InfoQ Homepage Data Content on InfoQ
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Adaptive Recommenders in the Real World: Inference, Evals, and System Design
Mallika Rao shares why building adaptive recommendation systems requires shifting focus from isolated ML models to real-time feedback loops, retrieval freshness, and production-level constraints.
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From AI Agent Demo to Production: Automated Testing and Evaluation
Zhou Yu explains why 95% of AI agents fail to reach production and shares how simulation-driven evaluation, synthetic users, and automated CI/CD testing unlock enterprise deployment.
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Autonomous Data Products for the Autonomous Era: Rethinking Data Architecture for GenAI
Jörg Schad discusses how to bridge the gap between prototypes and production by standardizing data access for GenAI, using autonomous data products to prevent context rot and ensure safety.
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Accelerating Netflix Data: a Cross-Team Journey from Offline to Online
Raj Ummadisetty and Ken Kurzweil explain how Netflix transitioned its key-value abstraction from stateless to stateful, reducing data deployment time by 90% and infrastructure costs by 70%.
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Beyond Speed Limits: Exploring the Performance Power of Valkey
Viktor Vedmich explains how to achieve sub-millisecond application latency using Valkey, an open-source, high-performance in-memory fork of Redis supported by AWS.
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Architecting a Centralized Platform for Data Deletion at Netflix
Netflix Engineers Vidhya Arvind and Shawn Liu discuss the pillars of safe, large-scale data deletion. They explain strategies to eliminate data ghosts and manage tombstone resource contention.
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Are You Missing a Data Frame? The Power of Data Frames in Java
Vladimir Zakharov discusses the power of DataFrames in Java. He compares implementations like DataFrame-EC and Tablesaw against Python’s pandas, focusing on performance and memory efficiency.
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Powering Enterprise AI Applications with Data and Open Source Software
Francisco Javier Arceo explored Feast, the open-source feature store designed to address common data challenges in the AI/ML lifecycle, such as feature redundancy, and low-latency serving at scale.
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Reliable Data Flows and Scalable Platforms: Tackling Key Data Challenges
Matthias Niehoff discusses bridging the gap between application and data engineering. Learn to apply software engineering best practices, embrace boring technologies, and simplify architecture.
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Achieving Precision in AI: Retrieving the Right Data Using AI Agents
Adi Polak discusses achieving precision in GenAI by moving beyond RAG to Agentic RAG. She details agent patterns, feedback loops, and using data streaming architectures to scale real-time AI.
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The Data Backbone of LLM Systems
Paul Iusztin discusses the evolution of AI engineering, highlighting the shift from model training to foundational models. He shares insights on scalable LLM systems and optimizing RAG.
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Efficient Incremental Processing with Netflix Maestro and Apache Iceberg
Jun He discusses how to use an IPS to build more reliable, efficient, and scalable data pipelines, unlocking new data processing patterns.
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