increase in volume has other consequences, especially when IoT devices must be aware of each other and communicate shared information. Peer-to-peer (P2P) communications between IoT assets can overwhelm a network and impair performance. Smart grids are an example of how sensors or electric meters are networked across a distribution grid to improve the overall reliability and cost of delivering
We are excited to announce new capabilities which are apart of time-series forecasting in Azure Machine Learning service. We launched preview of forecasting in December 2018, and we have been excited with the strong customer interest. We listened to our customers and appreciate all the feedback. Your responses helped us reach this milestone. Thank you.
Building forecasts is an integral part of any business, whether it’s revenue, inventory, sales, or customer demand. Building machine learning models is time-consuming and complex with many factors to consider, such as iterating through algorithms, tuning your hyperparameters and feature engineering. These choices multiply with time series data, with additional considerations of trends, seasonality, holidays and effectively splitting training data.
Forecasting within automated machine learning (ML) now includes new capabilities that improve the accuracy and performance of our recommended models:
New forecast function Rolling-origin cross validation Configurable Lags Rolling window aggregate features Holiday detection and featurization Expanded forecast function
We are introducing a new way to retrieve prediction values for the forecast task type. When dealing with time series data, several distinct scenarios arise at prediction time that require more careful consideration. For example, are you able to re-train the model for each forecast?