Data Storage

At Supermetrics, we’ve always been big advocates of storing marketing data. We believe all marketers should have control and ownership of their data, not only to keep it safe but to get more value out of it. ...

It seems like data lakes are all the rage these days. Why is that? And more importantly, if you work with marketing data, why should you care? And if you do care, but don’t know how to query data in your cloud storage bucket from BigQuery, how would you go about doing that? In this ...

Earlier this year, we introduced Supermetrics for Azure Storage, a fully managed data pipeline that allows you to move your marketing and sales data into Microsoft’s data lake solution with a few clicks. ...

In this guide, I’ll show you how to use the Supermetrics API with Python to query data from marketing and digital advertising platforms (like Facebook, LinkedIn, Twitter, and many more) in JSON format, how to convert that data into a DataFrame, and how to load it into a local MySQL database on your device. But ...

You know how your marketing data lives across dozens of different platforms, including — but definitely not limited to — Google Analytics, Facebook Ads, LinkedIn, Google Analytics, and/or Salesforce? A cloud-based marketing data warehouse (like Google BigQuery, Snowflake, or Azure Synapse Analytics) allows you to centralize all that data into a single destination. ...

Finance, sales, and product all appear to have access to all the data they need with technology stacks that can churn out answers to their questions in seconds. From their fully cloud-based set ups to beautiful dashboards in their BI tool of choice, it seems marketing is left trying to manually patch data together using ...

As the volume of marketing data grows, more and more businesses are moving their data to a data warehouse. Snowflake is a great choice for many as it can be built on top of any existing cloud platform, whether it’s Google, Amazon Web Services, or Microsoft Azure. That’s why we built a Snowflake connector for ...

While you’ve always been able to use common data extraction schemas in Supermetrics for BigQuery, you might occasionally also want to pull metrics that aren’t covered by a standard schema. To tackle this need, we’ve launched a new feature: the schema builder. The schema builder offers an easy way for you to create custom data ...

Because of the increasingly fragmented marketing data landscape, data-driven marketers now need specialized tools to reduce the time it takes for data to be processed into information. While marketing data warehouses are not a new concept, the legacy model to implement them has serious drawbacks. ...