Explanation
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.
Reference: https://docs.microsoft.com/en-us/power-query/querydiagnosticsfolding

Users indicate that the data in Power BI reports is stale. You discover that the refresh process of the Power BI model occasionally times out.
With Query Diagnostics, you can achieve a better understanding of what Power Query is doing at authoring and at refresh time in Power BI Desktop. While we'll be expanding on this feature in the future, including adding the ability to use it during full refreshes, at this time you can use it to understand what sort of queries you're emitting, what slowdowns you might run into during authoring refresh, and what kind of background events are happening.

Explanation
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.
Reference: https://docs.microsoft.com/en-us/power-query/querydiagnosticsfolding

Explanation
Box 1: 3
Power Query doesn't start at the first query and work down, it starts at the bottom (last) query and works backwards, so 3 tables from 1 will cause it to process that first source table 3 times.
Box 2: Using Table.Buffer in the Orders query
Table.Buffer buffers a table in memory, isolating it from external changes during evaluation. Buffering is shallow. It forces the evaluation of any scalar cell values, but leaves non-scalar values (records, lists, tables, and so on) as-is.
Note that using this function might or might not make your queries run faster. In some cases, it can make your queries run more slowly due to the added cost of reading all the data and storing it in memory, as well as the fact that buffering prevents downstream folding.
Example 1
Load all the rows of a SQL table into memory, so that any downstream operations will no longer be able to query the SQL server.
Usage
let
Source = Sql.Database("SomeSQLServer", "MyDb"),
MyTable = Source{[Item="MyTable"]}[Data],
BufferMyTable = Table.Buffer(dbo_MyTable)
in
BufferMyTable
Output
table
Reference: https://radacad.com/performance-tip-for-power-bi-enable-load-sucks-memory-up
https://docs.microsoft.com/en-us/powerquery-m/table-buffer

Explanation
Build a new dataset in the FinData workspace by using data from the Synapse Analytics dedicated SQL pool.
Provide all the customers with their own Power BI workspace to create their own reports. Each workspace will use the new dataset in the FinData workspace Reference: https://docs.microsoft.com/en-us/power-bi/connect-data/service-datasets-admin-across-workspaces
Topic 2, Litware, Inc. Overview
Litware, Inc. is a retail company that sells outdoor recreational goods and accessories. The company sells goods both online and at its stores located in six countries.
Azure Resources
Litware has the following Azure resources:
* An Azure Synapse Analytics workspace named synapseworkspace1
* An Azure Data Lake Storage Gen2 account named datalake1 that is associated with synapseworkspace1
* A Synapse Analytics dedicated SQL pool named SQLDW
Dedicated SQL Pool
SQLDW contains a dimensional model that contains the following table.

SQLDW contains the following additional tables.

SQLDW contains a view named dbo.CustomerPurchases that creates a distinct list of values from dbo.Customer [customeriD], dbo.Customer
[CustomerEmail], dbo.ProductfProductID] and dbo.Product[ProductName].
The sales data in SQLDW is updated every 30 minutes. Records in dbo.SalesTransactions are updated in SQLDW up to three days after being created. The records do NOT change after three days.
Power BI
Litware has a new Power Bl tenant that contains an empty workspace named Sales Analytics.
All users have Power B1 Premium per user licenses.
IT data analysts are workspace administrators. The IT data analysts will create datasets and reports.
A single imported dataset will be created to support the company's sales analytics goals. The dataset will be refreshed every 30 minutes.
Analytics Goals
Litware identifies the following analytics goals:
* Provide historical reporting of sales by product and channel over time.
* Allow sales managers to perform ad hoc sales reporting with minimal effort.
* Perform market basket analysis to understand which products are commonly purchased in the same transaction.
* Identify which customers should receive promotional emails based on their likelihood of purchasing promoted products.
Litware plans to monitor the adoption of Power Bl reports over time. The company wants custom Power Bl usage reporting that includes the percent change of users that view reports in the Sales Analytics workspace each month.
Security Requirements
Litware identifies the following security requirements for the analytics environment:
* All the users in the sales department and the marketing department must be able to see Power B1 reports that contain market basket analysis and data about which customers are likely to purchase a product.
* Customer contact data in SQLDW and the Power B1 dataset must be labeled as Sensitive. Records must be kept of any users that use the sensitive data.
* Sales associates must be prevented from seeing the CustomerEmail column in Power B1 reports.
* Sales managers must be prevented from modifying reports created by other users.
Development Process Requirements
Litware identifies the following development process requirements:
* SQLDW and datalake1 will act as the development environment. Once feature development is complete, all entities in synapseworkspace1 will be promoted to a test workspace, and then to a production workspace.
* Power Bl content must be deployed to test and production by using deployment pipelines.
* All SQL scripts must be stored in Azure Repos.
The IT data analysts prefer to build Power Bl reports in Synapse Studio.