I'm new to Oracle spatial and finding it hard to visualize complex Aggregate Geometry functions. So are there any provisions in Toad for Oracle to view the results of the queries on spatial data?
TOAD doesn't support any kind of visualisation for the geometry data type, but there is one built into SQL Developer, which you can get for free from Oracle.
You can find out more information here:
http://beyond-just-data.blogspot.co.uk/2015/07/sqldeveloper-and-map-data.html
Related
I am using Snowflake as my backend database and created & published a dataset in Power BI with direct query. As a next step I am trying to analyze the data (to get Pivot experience) in excel.
I am observing the hierarchies I have created are not showing up in excel, though those are showing when accessing through PBI Service.
DirectQuery comes with a slew of limitations compared to imported datasets. The only hierarchy-specific limitation included in the official documentation is that Auto date-time hierarchies are not created for DQ datasets. However, this documentation is about direct limitations and doesn't specifically cover limitations that might only be applied to XMLA connections, which your connection from Excel is.
A workaround is to just use computed columns with the hierarchy values and name them like Category01, Category02, Category03 and do the nesting yourself. Users often have use cases that involve using hierarchies out of order (like grouping by Category03 THEN by Category01) and so consider it a feature rather than a flaw.
Is there a way to call a SQL server stored procedure from Power BI?
Do you have to use Import Query mode only?
I need the visual based on the SQLstored procedure to refresh though!!
Can you do this with Query Direct Mode?
Please help!
It's not possible.
From Microsoft's Documentation:
Limited data transformations
Similarly, there are limitations in the data transformations that can be applied within Query Editor. With imported data, a sophisticated set of transformations can easily be applied to clean and reshape the data before using it to create visuals, such as parsing JSON documents, or pivoting data from a column to a row form. Those transformations are more limited in DirectQuery.
First, when connecting to an OLAP source like SAP Business Warehouse, no transformations can be defined at all, and the entire external model is taken from the source. For relational sources, like SQL Server, it's still possible to define a set of transformations per query, but those transformations are limited for performance reasons.
Any such transformation will need to be applied on every query to the underlying source, rather than once on data refresh, so they're limited to those transformations that can reasonably be translated into a single native query. If you use a transformation that is too complex, you receive an error that either it must be deleted or the model switched to import.
Additionally, the query that results from the Get Data dialog or Query Editor will be used in a subselect within the queries generated and sent to retrieve the necessary data for a visual. The query defined in Query Editor must be valid within this context. In particular, it's not possible to use a query using Common Table Expressions, nor one that invokes Stored Procedures.
I have a powerbi report for finance. Users need to see the latest data in real-time, so I have to choose DirectQuery. But in this mode, some functions, such as DateAdd and DatesMtd, cannot be used
(This DAX function is not supported for use in DirectQuery mode.),
So I need to write a very complex SQL statement to achieve the equivalent effect, but this makes the report very slow (more than 10 seconds) every time it runs, and the largest table in my data model is less than 80000 rows. I've tried to optimize the SQL statements, but it doesn't help. Any solution?
(I use powerbi report server with sqlserver enterprise version)
Of course, with no information, I can't know what's taking so much time, but in order to understand what's happening you can use the following tools:
PowerBI Performance Analyzer: This will tell you what part is taking the most time. for more info see MSDocs & SQLBI
Check the datamodel and the storage mode of each table involved (ie: fact table, calendar, customer, etc). When querying the source, PBI won't use filters (directly in the query) that come from tables in import mode. (search for "composite models" on the web)
Limit the number of objects, for each object in the dashboard a query will be sent to the datasource, limiting the number of objects might help. (remember that objects wait for each other, so one slow loading object might cause your problem)
(even if you probably already did it) Have a look at the query execution plan, you can also check it for queries automatically created by PowerBI by capturing them (the easiest way is to use SQL Server Profiler)
I think that just by using PowerBI Performance Analyzer you will be able to see where the problem is, and then do more accurate search about it.
You need to search for those keywords;
Native query in power query: Some M language functions can directly be translated to SQL, so that all transformation happens in sql server side.
Aggregated tables in model view: aggregated views can be added for specific needs of visuals. Ex: if a visual has product category and amount as value you can connect aggregated sql table to the original one so that visual can pick up the value faster.
Hybrid tables: import mode and DQ mode can be used together. so you can use DQ for daily data and import mode for older data together.
We're trialling PowerBI on a Snowflake dimensional model and performance seems very non-optimised. Can anyone point me to information on best practices for this connection? I've previously used Tableau and there's an excellent white paper describing the pros/cons of each connection type and how to set this up so that as much heavy lifting as possible is done in Snowflake, with minimal load on the viz tool.
e.g. when you summarise 1 million invoices to get a chart of sales volume by year that distils this to 10 data points, Tableu would send 'SELECT year, sum(volume) FROM t GROUP BY year' (~10 rows), but in PowerBI we see SF receiving a query like 'SELECT invoice_id, sum(volume) FROM t GROUP BY invoice_id' (~1M rows) - leaving the viz tool to do a lot more work.
So far, we've tried mapping the individual facts and dimensions within PowerBI, and also using a mix of direct query and import, but without significant improvement. Is there any guidance on best practice?
Thanks in advance!
I've never used Snowflake, and I have no clue about how PowerBi interfaces to it. That said on the PowerBI side you may be interested in the composite model and aggregations.
MS Docs:
https://learn.microsoft.com/en-us/power-bi/desktop-composite-models
https://learn.microsoft.com/en-us/power-bi/desktop-storage-mode
https://learn.microsoft.com/en-us/power-bi/desktop-aggregations
Radacad's blog about aggregations:
https://radacad.com/power-bi-fast-and-furious-with-aggregations
https://radacad.com/dual-storage-mode-the-most-important-configuration-for-aggregations-step-2-power-bi-aggregations
In practice, when you are using a composite model the aggregation functionality allows you to create a hidden table (in import mode) in your model with aggregated data (by year, month, customer, etc).
Now when you query your data, PowerBI will check if this table can answer the query, if yes then it will just pick the data from this table, otherwise, it will run a query against the source (direct query)
The example you shared about PowerBI querying the source without asking for aggregation (but instead asking for every single InvoiceId) might be caused by not setting up the composite model correctly.
A table in "direct query" cannot reference other tables in its query (in this case the calendar) unless that table is also in "Direct query" or "dual" mode.
How does the model look like in the case you shared? and which is the storage mode of each table?
I currently need to blend two data sources (two distinct BigQuery tables) in Google Data Studio which means every time a report filter selection is updated, Data Studio needs to re-compute the left outer join on the fly (or at least it looks as if it is because it takes ages to load). I was reading about BI Engine and wondered if that could be useful for this use case in speeding up left outer joins in Data Studio?
https://cloud.google.com/bi-engine/docs/getting-started-data-studio
Yes, it should be.
For this use case, you should create a new view in BigQuery that joins the two tables. Then query that view directly from Data Studio. The guide you linked to has information on how to enable BigQuery BI Engine for your GCP project.