Date(s) - 09/06/2022
20:00 - 22:00
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Software practitioners work to make their systems reliable. We hear teams boasting of having four or five 9s of uptime. This is not the case for Data Services. Data is not often 99.999% reliable. Systems are often out of date or out of sync. Pipelines and automated jobs fail to run. And, sometimes, the data sources are just not accurate. All these situations are examples of Data Downtime and lead to misleading results and false reporting.
Data Reliability Engineering is the practice of building resilient systems. By treating data systems as an engineering problem we can borrow tools and practices from SRE to build better systems. Together let’s explore how to take this natural extension of data engineering to make our data systems stronger and more reliable.
This talk uses a case study from a cross-team collaboration effort at LinkedIn. The work was intended (and succeeded) to address specific knowledge, process, and skill gaps between teams via the methodology of a learning team approach.
With this specific case study as a focal point, the talk will connect sources from the wider literature about organizational learning and learning teams to highlight processes, organizational structures, and skills that can be used to foster a healthy work environment with inclusivity and inter-team camaraderie while also achieving important business metrics and getting answers for that executive!
Participants will learn: – principles guiding the implementation of learning teams, – strengths and applicability for learning teams, – how to foster a more humane and effective workplace by appreciating the importance of “work as done” above work as imagined.
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