Data Engineering: unlocking insights with complete, high-quality data.

The infamous saying in the data community “garbage in is garbage out” makes one thing crystal clear: good data science and analytics cannot exist without good data. That is why the goal of Data Engineering is making complete, high-quality data securely available at your fingertips.

Data Engineering achieves this by automatically collecting, transforming and storing data in a structured manner fitted to your organization. Among other things, this structured approach allows you to trust your data, retain valuable information and drastically improve efficiency of your data teams. Simply put, Data Engineering creates the foundation that allows your organization to truly be data-driven.

 Does this sound familiar?

•  “My reports contradict each other.”
•  “I spend more time on collecting and managing my data than on generating insights.”
•  “I have no overview of how data is collected, transformed or stored.”
•  “I simply do not trust my data.”

If you recognize these or similar data related challenges, it means it is time to take your data science and analytics to another level with Data Engineering.

Data analysts and scientists are in their element when they turn data into insight. They have the technical skills to turn raw data into clean datasets, but often see it as a necessary or temporary means to an end. Data Engineering is so powerful because it flips the problem around; rather than fixing problems when they arrive, it creates a data structure that tackles these problems in advance.

The effect of well applied data engineering:

So what is Data Engineering? Data engineering combines data from multiple sources and transforms it into a structure that is optimized for use by the rest of the organization. The exact transformation and end result setup strongly depend on the end goal, data types and performance and security requirements of your organization. These and other requirements are captured in an architecture that defines the parameters of the solutions.

So, what does such an engineered data driven solution look like? There are three typical data engineering solutions with varying degrees of complexity, completeness, and impact:

Data Pipelines

To get your analysts and data scientists the information they need, we develop data pipelines to transform your organization’s data into useful information in a structured manner.

A Single Source of Truth

One data structure containing cleaned, organized, and well-maintained dataset. This directly leads to solving issues like having conflicting reports, not trusting your own data or inefficiencies when doing data analytics or data science projects.

Modern Cloud Platform

When your data solutions need increased scale and performance, a cloud data infrastructure may be the right option. By using modern cloud platforms and techniques like Microsoft Azure, AWS, Google Cloud and DataBricks, you can enjoy these and other advantages while optimizing for costs and security.

We have the know-how to bring the power of data engineering to your organization. At Bright Cape, we deliver data engineering solutions tailored to your organization’s needs. By paying a lot of attention at the start of the project to the specific needs and requests of the customer we make sure that we deeply understand what we want to achieve and how we can best approach the solution. We have found that this approach has led to successes for both our customers and us. Once the goal is clear, we take an iterative approach and collaborate with internal and external stakeholders to build the solution that works. The aim is to put you in control of your data and empower your data scientists and analysts. Our wide range of reference cases detailed below exemplify this. These reference cases show how data engineering allows you to gain control and retrieve value from data in specific business contexts as well as for a whole business department.