Chris Riccomini, an engineer with a rich background at LinkedIn and WePay, shares insights on building SlateDB and the intricacies of data infrastructure. He recounts his journey from PayPal to LinkedIn, detailing the shifts in technology and engineering challenges. The discussion flows into writing a collaborative handbook for new software engineers during the pandemic. Riccomini also delves into learning Rust, the role of AI in programming, and strategies for staying relevant in tech, emphasizing the future of data applications.

Chris Riccomini, an expert in stream processing and LSMs, dives into the evolution of streaming systems, highlighting the challenges developers face. He critiques SQL's limitations in this space and emphasizes the need for better API designs. The discussion also touches on the impact of Rust on usability and efficiency, particularly in embedded libraries. Chris shares insights about his exciting project involving log-structured merge trees on object storage, and the future of data systems with a focus on composable databases and the importance of metadata in AI.

Data systems experts Wes McKinney, Pedro Pedreira, Chris Riccomini, and Ryan Blue discuss the concept of composable data systems, the challenges and incentives for composable components, specialization and modularity in data workloads, and the efficiency and common layers in data management systems. They also explore the evolution of data system composability, exciting new projects in data systems, and the challenges of standardizing APIs.

Chris Riccomini, ex-Distinguished engineer at Wepay and co-creator of Apache Samza, discusses the evolution of data systems, the rise of Arrow, and the future trends in database systems. Topics include domain-specific databases, object storage for edge systems, developer ergonomics, and the shift towards specialized Postgres-compatible databases.

Chris Riccomini, a data engineer with experience at PayPal and LinkedIn, discusses building data infrastructure, lessons from open-source development, and the future of data engineering. Topics include scaling Hadoop clusters, choosing big data solutions, strategies for early-stage startups, and the importance of running models and microservices on the same continuous delivery stack. Chris also talks about creating Apache Samza, evangelizing open-source projects, and the evolution of data infrastructure at WePay. The podcast explores mastering interviewing skills in data engineering hiring, building Apache SAMHSA, the evolution of Apache Airflow, designing principles of Apache Kafka, and the future of data engineering predictions and industry insights.