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Carmel Eve By Carmel Eve Software Engineer II
Reactive data processing and a huge wealth of learning - A year as an Apprentice II at endjin

I've just completed my first year working full time for endjin, after I joined as an Apprentice II last May.

And without sounding too much like a cliché, time has truly flown. In the ins-and-outs of daily working life I haven't really noticed the huge progress I've made in the last year, but as we were going through my career development plan last week, I realised how much I'd learnt!

I've been involved in a huge number of exciting projects since last May. Just to name a couple, these included building modern data processing pipelines which fed machine learning models to give real business insights, and reactive processing engines capable of processing millions of events a second in real time!

These projects have really opened my eyes to the huge expanse of possibilities and opportunities for insight through data science.

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As part of the apprenticeship, I am also given the opportunity to spend a significant proportion of my time expanding my knowledge. Over the past year, I've attempted to consolidate that knowledge as I go. Twenty-five blogs later, here are the main topics I've covered in the last year:

And that's just the stuff I've gotten around to blogging about…

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So overall, the last year has been a complete whirlwind of learning, and data, and incredibly exciting moments where the huge value in what we were doing became strikingly apparent. (The one which especially springs to mind is where we were suddenly able to detect spikes in error messages in a specific location amongst a millions-of-message-a-second cloud of noise).

I am so excited to progress to the next level of my apprenticeship, and to continue immersing myself in the complex and exciting challenges that we solve every day here at endjin.

And if you're reading this and thinking that you'd find all this as exciting as I do, then send over a copy of you CV to hello@endjin.com! We're always on the lookout for new apprentices to join our team, and I cannot emphasise enough how great of an opportunity for learning and expanding the apprenticeship truly is!

Doodle of author celebrating a year as an Apprentice II!

FAQs

What are the advantages of reactive processing? Reactive processing at massive scale enables detection of meaningful patterns, like error message spikes in specific locations, within a cloud of noise. It can handle millions of events per second in real time, transforming raw data streams into actionable business insights that would be impossible to identify through traditional batch processing methods.
Why is blogging an effective method for consolidating technical learning during an apprenticeship? Writing 25 technical blogs over a year forces the apprentice to deeply understand and articulate complex topics ranging from C# internals and encryption to Azure services and software architecture. The act of explaining concepts in writing reveals gaps in understanding and creates a documented record of progress that might otherwise go unnoticed in day-to-day work.
What kind of projects do apprentices work on at endjin? Apprentices at endjin get involved in a wide variety of exciting projects, including building modern data processing pipelines that feed machine learning models, and reactive processing engines capable of handling millions of events per second. These projects span technologies from C# and Azure to data science and software architecture.
What is it like being an apprentice at endjin? The apprenticeship at endjin offers a huge variety of work across many different technology areas. Apprentices are encouraged to write technical blogs, present their work, and continuously develop their skills. The fast pace of learning means that looking back over a year reveals significant progress that might not be obvious in day-to-day work.
What distinguishes data processing pipelines that feed machine learning models from simpler data workflows? These modern pipelines must handle the full journey from raw data ingestion through transformation, feature engineering, and model training to produce real business insights. The complexity lies in maintaining data quality, handling scale, and ensuring the processed data actually enables meaningful machine learning predictions rather than just moving data between systems.

Carmel Eve

Software Engineer II

Carmel Eve

Carmel is a software engineer and LinkedIn Learning instructor. She worked at endjin from 2016 to 2021, focused on delivering cloud-first solutions to a variety of problems. These included highly performant serverless architectures, web applications, reporting and insight pipelines, and data analytics engines. After a three-year career break spent travelling around the world, she rejoined endjin in 2024.

Carmel has written many blog posts covering a huge range of topics, including deconstructing Rx operators, agile estimation and planning and mental well-being and managing remote working.

Carmel has released two courses on LinkedIn Learning - one on the Az-204 exam (developing solutions for Microsoft Azure) and one on Azure Data Lake. She has also spoken at NDC, APISpecs, and SQLBits, covering a range of topics from reactive big-data processing to secure Azure architectures.

She is passionate about diversity and inclusivity in tech. She spent two years as a STEM ambassador in her local community and taking part in a local mentorship scheme. Through this work she hopes to be a part of positive change in the industry.

Carmel won "Apprentice Engineer of the Year" at the Computing Rising Star Awards 2019.