Microsoft DP-500 Dumps

Understanding folding with Query Diagnostics

One of the most common reasons to use Query Diagnostics is to have a better understanding of what operations were 'pushed down' by Power Query to be performed by the back-end data source, which is also known as 'folding'. If we want to see what folded, we can look at what is the 'most specific' query, or queries, that get sent to the back-end data source. We can look at this for both ODATA and SQL.

Using the compute engine to improve performance

Take the following steps to enable workloads trigger the compute engine, and always improve performance:

For computed and linked entities in the same workspace:

Ensure you perform the operations that fold, such as merges, joins, conversion, and others.

For ingestion focus on getting the data into the storage as fast as possible, using filters only if they reduce the overall dataset size. It's best practice to keep your transformation logic separate from this step, and allow the engine to focus on the initial gathering of ingredients. Next, separate your transformation and business logic into a separate dataflow in the same workspace, using linked or computed entities; doing so allows for the engine to activate and accelerate your computations. In our analogy, it's like food preparation in the kitchen: food preparation is typically a separate and distinct step from gathering your raw ingredients, and a pre-requisite for putting the food in the oven. Similarly, your logic needs to be prepared separately before it can take advantage of the compute engine.

Box 1: pairplot

A pairs plot allows us to see both distribution of single variables and relationships between two variables. Pair plots are a great method to identify trends for follow-up analysis and, fortunately, are easily implemented in Python!

Example, let’s plot data using pairplot:

From the picture below, we can observe the variations in each plot. The plots are in matrix format where the row name represents x axis and column name represents the y axis. The main-diagonal subplots are the univariate histograms (distributions) for each attribute.

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Box 2: sepal_width

sepal_width is displayed with a height of 2.5 (between 2.0 and 4.5).