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Episode 433: Jay Kreps on ksqlDB : Software Engineering Radio

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It makes it easier to get correct results and reason about what happens if the machine fails in the middle of processing something. Um, but you do trade off, you know, a little bit of flexibility in, in how you, how you write that versus the low-level read and write. And then one level up from that, uh, I think is, is ksqlDB. So the analogy you can use is, you know, uh, if you've ever used one of these key value interfaces like rocks DB itself, you know, it's kind of very flexible and allowing you to work with data at a low level, um, probably more so than a SQL interface, but it's actually a lot more work for kind of simple stuff, uh, that you might want to do that then using a SQL database.


AIhub coffee corner – rethinking AI education

AIHub

This month, we discuss AI education. Joining the discussion this time are: Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), Holger Hoos (Leiden University) and Oskar von Stryk (Technische Universität Darmstadt). Sabine Hauert: As we are starting the new term, the question is how should we do AI education and what should students be learning? Thinking more broadly, how should we rethink AI education for the general population? There will be huge swaths of the public that will need to gain an understanding of AI, or be trained in the use of AI.


3 machine learning best practices to use in IoT projects

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In an ideal world, organizations could easily plan machine learning for IoT with the right technical experts, and components would perfectly integrate with each other. Unfortunately, no matter how well a team researches IoT machine learning before it starts development, some of their assumptions will be proven wrong and they will face unexpected challenges. During her IoT Tech Expo 2020 presentation, "Building Machine Learning Products -- a Best Practice Approach," Jenn Gamble, data science practice lead at Very, identified the required skills to implement machine learning with IoT and how teams can adopt best practices, approach software development and handle unexpected difficulties. "A lot of the data science development lifecycle is actually very different than what the Agile software development lifecycle is. A lot of those hard-won best practices from software engineering, the data science community was not always aware of, or, at least, not fully embracing or benefiting from," Gamble said.


Data Alone Is Not Enough: The Evolution of Data Architectures

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Data, data, data – it's long been a buzzword in the industry, whether big data, streaming data, data analytics, data science, even AI & machine learning -- but data alone is not enough: it takes an entire system of tools and technology to extract value from data. A multibillion dollar industry has emerged around data tools and technologies. And with so much excitement and innovation in the space: how exactly do all these tools fit together? This podcast – a hallway style conversation between Ali Ghodsi, CEO and Founder of Databricks, and a16z general partner Martin Casado – explores the evolution of data architectures, including some quick history, where they're going, and a surprising use case for streaming data, as well as Ali's take on how he'd architect the picks and shovels that handle data end-to-end today.


Proceedings of the 4th ACM SIGSOFT International Workshop on Machine-Learning Techniques for Software-Quality Evaluation

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Welcome to the fourth edition of the workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE 2020) to be held virtually on November 16, 2020, co-located with the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2020).


Microsoft and Salesforce ranked leaders in fiercely competitive low-code space

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Bengaluru: Microsoft and Salesforce have been ranked by GlobalData as leaders in the low-code market segment after causing a tectonic shift in this market with the automation capabilities within its Power Platform. Low-code is a software development approach that requires little to no coding in order to build applications and processes. The low-code market segment has become fiercely competitive, and Microsoft's Power Automate is expected, in itself, to be highly competitive when coupled with Azure's backend integration and storage service. "Low-code enabled artificial intelligence (AI) and automation innovations, coupled with the cloud, have created a culture of data ubiquity where data from virtually any source can be accessed and integrated into modern apps," Charlotte Dunlap, Principal Analyst at GlobalData, said. However, she said that enterprises are prioritising modern software delivery initiatives through these development platforms.


How I became a Software Developer during the pandemic without a degree or a bootcamp

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In 2018 I was depressed and unmotivated, I thought of myself as a failure and I thought I was too dumb to finish my degree or learn anything at all, I had no direction in life and just wanted everything to be over. Two years later, one spent working abroad and another dedicated to studying, I have a completely different perspective about myself and I just started my new exciting developer job on Monday. It took a lot of courage (and argumentations to convince my parents) to leave my university after three years of studies to accept a job in a Lisbon without knowing anyone nor the language but it was a wonderful experience that helped me find myself. Again it took even more grit and determination to leave Lisbon and start studying again, but I did it because I knew my dream was to become a programmer. I have no expertise in psychology and the best advice I have if you are in a dark place is to seek professional help, but I know what it feels to be lost and I want to help anyone that shares my same dream by writing this article offering actionable advice on how to achieve a career in software development.


Is AI the future of Testing? – Software Testing News

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Software development has already undergone an important journey from its beginnings to test automation and continuous testing. As time progresses, however, it is certain that testing will have to evolve as well. With digital transformations and the drive towards DevOps, automated testing is now at the heart of software testing and has taken the lead in the development process. Artificial Intelligence (AI) seems to be the future of testing. AI has created high hopes in software testing and test automation and the advancements in AI allow organizations to transform their processes and make progress.


Council Post: Hyperautomation And What It Means For Your Business

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By Riccardo Conte, serial entrepreneur and founder of Virtus Flow, a no-code digital process automation platform that streamlines work through process automation. Hyperautomation is a concept introduced by Gartner. However, the idea behind it has been in practice by companies leading the digital transformation race for some time. Forrester refers to it as digital process automation (DPA). Gartner identified it as one of the 10 trends of 2020 and predicted that adopting these technologies will reduce as much as 69% of the manager's workload by 2024.


Why software developers might be obsolete by 2030

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In 1930, John Maynard Keynes predicted we'd be having 15-hour workweeks by the end of the century. But by the time it was 2013, it was clear the great economist had gotten something wrong. Welcome to the era of bullshit jobs, as anthropologist David Graeber coined it. Since the 1930s, whole new industries have sprung up, which don't necessarily add much value to our lives. Graeber would probably call most jobs in software development bullshit.