When Using direct query, powerBI is saying that splitting a column is not supported. I have a column and I need to split it into many columns. Do I need to use the import data option or is there a workaround. I am not able to use the import option because my table is very very huge.
This is not supported through direct query. Direct query method has a lot of limitations when it comes to manipulation of the source dataset like you cannot create calculated columns or measures, number formatting etc. With Direct query you basically see the source as it is with very little modification possibilities.
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I have a report in Power BI that cannot refresh because the data from the table is too large:
The amount of data on the gateway client has exceeded the limit for a single table. Please consider reducing the use of highly repetitive strings values through normalized keys, removing unused columns, or upgrading to Power BI Premium
I have tried to shrink the columns used in the data set to the best of my ability, but it is still too large to refresh. I did a test where, instead of using just a single query to retrieve the data, I made two queries that split the columns roughly half and half and then link them back together in Power BI using their ID column. It looked to me that the test data refresh started working upon splitting up the table's data into two separate queries.
Please correct me if there is a better method to trim the data down to allow the data set to refresh, but for now this is the best solution I see. What I am wondering is, since now my data is split into two separate queries, what is the best way to adapt the already existing visualizations I have that are linked up to the full, non-refreshable query to the split, refreshable queries? It looks to me like I would have to recreate the visuals from scratch, but if there is a way to simply do a mass replace of the fields that would save so much time. The split queries I created both have the same fields as the non-split query.
When I get data into Power BI I can edit the query as well as perform edit to the model.
What is difference between edit performed in query edit vs during modelling?
When you edit the query, you use Power Query, with its own Query Editor user interface. The steps you apply are recorded in the "M" language. Use Power Query to extract, transform, and finally load data into the Data Model.
Once the data is in the Data Model, you use DAX to create measures that you use in visuals. You can also use DAX to add more columns or even tables to the data model.
Whether to use Power Query or DAX to add columns or tables to the data model depends on a variety of factors. Some things are dead easy to do in Power Query, but harder to achieve with DAX, and vice versa. If you create a column with a formula that depends on a DAX measure, then you can only do that with DAX, because Power Query is not aware of the measures that are created after the load into the data model.
Power Query is very powerful, but the M code syntax is very different to the Excel formula syntax, or the VBA macro language. Learning to write advanced M code can be quite challenging.
DAX, on the other hand, behaves very similar to Excel formulas. Many Excel functions can even be used in DAX verbatim. If you know Excel, you've already got a head start on DAX and you can ease your way into it by learning additional functions and then expanding into more complex formulas.
The latter is probably the reason why many data manipulations are done in DAX, even though they could as well have been done in Power Query.
There are also some efficiencies with data storage and performance. Power Query makes use of query folding with SQL queries, for example, where its transformations are actually performed at the data source, i.e. on the SQL server side, and not in desktop client, and only the final query result is transferred to the desktop client.
Edit after comment: When the data is loaded into the data model, an algorithm processes the data and sorts it in a way that is most efficient for maximum compression and minimum storage. I don't have any concreate examples, but adding a column in Power Query will result in a smaller footprint than adding the same column with DAX. Read more about the compression algorithm VertiPaq here: https://towardsdatascience.com/inside-vertipaq-in-power-bi-compress-for-success-68b888d9d463
But apart from that, it mainly comes down to personal preference based on skill and experience.
By the way, many of your questions can be answered by reading through the Microsoft documentation, e.g. https://learn.microsoft.com/en-us/power-bi/guidance/import-modeling-data-reduction
I have two (2) tables in Power Bi and I wish to bring over several columns from a table that has a column of matching values (there are many columns that I don't need). What is the best way to do so? I tried DAX query but it only allowed for one column to bring over using the LOOKUP function. I tried to merge the queries but didn't quite understand how to get it to work as the table expanded did not match up to the values. Some help please for performing this operation would be greatly appreciated. Thank you.
Don't do it with DAX, use Power Query to merge on the key column. This can all be achieved using the menu and options, no code required. This is the same as in Excel, so here's a walkthrough from Microsoft that goes through all the steps you might need.Once you start using Power Query you won't look back. Good Luck!
I have a PowerBI report that has a few different pages display different visuals. The report uses the same table of data (lets call it Jobs).
The previous author of this report has created two queries in the data section that read off this base table of data, but apply different transformations and filters to the underlying data. Then, the visuals use either of these models to display their data. For example, the first one applies a filter to exclude certain columns based off a status field and the other applies a different filter, and performs transformations on some of the columns
When I manually refresh the report, it looks like the report is retrieving data for both of these queries, even though the base data is the same. Since the dataset is quite large, I am worried that this report has been built inefficiently but I am not sure if there is a better way of doing this.
TL;DR; The Source and Navigation of both of queries is exactly the same - is this retrieving the data twice and causing my report to be inefficient, and if so, what is the approrpiate way to achieve what I am trying to do?
PowerBi will try to parallelize as much as possible. If you have two queries that read from the same table then two queries will be executed.
To avoid this you can:
create a query which only gets the necessary data from the table.
Set this table not to be loaded in the model (toggle "Enable Load")
Every other table that starts from this table won't be a clone of this but will reference it.
In this way, the data will be fetched once from the source and then used to create other tables using PowerQuery.
In Power BI, I've got some query tables generated from imported data. All the data comes in as type 'Any', and I'm trying to automatically detect the type of the data in each column.
Some of the queries generate tables with columns based on the in-coming data - I don't know what the columns are going to be until the query runs and sets up the table (data comes from an Azure blob). As I will have quite a few tables to maintain, which columns can change (possibly new columns being added) with any data refresh, it would be unmanageable to go through all of them each time and press 'Detect Data Type' on the columns.
So I'm trying to figure out how I can do a 'Detect Data Type' in the query formula language to attach to the end of the query that generates the table columns. I've tried grabbing the first entry in a column and do Value.Type(column{0}), however this seems to come out as 'Text' for a column which has integers in it. Pressing 'Detect Data Type' does however correctly identifies the type as 'Whole Number'.
Does anyone know how to detect a column's entry types?
P.S. I'm not too worried about a column possibly holding values of different data types
You seem to have multiple issues here. And your solution will be fragile, there's a better way. But let's first deal with column type detection. Power Query uses the 'any' data type as it's go to data type. You can write a function that samples the rows of a column in a table does a best match data type detection then explicitly sets the data type of the column. This is probably messy and tricky since you need to do it once per column. This might be workable for a fixed schema but for a dynamic schema you'll run into a couple of things very quickly. First you'll need to write some crazy PQ code to list all the columns and run you function on each. This will work the first time, but might break in subsequent refreshes because data model changes are not allowed during refresh. If you're using a tool like Power BI Desktop, you'll be able to fix things up. If you publish your report to the Power BI service, you'll just see refresh errors.
Dynamic Schemas will suffer the same data model change issue I mentioned above.
The alternate solution that you won't have problems with is using a Direct Query data source instead of using Power Query. If you load your data into Azure SQL or a Tabular Model, the reporting layer will get the updated fields automatically so you don't have to try to work around using PQ.