I'm having datasets in bigquery google cloud project,
I want to load this data from powerbi,So that I can create visualizations using this bigquery data sets.
Currently I'm able to see the google bigquery in getdata option in powerbi desktop,I tried to connect to bigquery but I'm not abe to see my bigquery dataset in the list,I'm using the same credentials for powerbi and google bigquery.
is there any thig that I need to enable in google cloud side so that it will be visible in the list when I connect from powerbi?
These are the following steps you can follow to connect GBQ to Power BI.
You can write a query and save the table in Google big Query.
Go in Power BI, get data and click on more and scroll down there you will find "Google Big Query" click on that.
Search your saved table and click on "Direct Query"
I think you will be good to go!!
I would not recommend using Power BI for Big Query analysis. As of Q1 2019 Power BI still do not support project billing unless manually editing the connexion script, it does not support Sheets federated tables, do not support nested and repeated fields and Power BI DML is nearly unusable with BQ. Microsoft do not seems to be willing to develop the compatibility between Power BI and Big Query further.
Learn more in this article (i am the author): https://medium.com/#remy_david/which-bi-tool-for-big-query-d9eb838ff7ad
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I am creating a Power BI dashboard using someone else's Power BI dashboard. All I've done right now is used Get Data -> Power BI datasets -> clicked on the dataset that is also used by this other person's dashboard. Then when I click on the Model tab on the left hand side, I see a bunch of SQL servers linked to one another. If I start changing the SQL relationships in the model section within my Power BI Desktop, will that also change the SQL relationships within the other person's Power BI dashboard? I am a new user of Power BI and SQL so apologies if this sounds confusing. I can provide more info if necessary.
Thank you.
When you use Get Data to connect to an existing Power BI Dataset, you aren't copying the dataset. Instead, multiple reports & dashboards will all connect to the same dataset. So, in the event that you have the required permissions, you changing the dataset will change it for the other reports & dashboards (which could break them).
For more information, check out the Shared Dataset information on MS Docs (e.g. https://learn.microsoft.com/en-us/power-bi/connect-data/service-datasets-across-workspaces).
More of a curiosity question really. I load data into Power BI report from Google BigQuery (using native Google BigQuery connector in Power BI). All works fine, but for some reason I don't see this query in BigQuery's query history.
Did anyone experience something similar and knows the reason why this happens or how to change that (if at all possible)?
If I do exactly the same thing but using simba ODBC connector, I see this query in BigQuery's query history as expected.
Never seen that before. I am always able to find the query history no matter what 3rd party connection I used. Could you confirm the GCP service-account or auth-account and the GCP project for BQ job query that you used for your native Google BigQuery connector in Power BI?
Please make sure you have the access to the query history of that GCP account in that BQ job project.
I have created an instance on Google BigTable and created an external table on Google BigQuery and created an external table which can able to fetch records from BigTable. I tried to create a BigQuery datasource in Power BI Desktop but the external tables are not listing but the tables created on BigQuery are been listed! Is it an issue? or any possible solutions?
I replicated your scenario and got the same behavior as the one you described. It seems that at the moment Power BI does not support BigTable external tables. A workaround is to create one or more views based on the BigTable table and then connect to those views.
In addition, if you're interested you can report the Power BI behavior here.
We are very pleased with the combination BigQuery <-> Tableau Server with live connection. However, we now want to work with a data extract (500MB) on Tableau Server (since this datasource is not too big and is used very frequently). This takes too much time to refresh (1.5h+). We noticed that only 0.1% is query time and the rest is data export. Since the Tableau Server is on the same platform and location, latency should not be a problem.
This is similar to the slow export of a BigQuery table to a single file, which can be solved by using "daisy chain" option (wildcards). Unfortunately we can't use similar logic with a Google BigQuery data extract refresh in Tableau...
We have identified some approaches, but are not pleased with our current ideas:
Working with incremental refresh: our existing BigQuery table rows can change: these changes can only be applied in Tableau if you do a full refresh
Exporting the BigQuery table to GCS using the daisy chain option and making a Tableau data extract using the Tableau SDK: this would result in quite some overhead...
Writing a Dataflow job using a custom sink for Tableau Server (data extracts).
Experimenting with a Tableau web connector that communicates directly with the BigQuery API: I don't think this will be faster? I didn't see anything about parallelizing calls with the Tableau web connecector, but I didn't try this approach yet.
We would prefer a non-technical option, to limit maintenance... Is there a way to modify the Tableau connector to make use of the "daisy chain" option for BigQuery?
You've uploaded the data in BigQuery. Can't you just use the input for that load job (a CSV perhaps) as input for Tableau?
When we use Tableau and BigQuery we also notice that extracts are slow but we generally don't do that because you lose BigQuery's power. We start with a live data connection at first, and then (if needed) convert this into a custom query that aggregates that data into a much smaller datasets which extracts in just a few seconds.
Another way to achieve higher performance with BigQuery and Tableau is aggregating or joining tables on beforehand. JOINs on huge tables can be slow, so if you use a lot of those you might consider generating a denormalised dataset which does all of the JOIN-ing first. You will get a dataset with a lot of duplicates and a lot of columns. But if you select only what you need in Tableau (hide unused fields!) then these columns won't count in your query cost.
One recommendation I have seen is similar to your point 2 where you export the BQ table to Google Cloud Storage and then use the Tableau Extract API to create a .tde from the flat files in GCS.
This was from an article on the Google Cloud site so I'd assume it would be best practice:
https://cloud.google.com/blog/products/gcp/the-switch-to-self-service-marketing-analytics-at-zulily-best-practices-for-using-tableau-with-bigquery
There is an article here which provides a step by step guide to achieving the above.
https://community.tableau.com/docs/DOC-23161
It would be nice if Tableau optimised the BQ connector for extract refresh using the BigQuery Storage API. We too have our Tableau Server environment in the same GCP zone as our BQ datasets and experience slow refresh times.
I'm using Power BI web preview and have SSAS as a data source. When my underlying SSAS tabular data model changes, I don't see my reports or the dataset in Power BI get updated.
Since manually "refreshing" the Power BI data set is not supported as of now, how do I make sure that my Power BI reports are in sync with the latest data?
There's a great blog on the Analysis Service connector here:
http://blogs.msdn.com/b/powerbi/archive/2015/03/11/power-bi-analysis-services-connector-deep-dive.aspx
It includes the top trouble shooting tips. If you're still having problems, please use the "?" at the top of the power bi preview UI to ask for support.
HTH,
Lukasz
As of now you can use Power BI Gateway, and you will have the option to update cubes on scheduled intervals, or you can live connect as well.
Are you using import or direct query method? If you are using import mode you can set up an schedule refresh for that. If you are using direct query all your data is going to be updated