With smaller data sets and a limited number of reports/charts/users, the Data Studio database connectors can be an effective solution. There is, of course, an out of the box solution that is turn-key, very affordable, and can be setup in just a few minutes by any business user. Data Studio will be querying BigQuery for that, and everything stays in the powerful Google Cloud. This way your database will only be queried once when the data needs to be updated, not every single time any user in Data Studio clicks on a date or filter control in a report. One of the best solutions to eliminate all these connections, and to speed up your Data Studio reports, is to replicate your data to BigQuery. The trouble is that consumes database administration resources from the often overloaded IT department and slows down the business intelligence team.įor charts and tables that are missing data due to the 150k row limit, you can use custom SQL to return the data and avoid the error, however as our tests illustrate, there is still a big impact on your DB and the charts will be slow to populate. Of course optimizing indexes or perhaps creating more tables in your database is also critical.
Upgrading the DB might help to solve some of the performance issues? But chances are it might become very expensive since buying raw processing power to overcome the number of users and reports will likely eventually fail. Data Studio is more than capable of handing large data sets, but not via the database connectors. While it uses caching when users are requesting exactly the same data, any type of dynamic filters, date range selections, etc., will mean another hit on the database. Since Data Studio does not buffer your data within the Google Cloud it has to query the database constantly, leading to connection issues and “fatal errors” about having “too many connections”. But reports that have most of their charts reading from a database, often using custom SQL to avoid row limits, are often slow to load and refresh.
When only a few charts are needed to query smaller data sets (less than 150,000 rows), the Data Studio SQL Server, MySQL, and other database connector can often be a good solution. Often this results in connection issues and other error messages returned from Data Studio. Since it does this for every user using Data Studio, and for every click of every filter/date range change, your database works really hard to serve data to each user and report.Īll of these requests create a large number of connections - one or more per chart, per page, and per user - and a large number of queries to your database. It generates requests for data in parallel, as users make them. I also realize the workaround to the nested group activity restriction is to create a completely different pipeline, but that is not ideal and creates unnecessary complexity when trying to return the results.Data Studio is a distributed system capable of having many users online at once. I also cannot set the list of excluded books outside of the Until activtity as it will change with each iteration of the Until activity. I realize one way to solve this is to loop through each record of the ExcludedBooks and use a SetVariableĪctivity to build an array of BookIDs which WOULD work with the collection function Contains(), but ADF does not allow nested activity groups for some reason (ForEach within an Until). What I have is listed below which does not work.
Azure data studio filter tables how to#
I would like the filter condition to be based on the BookID value not being listed in the ExcludedBooks values, but I'm not sure how to write this condition based on the collection functions in ADF. [Īfter these two lookups, I have a Filter activity whose items are the values from BookList lookup. The second lookup is a list of books that I want to exclude from the first list ( ExcludedBooks) which is listed below. The first lookup ( BookList) is a list of books that look like the JSON listed below. I have two lookups within an Until activity in ADF.
0 kommentar(er)
