18 Sep 2019 | Data Strategy

Speed Up Development With Powered by Fivetran

Sean Spediacci
Sean Spediacci
Speed Up Development With Powered by Fivetran
Powered by Fivetran (PBF) provides a simple framework that allows developers to go beyond internal analytics and build data pipelines into their applications within the Fivetran platform.

Powered by Fivetran (PBF) provides a simple framework for developers to go beyond internal analytics projects to build data pipelines into their applications within the Fivetran platform. With no engineering overhead, you can easily access hundreds of customer accounts across countless Fivetran-supported data sources, including advertising platforms, CRM systems, databases, web events and more. As companies continue adding applications to their stack, they need a way to make sense of the large amounts of data coming in. 

Examples of Powered by Fivetran Use Cases

From marketing agencies to BI or data providers, here are a few examples of how businesses can make sense of their client data:

  • Through its platform, MindMax helps its university clients across the country increase enrollment. But a legacy analytics & BI solution required manually extracting data from disparate sources, including Salesforce, Google Analytics, Facebook Ads, and Pardot, and mashing it up in Excel. With PBF, MindMax can analyze all of the data it collects in one location and present dashboards and analytics to its clients.
  • A marketing agency will often manually stitch data together through a series of CSV downloads and merge it together for each client. This process is timely for the agency and results in slower reporting turnaround for clients. With PBF, an agency can use Fivetran to ingest data into its warehouse and build standardized reports that update automatically. Clients can log in and gain insights on their campaigns immediately.
  • Companies like Gitprime are building data-rich intelligence platforms on top of source specific connections. While its platform was not built on Fivetran, it could have easily leveraged PBF to ingest and manage data flows from Github instead of building and maintaining those connections itself, allowing its team to focus on building even more platform functionality.
  • Many companies want to invest in AI/Machine Learning capabilities but need data to work with first. With PBF, companies can remain focused on perfecting the algorithm and customer experiences rather than building and maintaining integration pipelines.

These are just some of the examples of how PBF can help businesses build their data-powered products. In each of these cases, the business does not have core expertise in data integration and needs to find a way to scale that function in order to grow and improve margins.

If you’re currently building a data product, consider what your company can gain if you keep engineering hours dedicated to your core offerings rather than pipelines. Contact an expert today to learn how we can help.

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