Simon Späti

JULY 28, 2024

A Vim-Inspired Approach to Efficient Note Management with Obsidian and Markdown

Management 361

Management 361

databricks

JULY 28, 2024

The transformative potential of artificial intelligence (AI) is undeniable. From productivity efficiency, to cost savings, and improved decision-making across all industries, AI is.

Data 116

Data 116

Data Engineering Weekly

JULY 28, 2024

Meta: Introducing Llama 3.1: Our most capable models to date Probability one of the hottest announcements this week is Llama 3.1 release - the first-ever open-sourced frontier AI model competitive with leading foundation models across a range of tasks, including GPT-4, GPT-4o, and Claude 3.5 Sonnet. The Llama3 herd of models is an insightful paper that helps one deeply understand the foundational model.

Data Engineer 109

Data Engineer Data Engineering Engineering Database-centric 109

Zalando Engineering

JULY 28, 2024

"What’s happening inside my application?" - an age-old question bothering anyone who deploys a software service. Packaging source code for an application makes it a black box for its users who can only interact with it through explicitly available APIs. Fortunately, we’ve had several developments in the field of observability in recent years that help us peek into this black box and react to anomalies.

Bytes 63

Bytes Engineering Software Engineering Software Engineer 63

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In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate

Data Pipeline