Many people are concerned about Google Data Studio row limits and are struggling with slow reports as they add more data. As Google Marketing Platform Technology Partners and with many customers experiencing this problem, we set out to address it.
The result? We have effectively removed row limits on our Google Data Studio Partner Connector while delivering exceptional performance!
Overcome the Row Limits and Speed Up your Google Data Studio dashboards
Analytics Canvas was among the first 10 partners to release an SQL connector for Google Data Studio. Serving a global market of business users and IT professionals, we've seen a wide array of use cases and creative ways that people are using Data Studio.
So far we've satisfied customers looking to dramatically increase the performance of their reports. Our Partner Connector takes the pressure off of databases and allows them to seamlessly connect more of their data to Google Data Studio.
Customers are using Canvas to get data from SAP HANA, Amazon RedShift, Microsoft SQL Server, Oracle, and our best-in-class connectors for Google Analytics, Google AdWords and Search Console among other databases, files, and APIs.
But two issues kept coming up with customers large and small both as datasets continued to grow over time and as their adoption of Data Studio increased: data size limits and slow Data Studio dashboards.
Why are Data Studio Reports Slow?
If your Google Data Studio reports are slow, chances are you're using the native Sheets connector, your own MySQL, SQL Server, Oracle, or Postgres database, an API from Google (such as Google Analytics or Google Search Console) one of the other Partner Data Connectors.
Part of the reason these sources are slow is how they are designed and the resources behind them. Google Sheets, for example, is not intended to store large volumes of data. And while it is nice to have the 'live' connectivity of an API like Google Analytics, the connector just wasn't designed to fetch large volumes of data as fast as possible.
Data Studio itself is not designed to intelligently cache the results - if you make a query that can be served by existing data, it still goes back to the original data source, dropping all that data that already arrived.
Google has created the Partner Data Connector program and empowered developers to design solutions to these exact data problems... and Analytics Canvas has delivered.
Speed up your Google Data Studio dashboard
The Analytics Canvas connector is designed for performance. It alleviates pressure on databases and APIs by buffering the data within the Partner Data Connector. This means that dozens, even thousands of users can consume Data Studio reports without overwhelming databases and exhausting API limits.
Powered by BigQuery and using Data Studio Advanced Services, the Analytics Canvas Partner Data Connector is one of the highest performing connectors in the gallery. It buffers the data and incorporates a proprietary Dimensional Caching layer to serve data as quickly as possible without leaving Data Studio.
Whether your dataset is 1,000 rows, 1M rows or 1B rows, you will be able to visualize your entire dataset with the speed and power of the Google Cloud to process and return your results.
In fact, the larger the dataset, the more impressive the results! But don't just take our word for it - there is an instantly available free trial. Connect Canvas to your data in Sheets, Excel, databases, or files, and create your own Data Studio datasets in either Canvas Desktop or Online.
You can setup your own test very quickly either on your own or in a guided session with one of us.
Fix your Row Limit problem using our free trial
Concerns over Google Data Studio row limits are a thing of the past. The Analytics Canvas Partner Connector is capable of holding up to 2 Billion rows per dataset! Even very large datasets perform exceptionally well, and that performance is highly scalable as reporting ramps up.
Our best-in-class connector is included in our Starter, Pro, and Premium plans available for $49/mo, $299/mo, and $499/mo respectively.