InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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How Do You Distribute Your Database over Hundreds of Edge Locations?
Erwin van der Koogh explains a new model that Cloudflare has developed to distribute a database over hundreds of locations, and where it could go next.
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Panel: Challenges & Opportunities of the Modern Financial Institutions
Lucas Cavalcanti, Dio Rettori, and Camilla Crispim discuss the challenges and opportunities of modern financial institutions.
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Robust Foundation for Data Pipelines at Scale - Lessons from Netflix
Jun He and Harrington Joseph share their experiences of building and operating the orchestration platform for Netflix’s big data ecosystem.
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Data Mesh: an Architectural Deep Dive
Zhamak Dehghani introduces the architecture of new Data Mesh concepts such as data products, as well as the planes of the data platform in support of computational governance and distribution.
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From Batch to Streams: Building Value from Data In-Motion
Ricardo Ferreira discusses the risks of designing siloed-based systems and how streaming data architectures can become a solution to address competitiveness.
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Designing Better ML Systems: Learnings from Netflix
Savin Goyal shares lessons learned by Netflix building their ML infrastructure, and some of the tradeoffs to consider when designing or buying a machine learning system.
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Data-driven Development in the Automotive Field
Toshika Srivastava offers insight into how they in the automotive field are developing products with data and what their challenges are.
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Designing IoT Data Pipelines for Deep Observability
Shrijeet Paliwal discusses how Tesla deals with large data ingestion and processing, the challenges with IoT data collecting and processing, and how to deal with them.
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Evolving Analytics in the Data Platform
Blanca Garcia-Gil discusses the BBC’s analytics platform architecture, the failure modes they designed for, and the investigation of the new unknowns and how they automated them away.
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Scaling & Optimizing the Training of Predictive Models
Nicholas Mitchell presents the core building blocks of an entire toolchain able to deal with challenges of large amounts of data in an industrial scalable system.
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Using DevEx to Accelerate GraphQL Federation Adoption @Netflix
Paul Bakker and Kavitha Srinivasan discuss how they made certain Build vs Buy (open source) trade-offs and the socio-technical aspects of working with many teams on a single shared schema.
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Building Latency Sensitive User Facing Analytics via Apache Pinot
Chinmay Soman discusses how LinkedIn, Uber and other companies managed to have low latency for analytical database queries in spite of high throughput.
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