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The New Data Engineering

Data engineering is the set of practices that makes raw data usable. It involves designing and building systems that ensure the collection, transformation and analysis of data in a standard format. Once the data is engineered, data scientists can start deriving insights and developing machine learning or any AI-powered applications. 

According to Forbes, data engineering efforts in the foreseeable future are unlikely to be reduced due to several dynamics in the likes of increased data capture or data privacy regulations. As long as there is data to process, data engineers are highly needed. 

Yet in today’s organisations, the IT landscape is hyper-complex considering the heightened amounts of data captured and the increased number of tools and solutions. And often, these solutions do not integrate well with one another and in some cases do not even communicate. This means that tremendous work, time and resources are spent on ingesting, integrating and engineering multi-shaped data coming from different sources and platforms.

To sum up, high volume and variety of data captured at an astonishing pace, fragmented IT systems and environments as well as growing big data needs are the defining rules for current businesses’ data ecosystems. For all the above reasons, the data process complexity will considerably augment alongside the need for continuous data engineering. 

Yet, what about taking your data engineering to a whole new dimension? 

Our real-time data engineering platform, digazu has been specifically designed to simplify and automate the work of your data engineers. How?

We have taken every unnecessary steps away from the process to make data engineering as straightforward and accessible as possible. Digazu orchestrates and automates the collection, storage, transformation and distribution of your data. 

The platform was built to work at scale and integrate into your enterprise landscape seamlessly. It combines the best open technologies and architectures to provide scalable and sustainable data engineering. 

With such a solution, not only will you be able to optimise your data project life cycle, shorten your time-to-insight and unlock the value of your data more rapidly but you will also be able to increase the agility of your work environment and focus on what truly brings value. 

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