Fivetran pipelines reliably load your data to your chosen destination, but then what? Without joining, filtering, and aggregating your data, your business can’t produce data models to answer critical business decisions. This is why data transformations are essential to every business looking to maximize value from the data they collect from disparate sources.

A French startup is leveraging its modern data stack to overturn a longstanding business model.

You no longer need to coordinate with your administrator and manually translate formulas from Salesforce SQL to Standard SQL.

Monitor the number of surveys being created, track new responses coming in, see which surveys are generating the most responses, and more with Databox’s integration with SurveyMonkey.

Nearly 30 SEO professionals share the top Google ranking factors they are paying attention to in 2021.

The Modern Data Stack is quickly picking up steam in tech circles as the go-to cloud data architecture, and although its popularity has been quickly rising, it can be ambiguously defined at times. In this blog post we’ll discuss what it is, how it came to be, and where we see it going in the future. Regardless of whether you’re new to the modern data stack or have been an early adopter, there should be something of interest for everyone.

Automated ETL (extract, transform, load) and data integration workflows are essential for the modern data-driven organization, and they can swiftly and efficiently migrate data from sources to a target data warehouse or data lake. But ETL must run at regular intervals — or even in real-time — so how can you know which information is fresh and which information you’ve already ingested? Solving this problem is the goal of change data capture (CDC) techniques.