Education

In data management, selecting the appropriate approach to collect and process data can significantly impact the efficiency and responsiveness of analytics pipelines. One methodology gaining traction for its transformative impact is “incremental data collection”.

Businesses continually seek to maximise the value of their data assets, and data productisation stands out as a powerful strategy. But what exactly defines a data product, and how does it transform the way businesses use their data?

If you are uncertain about what incremental and parallel processing actually mean and, more specifically, why they are considered as effective approaches to processing high-volume data, you have landed in the right spot.

In this blog post, we explore why traditional ETL chains groan under the pressure of high-volume data and discuss strategies to address these challenges.

The data mesh architecture is built around four fundamental concepts, the second of which is “data-as-a-product” often just called data products.

IT-powered innovations, the Internet of Things and new generation customer demands are fueling real-time data creation. In this new data landscape, businesses from all industries are quickly shifting from batch processing to real-time data streams to match the new imperatives. In this blog, our marketing analyst, Selima Triki, unpacks the concept of real time and real-time data, some of its benefits and applications.

In this blog, we uncover the multiple facets of data engineering, what it is and how complex it can be. Digazu’s product manager, Luc Berrewaerts walks us through the new technologies to navigate the complexities of data engineering.

Learn the various ways in which you can store your data and choose the one that best suits your needs. This article will underline the difference between data warehouse, data lake and data mesh while exposing the benefits and drawbacks that you could encounter. Find the best solution no matter how mature your data is