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Utilizing big data is one of the biggest assets your organization can use to stay ahead of the competition. Even though big data continues to grow, most organizations have yet to leverage its capabilities fully. Why? Because millions of data sources exist on the internet and physically. Ingesting and integrating this data can quickly become overwhelming. With data lakes, you can integrate raw data from multiple sources into one central storage repository.

A recent decision by the European Commission should make data transit from the EU to the US simpler.

Not all ELT solutions are created equal. Here are the capabilities your tool needs to efficiently move data.

How data technologies circumvent ugly tradeoffs and satisfy competing priorities.

The final iteration of our series on ASCII files; how to combine dbt and Fivetran to integrate ASCII files.

In today's "Big Data" world, data integration is a critical business process. With the exponential growth of information seen in enterprises all over the globe and increasingly competitive marketplaces, there is a real need to consolidate and analyze business data. Big Data is made up of large amounts of information from many sources: customer interactions, Internet of Things (IoT) devices, mobile apps, SaaS cloud services, and more.

A few weeks ago, I wrote a post summarizing "Seven Data Integration and Quality Scenarios for Qlik | Talend," but ever since, folks have asked if I could explain a little deeper. I'm always happy to oblige my reader (you know who you are), so let's start with the first scenario: Database-to-database synchronization.