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Showing all posts by James Bowkett
March 14, 2024 | Blog, Data Engineering, Platform Engineering
Watch the recording of our Technical Delivery Director, James Bowkett from the GOTO Copenhagen 2023 conference for his talk ‘The 12 Factor App For Data’
July 5, 2023 | Blog, Platform Engineering
Watch the recording of our Technical Delivery Director, James Bowkett from the PlatformCon Conference 2023 in his talk The Golden Age of the Platform.”
February 1, 2023 | Blog, Data Analysis, Neo4j
As data becomes ubiquitous and deeply interconnected, tracing where who or which system that data comes from – its lineage – will create bigger problems and opportunities for us on the horizon. Watch the recording of James Bowkett talk from NODES 2022 – Neo4j Online Developer Education Summit 202 on ‘Tracing Your Data’s DNA.’
May 26, 2022 | Data Analysis, Data Engineering
As data becomes ubiquitous and deeply interconnected, tracing where who or which system that data comes from – its lineage – will create bigger problems and opportunities for us on the horizon. Watch the recording of James Bowkett’s talk from Devoxx UK on ‘Tracing Your Data’s DNA’
February 11, 2022 | AWS, Cloud, GCP, Kubernetes, Microservices, Open Source, Software Consultancy
Serverless functions are easy to install and upload, but we can’t ignore the basics. This article looks at different strategies related to testing serverless functions.
April 2, 2020 | Machine Learning
Recent years have seen many companies consolidate all their data into a data lake/warehouse of some sort. Once it’s all consolidated, what next?
Many companies consolidate data with a field of dreams mindset – “build it and they will come”, however a comprehensive data strategy is needed if the ultimate goals of an organisation are to be realised: monetisation through Machine Learning and AI is an oft-cited goal. Unfortunately, before one rushes into the enticing world of machine learning, one should lay more mundane foundations. Indeed, in data science, estimates vary between 50% to 80% of the time taken is devoted to so-called data-wrangling. Further, Google estimates ML projects produce 5% ML code and 95% “glue code”. If this is the reality we face, what foundations are required before one can dive headlong into ML?
