Data Engineering


Data Engineering: A bridge between Big Data and Data Science

Data Engineering was born out of the necessity to cover an existing gap within the world of big data and data science. This new field is based on the knowledge of traditional disciplines such as data quality, master data management and integration and relies on evolution to adapt to the challenges that come with facing the management of large quantities of data.

A key space exists between the two disciplines: the discipline of data engineering, a necessary profile that serves as a bridge between big data and data science. Big data specialists are dedicated to implementing an adequate architecture, a further layer of technology. However, for data engineers, the objective lies in the correct storage, processing and preparation of data for consultations, which allows data scientists to focus on their own work of analysis and discovering new relationships without losing time on the preceding processes.

This process carried out by data engineers is known as “data wrangling” and allows for optimization of the cleaning and data storage processes in order to achieve the maximum quality possible for analysis. Up until the appearance of this new profile, data scientists dedicated about 80% of their work to this process. Now, for the first time, they are able to concentrate on their main strength, advanced analysis.

At Synergic Partners, we understand this vision and have been, and continue to be, pioneers in the field and in the creation of teams of data engineers for our clients.