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This is how the OnlineAnalyticalProcessing (OLAP) cube was born, which you might call one of the grooviest BI inventions developed in the 70s. It’s a snapshot of data at a specific point in time, at the end of a day, week, month or year. Saving time and headaches with onlineanalyticalprocessing tool.
Deriving business insights by identifying year-on-year sales growth is an example of an onlineanalyticalprocessing (OLAP) query. Download the following files to perform the ELT: The Python script to load sample data into our DynamoDB table: load_dynamodb.py. These types of queries are suited for a data warehouse.
The following figure shows a daily query volume snapshot (queries per day and queued queries per day, which waited a minimum of 5 seconds). To identify percentile and top running queries, you can download the sample SQL notebook system queries. The data warehouse is highly business critical with minimal allowable downtime.
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