But the concept of flattening arrays, and Upsolver’s automated method of addressing it, spans industries and use cases. In the following specific example we work with retail store sales data, which the data team wants to analyze to determine the company’s best-performing items and stores. When you do this Upsolver automatically flattens the arrays out by line item. sending it to Athena for data analysts to investigate and derive business insights.ingesting nested data that contains arrays,.Here’s an example of how straightforward this can be using Upsolver: That includes calculations on fields originating from arrays in this case Upsolver uses the target field name of calculations to determine how to proceed. It removes the need for manual review and intervention, automating all of the steps involved and also handling all behind-the-scenes processing. ![]() What’s needed is a robust default handling behavior for arrays. If you lack good visibility into the data coming in, you may not even know they’re there. And in any case, if you’re working with multiple streaming data sources, whether from document DBs, event streams, services such as Kafka or Kinesis, and so on, there’ll likely be complex objects – arrays with items, classes, categories, and so on – in there somewhere. Some data producers who are beyond your direct control may provide data with arrays. Some of your producers who provide data with arrays may be beyond your direct control. Nor can you avoid dealing with these objects. You can do it manually with code – identify the object, understand its syntax, possibly relate it to other arrays, write the code for it, then test it out. But it’s difficult to use SQL to process this data such that it’s fully available to query engines. Otherwise, the system could mix up values and return incorrect results.įlattening the data maximizes its usability.
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