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.

Discover how you can take your data engineering to a whole new dimension with Digazu. Specifically designed to simplify the work of your data engineers, Digazu was built to work at scale and to integrate with your IT landscape for sustainable and scalable data engineering.