![]() Give it a try today - it takes just a few minutes to turbocharge your Data Studio reporting, relieve the added pressure on your databases, and resolve database connection issues in Google Data Studio.Data science is an essential part of many industries today, given the massive amounts of data that are produced, and is one of the most debated topics in IT circles. While custom SQL may help to deal with the 150K row limit, and while you can have a DBA index tables or scale up the DB, the time and costs involved erode the value of using Data Studio pretty quickly.Īnalytics Canvas eliminates these issues by buffering the data in BigQuery, using Data Studio’s Advanced Services, caching results that are re-used across the report, and applying other optimization techniques to reduce response times and protect your database from unnecessary load. However, due to the way that the SQL Server, MySQL, Postgres, and other database connectors within Data Studio have been designed, your database can quickly become overwhelmed when trying to serve data to Data Studio reports. ![]() 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. Data Studio is a distributed system capable of having many users online at once. ![]()
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