1. The foundations of Canva’s continuous data platform with Snowpipe Streaming
How does Canva handle real-time data for 200 million users?
In this article author says that building a strong data culture takes more than tools—it takes craft, collaboration, and innovation. Netflix’s Analytics Engineering Summit highlights cutting-edge dashboard design, deploying analytics APIs, and advancing real-time decision-making. Curious about how they optimize for impact while navigating complex technical challenges? This post is a treasure trove of insights into scaling data engineering with creativity and precision.
https://www.canva.dev/blog/engineering/snowpipe-streaming/
2. The Unstructured Data Landscape
How is the unstructured data landscape shaping the future of data-driven decision-making?
In this article, Eric Flaningam explores how the rise of unstructured data, from text and images to geospatial data, is driving innovation in AI infrastructure. Discover why mastering unstructured data is key to unlocking the next generation of intelligent applications. If you’re curious about where data infrastructure is headed, this is a must-read.
3. Improving Recruiting Efficiency with a Hybrid Bulk Data Processing Framework
How can a hybrid bulk data processing framework transform recruiting efficiency?
In this article, Aditya Hegde and Saumi Bandyopadhyay explore how LinkedIn’s hybrid bulk data processing framework tackles complex data ownership challenges. Discover how this system achieved a 99.9% success rate, reduced customer escalations by 78%, and transformed recruiting workflows with enhanced scalability and observability.
4. How the Apache Arrow Format Accelerates Query Result Transfer
How does Apache Arrow accelerate query result transfers?
This blog discusses that traditional data transfer protocols, not just computation, are a major bottleneck. Apache Arrow eliminates costly serialization overhead, enabling query result transfers that are up to 38x faster. If you work with large-scale analytics, this post is a must-read.
https://arrow.apache.org/blog/2025/01/10/arrow-result-transfer/
5. From RAG to Richness: How Ramp Revamped Industry Classification
How did Ramp transform industry classification using RAG?
In this blog it is discussed that industry classification is messy, but it doesn’t have to be. Ramp replaced its fragmented, homegrown system with a Retrieval-Augmented Generation (RAG) model, achieving greater accuracy, consistency, and control. If you're curious about how AI can bring structure to unstructured classification problems, this is a must-read.
https://engineering.ramp.com/industry_classification
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