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Caeli: The Sky Is Not the Limit in Measuring Particulate Matter in the Atmosphere

Measuring by Satellite

Caeli is a startup dedicated to providing insight into air quality with a view from above. The satellites orbiting our planet provide their end-users with chronological and (near) real-time information. Satellite imagery can be a cheaper and more readily available option than remote sensing as a tool for measuring the molecular composition of our atmosphere. Generating maps displaying particulate matter such as Nitrogen Dioxide (NO2), Ammonia (NH3), Methane (CH4) and Ozone (O3) can help the public and government understand how changes to the atmosphere may affect health or influence the climate.

An enormous amount of visual and quantitative satellite data needs to be processed to create these real-time insights. Copernicus Sentinel imagery offers Caeli historical insights to observe change in air quality over time. The raw multispectral and geographically calibrated source data must not only be able to be processed quickly but also organized chronologically and stored overtime in accessible files. The original Caeli database was not scalable enough to support these data streams, so we needed a new architecture. 

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ELT, ETL and Data Pipelines: loading Data in an Automated Manner and how to do it yourself

My name is Don and I am something between a data engineer and a data scientist. I automate repetitive tasks, generate insights from the data, manage projects and I often help in an advisory role for these processes. I especially enjoy the creative process required to solve problems using data.

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Data Pipeline Implementation: how to do it yourself

These instructions build on what has been discussed in ELT, ETL and Data Pipelines. In that guide, we discussed the problems that arise in storing and using data for a company. In response to those problems, we introduced the concept of Data Pipelines, which helps the company become better aware of the data loading steps and incorporating these steps in the most optimal way to create a Data-driven Culture. We also discussed some specific tooling that can be used to properly deploy Data Pipelines. 

Now that we understand the concepts behind Data Pipelines, we will now apply them to implement a functioning Data Pipeline. Just like most of our data engineering processes, we follow a step-by-step plan and provide an implementation strategy for each step. 

Hopefully a step-by-step plan will give you a solid foundation when you are constructing your own Data Pipeline as well as the implementation methods.  You can find the whole code on our Giftlab.

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Data Scientist Jason: “Young Mavericks taught me a lot of great technical skills”

Jason (27) was one of Young Mavericks’ first generation of Data Science trainees. “As a
Young Mavericks trainee, I could do all sorts of projects at the Chamber of Commerce and
the Consumers Association with other companies. The traineeship taught me so much – I
developed data and programming skills and learned how to arrive at customer-focused
solutions.”

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Making Waves with Cordstrap: How Young Maverick’s Data-Driven Solutions Protect Ships

We collaborate with partners of all shapes and sizes: from banks, insurance companies and marketing specialists to the Dutch government and publishing houses. Each project brings a new  collaborator, but Young Mavericks’ goal remains the same: to make the possibilities of innovative technology useful for organizations.One of our latest success stories is our collaboration with Cordstrap, a multinational with Dutch roots that specializes in keeping the world’s Cargo safe. For their ‘Cargo Monitoring Data’ project, our Data Scientist, Wing, developed models that optimize risk analyses, which enable Cordstrap to better monitor and protect their customers’ cargo.

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