Welcome to another episode of Data Engineering Weekly Radio. Ananth and Aswin discussed a blog from BuzzFeed that shares lessons learned from building products powered by generative AI. The blog highlights how generative AI can be integrated into a company's work culture and workflow to enhance creativity rather than replace jobs. BuzzFeed provided their employees with intuitive access to APIs and integrated the technology into Slack for better collaboration.
Some of the lessons learned from BuzzFeed's experience include:
Getting the technology into the hands of creative employees to amplify their creativity.
Effective prompts are a result of close collaboration between writers and engineers.
Moderation is essential and requires building guardrails into the prompts.
Demystifying the technical concepts behind the technology can lead to better applications and tools.
Educating users about the limitations and benefits of generative AI.
The economics of using generative AI can be challenging, especially for hands-on business models.
The conversation also touched upon the non-deterministic nature of generative AI systems, the importance of prompt engineering, and the potential challenges in integrating generative AI into data engineering workflows. As technology progresses, it is expected that the economics of generative AI will become more favorable for businesses.
Moving on, We discuss the importance of on-call culture in data engineering teams. We emphasize the significance of data pipelines and their impact on businesses. With a focus on communication, ownership, and documentation, we highlight how data engineers should prioritize and address issues in data systems.
We also discuss the importance of on-call rotation, runbooks, and tools like PagerDuty and Airflow to streamline alerts and responses. Additionally, we mention the value of having an on-call handoff process, where one engineer summarizes their experiences and alerts during their on-call period, allowing for improvements and a better understanding of common issues.
Overall, this conversation stresses the need for a learning culture within data engineering teams, focusing on building robust systems, improving team culture, and increasing productivity.
Finally, Ananth and Aswin discuss an article about adopting dimensional data modeling in hyper-growth companies. We appreciate the learning culture and emphasize balancing speed, maturity, scale, and stability.
We highlight how dimensional modeling was initially essential due to limited computing and expensive storage. However, as storage became cheaper and computing more accessible, dimensional modeling was often overlooked, leading to data junkyards. In the current landscape, it's important to maintain business-aware domain-driven data marts and acknowledge that dimensional modeling still has a role.
The conversation also touches upon the challenges of tracking slowly changing dimensions and the responsibility of data architects, engineers, and analytical engineers in identifying and implementing such dimensions. We discuss the need for a fine balance between design thinking and experimentation and stress the importance of finding the right mix of correctness and agility for each company.