Digazu helps you answer the ETL challenges, its platforms will gear companies up to achieve their ETL and data-driven business ambitions and answer the above challenges. With Digazu’s platforms companies can reduce the:
- Consumed time : collect and register your data sources. Thanks to its transformation layer, data transformations can be shared between users and use cases, thereby saving time and resources. This means no more ETL spaghetti to maintain and less work for IT to extract data from operational systems.
- Demanded expertise: Digazu handles the creation and the maintenance of the pipelines. No matter the number of pipelines, the allowed user that consumes the data (at any time) the transformed data can be loaded multiple times in an easy and fast way. It makes the use of the operational systems easier.
- Costs: you can optimise the number of data engineers and/or optimise the workload of your Data scientists. They can quickly and easily get the data they need to build and train their predictive models. So the number of models will increase and the cost per model will decrease.
Furthermore, Digazu streams the data in real time. With it, you can feed your models and services with real-time events. It enables advanced use-cases such as live customer experience personalisation, predictive maintenance, real-time banking and fraud detection.
We can conclude that many companies are not yet geared up to achieve their data-driven business ambitions. Digazu’s platforms will help the companies leveraging the value of their data, by extracting data only once, sharing the transformations on data, and loading transformed data multiple times.
Using Digazu’s platforms (end-to-end data engineering platform & end-to-end data science platform) will rationalize the process, lower the utilisation of data, and it’s cost by making the life of the data engineers easier.