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Tools: Great Expectations, Soda Core, Deequ
Data quality validation is no longer an afterthought but a core component of modern data pipelines. This article explores three leading open-source frameworks — Great Expectations, Soda Core, and Deequ — that automate data validation, profiling, and continuous monitoring. We compare their architecture, integration capabilities, and practical strengths through empirical examples and real-world use cases…
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Empirical: throughput comparison of streaming architectures
This empirical analysis benchmarks the throughput of modern streaming architectures, comparing Apache Kafka, Apache Pulsar, Redpanda, and Flink-based pipelines. Using standardized workloads and realistic latency constraints, we dissect their design trade-offs, operational costs, and observed performance under varied load conditions.
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Expert: high-dimensional clustering
High-dimensional clustering has become a cornerstone of advanced data analysis in 2025, bridging unsupervised learning, representation learning, and manifold geometry. This post explores the theory and practice of clustering in high-dimensional spaces — from the curse of dimensionality to cutting-edge techniques like subspace clustering, contrastive learning embeddings, and scalable approximate algorithms used in production by…
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Topics Everyone Is Talking About No270
Long-term safety confirmed: 4-year mortality data after mRNA vaccination • AV1 wins an Emmy open video codec that transformed the web • LISP Style Design • PostgreSQLs 1600-column limit why more isnt always better
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Topics Everyone Is Talking About No268
Django 6 • We Gave 5 LLMs 100K to Trade Stocks for 8 Months • Lookup Table vs. Enum Type: Which Wins in PostgreSQL?
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