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Russ Miles – Chaos Engineer Thought Leader & Author of multiple books including “Antifragile Software: Building Adaptable Software with Microservices”. Chris Richardson – Developer & Architect, Author of “POJOs in Action“ and “Microservices patterns“, Founder at Eventuate.
DataEngineering: Building your BI infrastructure from scratch by Estefania Rabadan Martinez – DataEngineer Lead at Hotjar. Most of us have heard of Trunk Based Development, Continuous Deployment and Microservices. Maybe even convinced our stakeholders it was time to put them into practice.
The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. process data in real time and run streaming analytics. This list includes but is not limited to C++, Python , Go,NET , Ruby, Node.js , Perl, PHP, Swift , and more.
Gone are the days of a web app being developed using a common LAMP (Linux, Apache, MySQL, and PHP ) stack. That’s why applications that are designed to run as a set of discrete microservices benefit the most from containers. Those who work in IT may relate to this shipping-container metaphor. Common Docker use cases.
Building applications with RAG requires a portfolio of data (company financials, customer data, data purchased from other sources) that can be used to build queries, and data scientists know how to work with data at scale. Dataengineers build the infrastructure to collect, store, and analyze data.
They might be adding AI-driven features or moving it to the cloud and orchestrating it with Kubernetes, but they’re not likely to drop React (or even PHP) to move to the latest cool framework. For several years, microservices has been one of the most popular topics in software architecture, and this year is no exception.
While we like to talk about how fast technology moves, internet time, and all that, in reality the last major new idea in software architecture was microservices, which dates to roughly 2015. Microservices saw a 20% drop. Many developers expressed frustration with microservices during the year and argued for a return to monoliths.
A quick look at bigram usage (word pairs) doesn’t really distinguish between “data science,” “dataengineering,” “data analysis,” and other terms; the most common word pair with “data” is “data governance,” followed by “data science.” That’s no longer true. Programming Languages.
We’ll be working with microservices and serverless/functions-as-a-service in the cloud for a long time–and these are inherently concurrent systems. The biggest challenge facing operations teams in the coming year, and the biggest challenge facing dataengineers, will be learning how to deploy AI systems effectively.
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