Read about the 2022 latest trends and use cases shaping the new automotive industry.
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.
Our CEO shares his thoughts on the 2022 most defining trends of the data and analytics market.
A leading investment bank approached us recently to review their data management to support new analytics use cases. Learn about how we helped them enhance their data processes through automation and scalability.
Read our latest White Paper on the most common mistakes of digital transformation programs, the dos and don'ts of the data journey. Come take a closer look at some of the most challenging pitfalls businesses regardless of their space face in their pursuit of digital and capture the best practices and lessons learned to to drive successful data projects.
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.
In this article we are focusing on a crucial step in adapting better data practices: data processing. Use your data to its fullest by properly managing how you store the data so that it is readily available any time you need to make a decision.
Overlooking the basic stages of the data journey happens very often which results in severe delays of ML and deep learning projects. This article shines the light upon the main players in the 80/20 data Science dilemma: data preparation. Find out how to make sure that your data scientists and ML experts uncover insightful value from your data in the shortest time possible.
Pushing the limits of your data doesn’t have to be a difficult task. Still, if left alone, data has little to no value. Read this article and bring your data to life by taking proactive actions towards understanding how to overcome the challenges that data collection can pose. Sometimes simple solutions can solve complex problems.