Re: Pro Real Time Que Es
Vida Hubbert
Real-Time Intelligence is a powerful service that empowers everyone inyour organization to extract insights and visualize their data inmotion. It offers an end-to-end solution for event-driven scenarios,streaming data, and data logs. Whether dealing with gigabytes orpetabytes, all organizational data in motion converges in the Real-TimeHub. It seamlessly connects time-based data from various sources usingno-code connectors, enabling immediate visual insights, geospatialanalysis, and trigger-based reactions that are all part of anorganization-wide data catalog.
Once you seamlessly connect any stream of data, the entire SaaS solutionbecomes accessible. Real-Time Intelligence handles data ingestion,transformation, storage, analytics, visualization, tracking, AI, andreal-time actions. Your data remains protected, governed, and integratedacross your organization, seamlessly aligning with all Fabric offerings.Real-Time Intelligence transforms your data into a dynamic, actionableresource that drives value across the entire organization.
Real-Time Intelligence can be used for data analysis, immediate visualinsights, centralization of data in motion for an organization, actionson data, efficient querying, transformation, and storage of largevolumes of structured or unstructured data. Whether you need to evaluatedata from IoT systems, system logs, free text, semi structured data, orcontribute data for consumption by others in your organization,Real-Time Intelligence provides a versatile solution.
Even though it's called "real-time," your data doesn't have to beflowing at high rates and volumes. Real-Time Intelligence gives youevent-driven, rather than schedule-driven solutions. The Real-TimeIntelligence components are built on trusted, core Microsoft services,and together they extend the overall Fabric capabilities to provideevent-driven solutions.
The Real-Time hub serves as a centralized catalog within your organization. It facilitates easy access, addition, exploration, and data sharing. By expanding the range of data sources, it enables broader insights and visual clarity across various domains. Importantly, this hub ensures that data is not only available but also accessible to all, promoting quick decision-making and informed action. The sharing of streaming data from diverse sources unlocks the potential to build comprehensive business intelligence across your organization.
Once you select a stream from your organization or connected tooutside or internal sources, you can use the data consumption toolsin Real-Time Intelligence to explore your data. The data consumptiontools use visual data exploration process and drill down on datainsights. You can access data that's new to you and easilyunderstand the data structure, patterns, anomalies, forecastingquantities, and data rates. Accordingly, you can act or make smart decision based on the data. Real-Time dashboards come equipped with out-of-the-boxinteractions that simplify the process of understanding data, makingit accessible to anyone who wants to make decision based on data inmotion using visual tools, Natural Language and Copilot.
Microsoft sources: Easily discover streaming sources that youhave and quickly configure ingestion of those sources into Fabric,for example: Azure Event Hubs, Azure IoT Hub, Azure SQL DB ChangeData Capture (CDC), Azure Cosmos DB CDC, PostgreSQL DB CDC.
Fabric events: Event-driven capabilities support real-time notifications and data processing. You can monitor and react to events including Fabric Workspace Item events and Azure Blob Storage events. These events can be used to trigger other actions or workflows, such as invoking a data pipeline or sending a notification via email. You can also send these events to other destinations via Event streams.
Event streams are the Fabric platform way to capture, transform, androute high volumes of real-time events to various destinations with ano-code experience. Event streams support multiple data sources and datadestinations, including a wide range of connectors to external sources,for example: Apache Kafka clusters, database change data capture feeds,AWS streaming sources (Kinesis), and Google (GCP Pub/Sub).
By using the event processing capabilities in Event streams, you can dofiltering, data cleansing, transformation, windowed aggregations, anddupe detection, to land the data in the shape you want. You can also usethe content-based routing capabilities to send data to differentdestinations based on filters. Another feature, derived event streams,lets you construct new streams as a result of transformations and/oraggregations that can be shared to consumers in Real-Time hub.
Eventhouses are the ideal analytics engine to process data in motion. They're tailored to time-based, streaming events with structured, semi structured, and unstructured data. This data isautomatically indexed and partitioned based on ingestion time, giving you incredibly fast and complex analytic querying capabilities on high-granularity data. Data stored in eventhouses can be made available in OneLake for consumption by other Fabric experiences.
The indexed, partitioned data stored in eventhouses is ready forlightning-fast query using various code, low-code, or no-codeoptions in Fabric. Data can be queried in native KQL (Kusto QueryLanguage) or using T-SQL in the KQL queryset. The Kusto copilot, alongwith the no-code query exploration experience, streamlines the processof analyzing data for both experienced KQL users and citizen datascientists. KQL is a simple, yet powerful language to query structured,semi-structured, and unstructured data. The language is expressive, easyto read and understand the query intent, and optimized for authoringexperiences.
These data insights can be visualized in KQL querysets, Real-Timedashboards and Power BI reports, with seconds from data ingestion toinsights. Visualization options range from no-code to fully specializedexperiences, giving value to both the novice and expert insightsexplorer to visualize their data as charts and tables. You can usevisual cues to perform filtering and aggregation operations on queryresults and using a rich list of built-in visualizations. These insightscan be viewed in Power BI Reports and Real-Time Dashboards, both ofwhich can have alerts built upon the data insights.
Alerts monitor changing data and automatically take actions whenpatterns or conditions are detected. The data can be flowing inReal-Time hub, or observed from a Kusto query or Power BI report. Whencertain conditions or logic is met, an action is then taken, such asalerting users, executing Fabric job items like a pipeline, or kickingoff Power Automate workflows. The logic can be either a simply definedthreshold, a pattern such as events happening repeatedly over a timeperiod, or the results of complex logic defined by a KQL query. DataActivator turns your event-driven insights into actionable businessadvantages.
NOAA's PORTS program is a decision support tool that improves the safety and efficiency of maritime commerce and coastal resource management. PORTS collects and disseminates observations of water levels, currents, salinity, bridge air gap and meteorological parameters (e.g., winds, atmospheric pressure, air and water temperatures) that mariners need to navigate safely. The objectives of the PORTS program are to promote navigation safety, improve the efficiency of U.S. ports and harbors, and ensure the protection of coastal marine resources.
Actually, it's not really much of a debate or disagreement. It's more just friendly banter. You see, we can both agree on the intent of the terms 'real-time' vs. 'near real-time'. Processing that happens seemingly instantaneously.
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With CloudFront real-time logs, you can get information about requests made to a distribution in real time (logs are delivered within seconds of receiving the requests). You can use real-time logs to monitor, analyze, and take action based on content delivery performance.
CloudFront real-time logs are delivered to the data stream of your choice in Amazon Kinesis Data Streams. You can build your own Kinesis data stream consumer, or use Amazon Data Firehose to send the log data to Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon OpenSearch Service (OpenSearch Service), or a third-party log processing service.
CloudFront charges for real-time logs, in addition to the charges you incur for using Kinesis Data Streams. For more information about pricing, see Amazon CloudFront Pricing and Amazon Kinesis Data Streams pricing.
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