Seattle Data, AI & Security Meetup 2024

Data engineering teams encounter challenges like data quality issues and pipeline failures, especially in enterprise environments. Addressing this, our approach combines the linguistic prowess of models like GPT-4 with data engineering tasks. We autonomously identify and rectify data quality issues, transform anomaly detection paradigms, and automate recovery tasks. Our methodology achieves reduced resolution times, fine-tuned anomaly detectors and minimized downtime. Empirical evidence showcases enhanced metrics such as reduced MTTR and fewer false positives, advocating a future where AI plays a pivotal role in on-call data engineering.

Innovative Use of GPT-4:

Leveraging large language models like GPT-4 can revolutionize traditional data engineering tasks and offer autonomous solutions.

Improved Anomaly Detection:

By analyzing historical data, our system provides optimized thresholds for anomaly detectors, balancing alert sensitivity and accuracy.

Efficient Issue Resolution:

The approach significantly reduces resolution times, allowing engineers to focus on intricate challenges.

Our case studies validate improved metrics and overall system stability, suggesting tangible benefits of integrating AI in on-call data engineering.