DE & ML Digest – Telegram
DE & ML Digest
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Collection of all articles on Data Engineering and Machine Learning

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📌 Mozilla fixes Firefox zero-day actively exploited in attacks

Hey there! Mozilla has recently patched a zero-day vulnerability in Firefox that was being actively exploited in attacks. This fix is super important for keeping your browsing experience safe and secure. Stay updated and make sure your Firefox browser is up to date to protect yourself against potential threats. Stay safe online, folks! 🛡🔒

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📌 Amazon EMR on EC2 cost optimization: How a global financial services provider reduced costs by 30%

A global financial services provider reduced their Amazon EMR, EC2, and S3 costs by over 30% monthly by optimizing their setup on AWS. They migrated Apache Hadoop clusters to Amazon EMR on EC2 and collaborated with DevOps teams to implement cost-saving strategies. By enforcing auto-termination, optimizing scaling configurations, using Spot Instances, and fine-tuning Spark and HBase setups, they achieved significant cost reductions. A financial hackathon further helped identify and implement data-driven cost optimization strategies, leading to a 30% reduction in monthly costs over 3 months.

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📌 The Ultimate Guide to Error Handling in Python

The article discusses Python error handling, comparing "Look Before You Leap" (LBYL) and "Easier to Ask Forgiveness than Permission" (EAFP) methods. EAFP, using exceptions, is often preferred. It categorizes errors as new or bubbled-up, and recoverable or non-recoverable, providing strategies for handling each type. The key takeaway is to handle errors appropriately based on their nature to write more effective error handling code in Python.

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📌 Devs gaining little (if anything) from AI coding assistants

The article discusses how developers might not be benefiting much from AI coding assistants. Despite the hype around these tools, developers may not see significant improvements in productivity or code quality. The Reddit post delves into the topic, sharing insights and opinions on whether AI coding assistants truly add value to developers' workflows. It's a reminder to carefully evaluate the actual impact of technology tools before fully embracing them in your coding process. Remember, not every trendy tool may bring the expected benefits!

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📌 How GenAI will impact data engineering

GenAI is gaining popularity in data engineering, offering benefits like faster development and reduced maintenance. By using GenAI for tasks like code generation and quality checks, data engineers can focus on key business aspects. GenAI can also help in troubleshooting issues like false positives and out-of-memory errors, improving mental health and lowering maintenance costs. It's important for data engineers to embrace GenAI, stay updated on AI advancements, and consider learning new skills like building online systems or adopting more analytical roles. The future of data engineering involves leveraging AI tools like LLMs, exploring AI infrastructure, and adapting to changes in the field.

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