Important to mention is that, although query objects are faster then Page objects, especially in reporting scenarios using Power BI or Excel with Power Query, they can still get slow. And remember, you still require a join in the Power Query editor. Yes, it would be faster then explained here above importing the ODATA-pages, but it’s still a lot slower compared to using a query. If you copy the URL of the two queries generated with the Setup Reporting Data wizard, then there are filters in the ODATA-URL, so why not use those, wouldn’t that be faster? Now I can hear you thinking, wait a second. Queries are also much easier to maintain, you manage them in your extension, your extension is linked to git and/or DevOps so you can also have some SCM on your query objects, instead of creating them directly in the application using some kind of wizard that generates stuff that’s not really performant…Īnd, even your Power BI report files can be managed in GIT, DevOps. It’s much easier in Power BI, it’s much faster to retrieve and to refresh the data. We have the same result, but much faster. Now publish your query and import it in Power BI: This is a much faster and less expensive way to get the data from the SQL database. In this query I also fetch the required data from the Sales Shipment Header and Line table, but when it executes only the columns will be fetched from the SQL database, no SELECT *, AND because of the totaling method (Sum), a GROUP BY clause will be added to the SQL statement, aggregating the data with a subtotal of Quantity by Item, Posting Date and Customer. Yes we can, by replacing the two page datasets with one query dataset. When working with demo data you will not notice this, but in real life, with real data, you will have to wait some time for the report to refresh. The sources are ODATA page objects, SELECT * queries will be sent to the SQL database and an expensive load & join in Power Query will not make the report very fast. Not so very optimal performance wise if you ask me. Then you can disable load for the two original datasets, since we only need the merged one:Īs you can imagine, every time you refresh the report (manually or scheduled) both queries will be executed, joined and the joined data will be loaded again to the report. Imho it’s better to do this asap, so in the Power Query editor. Now you can choose to join the two datasets in the Power Query editor, or import both datasets and libnk them in the relationships window of the Power BI report. Select the Transform Data button to open the Query Editor: fields, so you can join the two datasets later.) Now Power BI Desktop can find these datasets using the Business Central connector:Īs you can see, both datasets are here, but the Power BI connector lists all fields from the source pages and does not apply any filters. When both datasets are created and published you can find them in the web services page: Then we will create a dataset for the lines:Īnd let’s filter the lines on only items: First we will create a dataset for the Header: For more information on how this works, click here.ġ8 U.S.C.Let’s now use the Setup Reporting Data wizard to create this dataset. As a member of Bicurious Chat City, your profile will automatically be shown on related bisexual chat sites or to related users in the network at no additional charge. Further, all members of this dating site MUST be 18 years or older.īicurious Chat City is part of the chat network, which includes many other general and bisexual chat sites. Charges will accrue if you purchase a premium membership which is offered upon completion of your profile.Īll members and/or models displayed on this website were 18 years or older at the time the image was submitted to this web property in accordance with federal laws. Disclaimer: 100% Free basic membership allows you to browse the site, view profiles, send flirts and modify your profile.
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