Azure 時間序列深入解析 Gen2 使用案例Azure Time Series Insights Gen2 use cases

本文摘要說明 Azure 時間序列深入解析 Gen2 的數個常見使用案例。This article summarizes several common use cases for Azure Time Series Insights Gen2. 本文中的建議可作為開始開發您的應用程式和解決方案 Azure 時間序列深入解析 Gen2 的起點。The recommendations in this article serve as a starting point to develop your applications and solutions with Azure Time Series Insights Gen2.

具體而言,本文會回答下列問題:Specifically, this article answers the following questions:

下列各節將說明這些使用案例的總覽。An overview of these use scenarios is described in the following sections.


Azure 時間序列深入解析 Gen2 是端對端的平臺即服務供應專案。Azure Time Series Insights Gen2 is an end-to-end, platform-as-a-service offering. 它可用來收集、處理、儲存、分析及查詢高度內容相關且已進行時間序列最佳化的 IoT 級別資料。It's used to collect, process, store, analyze, and query highly contextualized, time-series-optimized IoT-scale data. 它很適合用於臨機運算元據探索和作業分析。It is ideal for ad-hoc data exploration and operational analysis. Azure 時間序列深入解析 Gen2 是可唯一擴充的自訂服務供應專案,可滿足產業 IoT 部署的廣泛需求。Azure Time Series Insights Gen2 is a uniquely extensible, customized service offering that meets the broad needs of industrial IoT deployments.

資料探索與視覺化的異常偵測Data exploration and visual anomaly detection

立即探索與分析數十億個事件,並找出異常行為及發掘資料背後隱藏的趨勢。Instantly explore and analyze billions of events to spot anomalies and discover hidden trends in your data. Azure 時間序列深入解析 Gen2 可為您的 IoT 和 DevOps 分析工作負載提供近乎即時的效能。Azure Time Series Insights Gen2 delivers near real-time performance for your IoT and DevOps analysis workloads.

資料 explorerData explorer

大部分的客戶都同意取得深入解析所需的最少時間,是 Azure 時間序列深入解析 Gen2 的其中一項最顯著功能:Most customers agree that the minimal amount of time required to gain insight is one of the standout features of Azure Time Series Insights Gen2:

  • Azure 時間序列深入解析 Gen2 不需要預先準備資料。Azure Time Series Insights Gen2 requires no upfront data preparation.
  • 只要幾分鐘的時間,就能快速將您連線到 Azure IoT 中樞中的數十億個事件或 Azure 事件中樞實例。It works fast to connect you to billions of events in your Azure IoT Hub or Azure Event Hubs instances in minutes.
  • 一旦連線,您就可以視覺化與分析數十億個事件,並找出異常行為及發掘資料背後隱藏的趨勢。Once connected, you can visualize and analyze billions of events to spot anomalies and discover hidden trends in your data.

Azure 時間序列深入解析 Gen2 很直覺且容易使用。Azure Time Series Insights Gen2 is intuitive and simple to use. 您不需撰寫任何一行程式碼,即可與您的資料互動。You can interact with your data without writing a single line of code. 您也不需要學習新的語言,雖然 Azure 時間序列深入解析 Gen2 為熟悉 SQL 的 advanced 使用者提供更精細的文字查詢語言。There's also no new language you're required to learn, although Azure Time Series Insights Gen2 provides a granular text-based querying language for advanced users who are familiar with SQL. 它還為新手提供了選取及點選探索。It also provides select-and-click exploration for novices.

客戶可以利用快速診斷資產相關問題的速度。Customers can take advantage of the speed to diagnose asset-related issues quickly. 他們可以執行 DevOps 分析,以取得 IoT 解決方案中錯誤的根本原因。They can perform DevOps analysis to get to the root cause of a bug in an IoT solution. 它們也可以在資料科學計畫中識別要進一步調查的區域。They also can identify areas to flag for further investigation as part of their data science initiatives.

有三種主要方式可與 Azure 時間序列深入解析 Gen2 中儲存的資料互動:There are three primary ways to interact with data stored in Azure Time Series Insights Gen2:

  • 開始使用的第一個和最簡單的方式是使用 Azure 時間序列深入解析 Gen2 Explorer。The first and easiest way to get started is with the Azure Time Series Insights Gen2 Explorer. 您可以使用它在同一個地方,快速地以視覺化方式檢視您所有的 IoT 資料。You can use it to quickly visualize all of your IoT data in one place. 它提供熱度圖之類的工具,可協助您找出資料中的異常。It provides tools like the heat map to help you spot anomalies in your data. 它還提供了透視圖。It also provides a perspective view. 使用它可在單一儀表板中,從一或多個 Azure 時間序列深入解析 Gen2 環境比較最多四個視圖。Use it to compare up to four views from one or more Azure Time Series Insights Gen2 environments in a single dashboard. 儀表板可讓您檢視所有位置的時間序列資料。The dashboard gives you a view of your time-series data across all your locations. 深入瞭解 Azure 時間序列深入解析 Gen2 ExplorerLearn more about the Azure Time Series Insights Gen2 Explorer. 若要規劃您的環境,請閱讀 Azure 時間序列深入解析 Gen2 規劃To plan out your environment, read Azure Time Series Insights Gen2 planning.

  • 第二個開始的方法是使用 JavaScript SDK,在您的 web 應用程式中快速內嵌功能強大的圖表和圖形。The second way to start is to use the JavaScript SDK to quickly embed powerful charts and graphs in your web application. 只需幾行程式碼,您就可以撰寫功能強大的查詢。With just a few lines of code, you can author powerful queries. 您可以使用它們來填入折線圖、圓形圖、橫條圖、熱度圖、資料方格等等。Use them to populate line charts, pie charts, bar charts, heat maps, data grids, and more. 透過使用 SDK,所有這些元素都是立即可用。All of these elements exist out-of-the-box by using the SDK. SDK 也會將 Azure 時間序列深入解析 Gen2 查詢 Api 抽象化。The SDK also abstracts Azure Time Series Insights Gen2 query APIs. 您可以使用它們來撰寫類似 SQL 的述詞,以查詢要在儀表板上顯示的資料。You can use them to author SQL-like predicates to query the data you want to show on a dashboard. 針對混合式展示層解決方案,Azure 時間序列深入解析 Gen2 提供參數化的 Url。For hybrid presentation-layer solutions, Azure Time Series Insights Gen2 offers parameterized URLs. 他們使用 Azure 時間序列深入解析 Gen2 Explorer 提供順暢的連接點,以便深入探討資料。They provide seamless connection points with the Azure Time Series Insights Gen2 Explorer for deep dives into data.

  • 第三個開始的方法是使用強大的 Api 來查詢 Azure 時間序列深入解析 Gen2 中儲存的資料。The third way to start is to use the powerful APIs to query data stored in Azure Time Series Insights Gen2. Azure 時間序列深入解析 Gen2 具有時態性運算子 from ,例如、 tofirstlastAzure Time Series Insights Gen2 has temporal operators such as from, to, first, and last. 它具有匯總和轉換,例如 averagesumminmaxtime-weighted average 、等等 time-weighted sum 。它也允許篩選、算術和布林運算子、純量函數等等。所有這些運算子都可讓下游應用程式快速找出您資料中的有趣趨勢與模式。It has aggregations and transformations such as average, sum, min, max, time-weighted average, time-weighted sum, etc. It also allows filtering, arithmetic and boolean operators, scalar functions, etc. All these operators enable downstream applications to quickly find interesting trends and patterns in your data. 您可以使用它們來填入自主視覺效果,以找出異常狀況。Use them to populate homegrown visualizations to spot anomalies.

營運分析與推動流程效率Operational analysis and driving process efficiency

使用 Azure 時間序列深入解析 Gen2 來大規模監視設備的健康情況、使用量和效能,以及測量營運效率。Use Azure Time Series Insights Gen2 to monitor the health, usage, and performance of equipment at scale and measure operational efficiency. Azure 時間序列深入解析 Gen2 可協助管理各種和無法預測的 IoT 工作負載,而不會犧牲內嵌或查詢的效能。Azure Time Series Insights Gen2 helps manage diverse and unpredictable IoT workloads without sacrificing ingestion or query performance.

螢幕擷取畫面顯示 Azure 時間序列深入解析 Gen2 中的 T 裝置/應用程式資料、串流處理、作業效率、智慧/見解和先進分析。Screenshot shows I o T devices / application data, stream processing, operational efficiency, intelligence / insights, and advanced analytics in Azure Time Series Insights Gen2.

如果與正確的技術或解決方案相結合,來自營運流程之資料的串流和連續處理,可以成功地轉換任何業務。Streaming and continuous processing of data coming from operational processes can successfully transform any business if it's combined with the right technology or solution. 通常這些解決方案是多個系統的組合。Often these solutions are a combination of multiple systems. 它們可讓您探索及分析不斷變更的資料(尤其是在 IoT 領域中),並共用一般模式。They enable exploration and analysis of data that changes constantly, especially in the IoT realm, and share a common pattern.

這些模式通常會從啟用 IoT 的平台開始,這些平台從跨越各種地區設定的裝置和感應器中內嵌數十億個事件。These patterns often start with IoT-enabled platforms that ingest billions of events from devices and sensors that span various locales. 這些系統會處理並分析資料流資料,以衍生即時的資訊分析與動作。These systems process and analyze streaming data to derive real-time insights and actions. 資料通常會封存至暖和冷存放區,以進行近乎即時和批次分析。Data is typically archived to warm and cold store for near real-time and batch analytics.

收集的資料會經過一連串的處理,以便針對下游查詢和分析案例來清理資料並加以語境化。Data that's collected goes through a series of processing to cleanse and contextualize it for downstream querying and analytics scenarios. Azure 提供豐富的服務,可套用至像是資產維護和製造等 IoT 案例。Azure offers rich services that can be applied to IoT scenarios such as asset maintenance and manufacturing. 這些服務包括 Azure 時間序列深入解析 Gen2、IoT 中樞、事件中樞、Azure 串流分析、Azure Functions、Azure Logic Apps、Azure Databricks、Azure Machine Learning 和 Power BI。These services include Azure Time Series Insights Gen2, IoT Hub, Event Hubs, Azure Stream Analytics, Azure Functions, Azure Logic Apps, Azure Databricks, Azure Machine Learning, and Power BI.

解決方案架構可以透過下列方式來達成:Solution architecture can be achieved in the following manner:

  • 透過 IoT 中樞或事件中樞內嵌資料,以獲得最佳的安全性、輸送量和延遲。Ingest data via IoT Hub or Event Hubs for best-in-class security, throughput, and latency.
  • 執行資料處理和計算。Perform data processing and computations. 漏斗圖透過 Stream Analytics、Logic Apps 和 Azure Functions 等服務內嵌數據。Funnel ingested data through services such as Stream Analytics, Logic Apps, and Azure Functions. 您所使用的服務取決於特定的資料處理需求。The service you use depends on the specific data-processing needs.
  • 處理管線的計算信號會推送至 Azure 時間序列深入解析 Gen2 以進行儲存和分析。Computed signals from the processing pipeline are pushed to Azure Time Series Insights Gen2 for storing and analytics.

Azure 時間序列深入解析 Gen2 透過歷程記錄資料,提供近乎即時的資料探索和資產型見解。Azure Time Series Insights Gen2 offers near real-time data exploration and asset-based insights over historical data. 根據您的商務需求,MapReduce 和 Hive 作業可透過將 Azure 時間序列深入解析 Gen2 連接至 Azure HDInsight,在儲存于 Azure 時間序列深入解析 Gen2 中的資料上執行。Depending on your business needs, MapReduce and Hive jobs can run on data stored in Azure Time Series Insights Gen2 by connecting Azure Time Series Insights Gen2 to Azure HDInsight. Azure 時間序列深入解析 Gen2 中儲存的資料,可透過 Azure 時間序列深入解析 Gen2 公用介面查詢 Api,供 Power BI 和其他客戶應用程式使用。Data stored in Azure Time Series Insights Gen2 is available to Power BI and other customer applications via the Azure Time Series Insights Gen2 public surface query APIs. 這項資料可用於深入的業務和營運智慧案例。This data can be used for deep business and operational intelligence scenarios.

進階分析Advanced analytics

與 Machine Learning 和 Azure Databricks 這類進階分析服務整合。Integrate with advanced analytics services such as Machine Learning and Azure Databricks. Azure 時間序列深入解析 Gen2 從數百萬部裝置 ingresses 原始資料。Azure Time Series Insights Gen2 ingresses raw data from millions of devices. 它會加入可由 Azure 分析服務套件順暢使用的內容相關資料。It adds contextual data that can be consumed seamlessly by a suite of Azure analytics services.


進階分析和機器學習服務會取用並處理大量的資料。Advanced analytics and machine learning consume and process large volumes of data. 這項資料用來制定資料導向決策,並執行預測性分析。This data is used to make data-driven decisions and perform predictive analysis. 在 IoT 使用案例中,進階分析演算法會從來自數百萬個裝置所收集的資料中學習。In IoT use cases, advanced analytics algorithms learn from the data collected from millions of devices. 這些裝置每秒會傳輸多次資料。These devices transmit data multiple times every second. 從 IoT 裝置所收集的資料是未經處理的。The data collected from IoT devices is raw. 它缺少了內容相關資訊,例如裝置的位置和感應器讀取的單位。It lacks contextual information such as the location of the device and the unit of the sensor reading. 因此,未經處理的資料很難直接供進階分析使用。As a result, raw data is difficult to consume directly for advanced analytics.

Azure 時間序列深入解析 Gen2 能以兩種簡單且符合成本效益的方式,來橋接 IoT 資料和先進分析之間的橋樑:Azure Time Series Insights Gen2 bridges the gap between IoT data and advanced analytics in two simple and cost-effective ways:

  • 首先,Azure 時間序列深入解析 Gen2 會使用 IoT 中樞從數百萬個裝置收集原始遙測資料。First, Azure Time Series Insights Gen2 collects raw telemetry data from millions of devices by using IoT Hub. 它透過內容相關資訊來充實資料,並將資料轉換成 parquet 格式。It enriches data with contextual information and transforms data into a parquet format. 這種格式可以輕鬆地與其他進階分析服務整合,例如機器學習、Azure Databricks 和第三方應用程式。This format can easily integrate with other advanced analytics services, such as Machine Learning, Azure Databricks, and third-party applications.

    Azure 時間序列深入解析 Gen2 可以作為整個組織中所有資料的真實來源。Azure Time Series Insights Gen2 can serve as the source of truth for all data across an organization. 它為下游分析工作負載建立了一個中央儲存機制。It creates a central repository for downstream analytics workloads to consume. 因為 Azure 時間序列深入解析 Gen2 是近乎即時的儲存體服務,所以先進的分析模型可以從傳入的 IoT 遙測資料中持續學習。Because Azure Time Series Insights Gen2 is a near real-time storage service, advanced analytics models can learn continuously from incoming IoT telemetry data. 如此一來,模型可以進行更精確的預測。As a result, the models can make more accurate predictions.

  • 其次,您可以將機器學習和預測模型的輸出送入 Azure 時間序列深入解析 Gen2,以視覺化並儲存其結果。Second, the output of machine learning and prediction models can be fed into Azure Time Series Insights Gen2 to visualize and store their results. 此程序可協助組織最佳化並調整其模型。This procedure helps organizations to optimize and tweak their models. Azure 時間序列深入解析 Gen2 可讓您輕鬆地將相同平面上的串流遙測資料視覺化,如同定型的模型輸出。Azure Time Series Insights Gen2 makes it simple to visualize streaming telemetry data on the same plane as the trained model outputs. 如此一來,它可以協助資料科學小組找出異常狀況並識別模式。In this way, it helps data science teams spot anomalies and identify patterns.

下一步Next steps