How can you use data integration to better understand and improve your customer experience? Read how Elevate used Integrate.io to improve CX in this case study.

Successful data pipeline projects being built require broader considerations than just the pipelines themselves. Understanding business outcomes and needs, help data teams build better data pipelines across the data ecosystem.

Bill Inmon, father of the data warehouse, discusses data science and the lack of understanding when it comes to data architecture and data warehousing.

Data observability helps data engineers with maintaining data quality, tracking the root cause of errors, and future-proofing the data pipelines. Let’s talk about the benefits in more detail below.

Too many data projects overlook the root causes driving data pipeline needs. Organizations need to drive data projects by evaluating business challenges, risks, and opportunities. Overall project success requires an...

The AlloyDB connector from Integrate.io empowers organizations to rapidly consolidate all of their data into AlloyDB—a high-powered, Google Cloud database that is 100% compatible with open-source PostgreSQL. By serving as...

Bill Inmon, the father of the data warehouse, explains why it is a tautology that transformation is required for the proper analysis of unintegrated data sets.

The five important key factors of any Data Lake to implement a successful architecture are Security, Governance, Metadata, stewardship, & ETL

In the competitive market of ETL solutions, platforms like Hevo, Fivetran, and Integrate.io are among the top contenders. So how do you decide which tool meets your business needs?