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BUILT‐IN FUNCTIONS – Data Sources and Ingestion

BUILT‐IN FUNCTIONS – Data Sources and Ingestion

As shown in Table 3.20, the built‐in functions are further categorized into groups such as aggregate, analytic, conversion, date, mathematical, and windowing. TABLE 3.20 Azure Stream Analytics built‐in functions Type Functions Aggregate AVG, COUNT, MIN,…

  • by Charlene hill
  • Posted on 30/11/202305/08/2024
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Create an Azure Stream Analytics Job – Data Sources and Ingestion

Create an Azure Stream Analytics Job – Data Sources and Ingestion

FIGUER 3.78 Provisioning an Azure Stream Analytics job Consider testing the connection from the Azure Stream Analytics job and the event hub by pressing the Test menu option on the Input blade. The configuration of…

  • by Charlene hill
  • Posted on 19/10/202305/08/2024
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Upserts and Deletes – Data Sources and Ingestion

Upserts and Deletes – Data Sources and Ingestion

The example of combining multiple data files into a single table for analysis in Exercise 3.15 simply appended one file onto the next. This required you to remove and re‐create the table. Instead of dropping…

  • by Charlene hill
  • Posted on 17/09/202305/08/2024
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Streaming and Batch Unification – Data Sources and Ingestion

Streaming and Batch Unification – Data Sources and Ingestion

Streaming, batching, and querying data are all very performant and reliable activities when using delta tables. This means if your data analytics solution requires streaming or batching, the approach for ingesting, transforming, and serving is…

  • by Charlene hill
  • Posted on 27/05/202305/08/2024
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Create an Azure Event Namespace and Hub – Data Sources and Ingestion

Create an Azure Event Namespace and Hub – Data Sources and Ingestion

It is possible to create an additional policy with fewer rights. The RootManageSharedAccessKey policy has full access. You might consider creating a policy with the minimum access necessary, perhaps Send Only. It depends on your…

  • by Charlene hill
  • Posted on 26/04/202305/08/2024
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Job Topology – Data Sources and Ingestion

Job Topology – Data Sources and Ingestion

The word topology is common in IT. The concept is often experienced in the context of a network topology. It is simply the arrangement of interrelated parts that constitute the whole. The same definition can…

  • by Charlene hill
  • Posted on 16/03/202305/08/2024
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SLIDING WINDOW – Data Sources and Ingestion

SLIDING WINDOW – Data Sources and Ingestion

A sliding window is illustrated in Figure 3.82 and implemented using the following SQL snippet: SELECT READINGTYPE, COUNT(*) as CountFROM brainwaves TIMESTAMP BY CreatedAtGROUP BY READINGTYPE, SlidingWindow(second, 10)HAVING COUNT(*)> 3 The second parameter represents the…

  • by Charlene hill
  • Posted on 14/02/202305/08/2024
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TIME MANAGEMENT – Data Sources and Ingestion

TIME MANAGEMENT – Data Sources and Ingestion

There are many scenarios where the timestamp of a message/event is critical to the success of an application or business process. Consider a banking transaction that applies credits and debits to an account in the…

  • by Charlene hill
  • Posted on 13/01/202305/08/2024
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Data Ingestion – Data Sources and Ingestion

Data Ingestion – Data Sources and Ingestion

Figure 3.75 shows how data is ingested into Azure Databricks. There are numerous ways to ingest data, including by streaming via Event Hubs, Stream Analytics, or Kafka, or by copying data from a remote source…

  • by Charlene hill
  • Posted on 23/12/202205/08/2024
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USERS – Data Sources and Ingestion

USERS – Data Sources and Ingestion

The Users page provides a list of users who have access to the workspace. The role and permissions of each user are rendered as well. Table 3.18 describes the permissions (i.e., entitlements). TABLE 3.18 Azure…

  • by Charlene hill
  • Posted on 14/06/202205/08/2024
  • Leave a comment on USERS – Data Sources and Ingestion
  • Create an Azure Stream Analytics Job
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