To effectively use the massively parallel processing (MPP) architecture with a SQL pool, you need to understand table geometries. Table geometries specify how data is sharded into distributions on your existing compute nodes. These table…
Anyone who has worked with computers, even for a short amount of time, knows that unexpected events happen. You can track this down all the way to the 3.3V of electricity required to flip a…
There are two primary reasons to implement partitioning: manageability and performance. From a manageability perspective, your tables are organized into a structure that makes their content discoverable. That makes it possible to deduce the content…
The persistent batching, incremental loading, streaming, inserting, or updating of data increases the amount of data over time. If you want to move your in‐house data consisting of many gigabytes, terabytes, or petabytes of data…
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,…
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…
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…
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…
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…
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…