Learn how to scale queries using Streaming Units and partitions in this tutorial by Krishnaswamy Venkataraman, a big data and cloud architect.
In a traditional static data scenario, a query can be executed against a fixed data set and results will be available after a known interval. On the other hand, with a streaming data scenario involving constant changes to a dataset, the queries will run for a longer duration or might not even complete.
Additionally, a constant stream of data will increase the volume of the data, and the query might drain the working memory. One way to draw a data boundary is through the context of time. For example, with a streaming dataset, you can specify a data boundary that resides within the start and end time. This will restrict the query execution between known boundaries. There are two types of timing constraints you can use to set time boundaries for the streaming data, application and arrival time.
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