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‘Security as code’ demands proactive DevSecOps

Dynatrace

This is known as “security as code” — the constant implementation of systematic and widely communicated security practices throughout the entire software development life cycle. Willie Hicks discussed how Dynatrace Application Security helps organizations adopt a security-as-code approach. The security challenges of DevOps.

Code 162
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C++ safety, in context

Sutter's Mill

I chair the ISO C++ standards committee and I work for Microsoft, but these are my personal opinions and I hope they will invite more dialog across programming language and security communities. When I say “safety” unqualified here, I mean programming language safety, which benefits both software security and software safety.

C++ 139
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SKP's Java/Java EE Gotchas: Clash of the Titans, C++ vs. Java!

DZone

This begins not only in designing the algorithm or coming out with efficient and robust architecture but right onto the choice of programming language. Recently, I spent some time checking on the Performance (not a very detailed study) of the various programming languages. Ahem, Slow!

Java 207
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Ensuring Performance, Efficiency, and Scalability of Digital Transformation

Alex Podelko

Looking at the program I have the same problem as I always had with CMG conferences – how could I attend all the sessions I want considering that we have multiple tracks? So you need to understand what is going on there – and Debbie is definitely an authority in that area. day program. Something you shouldn’t miss.

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Fearing the Wrong Thing

O'Reilly

Matt Welsh has been talking and writing about the end of programming as such. He’s asking whether large language models eliminate programming as we know it, and he’s excited that the answer is “yes”: eventually, if not in the immediate future. AIs generate incorrect code, and that’s not going to end soon.

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What is a data lakehouse? Combining data lakes and warehouses for the best of both worlds

Dynatrace

While data lakes and data warehousing architectures are commonly used modes for storing and analyzing data, a data lakehouse is an efficient third way to store and analyze data that unifies the two architectures while preserving the benefits of both. These include application programming interfaces, streaming, and more.

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Orchestrating Data/ML Workflows at Scale With Netflix Maestro

The Netflix TechBlog

We want users to rely on shared templates and reuse their workflow definitions across their team, saving time and effort on creating the same functionality. Maestro high level architecture In Maestro, a workflow is a DAG (Directed acyclic graph) of individual units of job definition called Steps. But sometimes, it is not efficient.

Java 202