Data Engineering Podcast

MAY 5, 2024

Summary Artificial intelligence has dominated the headlines for several months due to the successes of large language models. This has prompted numerous debates about the possibility of, and timeline for, artificial general intelligence (AGI). Peter Voss has dedicated decades of his life to the pursuit of truly intelligent software through the approach of cognitive AI.

Building 147

Building Data Lake High Quality Data Machine Learning 147

Knowledge Hut

MAY 5, 2024

In the dynamic world of business, the only constant thing is change. Hence, each organization, regardless of its size, market share, product, or service range, location or brand has to learn to adapt quickly to the ever-changing corporate reality. The modern term of this process is referred to as business agility and often, this is one of the most valuable assets a company may have.

Certification 98

Certification Project Management Systems 98

Confluent

MAY 5, 2024

A lookback at all the exciting happenings at our first-ever Kafka Summit Bangalore—and some key announcements, including Confluent Platform for Apache Flink.

Kafka 81

Kafka 81

Data Engineering Weekly

MAY 5, 2024

Ken Liu: Machine Unlearning in 2024 One of the insightful articles is about the growing adoption of one large language model and the challenge it brings to machine unlearning. The motivation for Machine Unlearning is critical from the privacy perspective and for model correction, fixing outdated knowledge, and access revocation of the training dataset.

Data Engineering 67

Data Engineering Data Engineer Engineering Google Cloud 67

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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