Other factors that slow down your reports include network latency and the complexity of data in your tables. The more often this happens, the more likely it is that your Google Looker Studio performance will slow down. The frequency of performing a new query to portray the latest data is determined by “ Data freshness”. On the other hand, each time a page of your Looker Studio loads, a query on your Data Source is performed automatically to retrieve the latest data. Depending on the project you’re working on, consider reducing the number of elements that need to be queried from your charts. This slows down the output process significantly. When you’re producing reports with data from platforms like Google Analytics or Google Ads, you’re usually querying many detailed columns of data that are not necessary for your reports. Slow performance caused by network latency issues.Slow performance occurs because unnecessary data is being queried.Slow performance occurs due to inefficient connectors.Crashes occur when trying to fetch a huge amount of data directly from the original source.Slow performance occurs when setting a frequent “Data freshness” update period.The underlying data set is causing poor performance.Data can’t be queried within the expected timeframe because it’s too complex.However, there are a number of other issues that could affect how you work with Google Looker Studio (formerly Data Studio). The above factors are said to affect the speed of producing data reports. All of these factors combined could negatively impact other work processes in the meantime. This can make it take longer for data reports to load. Last but not least, the number of people using Looker Studio at the same time can also cause problems. The output of Looker Studio reports also depends on the performance of the respective data set, according to Google. However, the nature of the queries you write in Looker Studio could also be the reason for slow performance, as complex queries take more time to execute. Slow reports can impact all the processes in your agency.įirst of all, the data queried by the report’s visualisation tools could be so complicated that the algorithms can’t answer quickly. Or, delaying a keyword analysis, which then leads to missed deadlines and dissatisfied clients. Imagine you have a client waiting for a website audit and it takes you hours to produce a single report. The size and complexity of your datasets are the main reasons behind the slow reports in Google Looker Studio (formerly Data Studio). Two use cases, two tools.īigQuery also makes it super easy to push your data out to other data visualisation platforms like Tableau, Looker, Microsoft Power BI and more if you want to switch things up.īut of course there are other data warehouse tools as well so let’s go over why we recommend BigQuery for digital marketing agencies. BigQuery to prepare and process datasets and Looker Studio to visualise the tables. Think of it this way: Google is giving you two highly performant tools that integrate seamlessly. In a second step you then push out the data to Looker Studio, which is designed to visualise data, but ideally you want to be working with finished tables here. What you can do to avoid performance issues is to first pull the data into BigQuery which is Google’s data warehouse and unlike Looker Studio it’s actually designed to query and process big datasets so you won’t experience any performance issues. Many agencies that are using connectors like Supermetrics or Funnel are experiencing performance issues when working with Looker Studio (formerly Data Studio) once datasets get bigger and the reason is that you are most likely skipping an important step.
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