Data Infrastructure and Processing
Integration of Multiple Sources
Connecting and obtaining data from multiple sources, such as different type of databases, tracking tools and third-party applications.
We optimize the data transmission between systems.
Our algorithms process data in the cloud with parallelization techniques to save resources and maximize the efficiency.
Data warehouses and data lakes
We develop data lakes where data can be stored in different layers based on the degree of transformation applied to the data (normalization, anonymization, curation, etc.) from the original to the most transformed or final version.
We develop all type of algorithms based on latest techologies for big data processing.
Processing data from different sources through automated ETL tasks (Extract, Transform and Load).
The data is processed to obtain data sets that are used for different purposes, such as reporting, development of machine learning algorithms or business intelligence activities.
ETL tasks include connectors to different sources, dataflow creation, tasks automation and Data normalization