Data engineering
Our articles offer strategies to keep your systems lean, fast, and ready for tomorrow’s demands. From pipelines to integration, we provide insights that let you reshape how your data moves and performs at scale.
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This article focuses on the technical and operational issues that most often break web data collection in projects.
To understand what actually goes wrong, we analyzed 82 discussion threads (questions, issues, and conversations) from Stack Overflow, Reddit, GitHub Issues, Hacker News, niche and regional platforms.
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In search of the best tool to extract data from PDF?
We benchmarked Amazon Textract against Anthropic Claude to extract specific data fields from the first two pages of PDF files.
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In 2021, California courts removed date-of-birth information from online search portals to address privacy concerns. For background screening companies, that single change broke long-standing identity matching workflows overnight.
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Back in the early 2000s, Martin Odersky—who had already contributed to Java’s generics—wanted to create something to address the shortcomings of Java. So he built Scala.
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You’re deep into an ETL project, and suddenly, data discrepancies start to surface.
Reports don’t match up, and dashboards look off.
Frustrating, right?
...Trending articles
Amazon Textract vs Anthropic: PDF to JSON Accuracy, Cost, and Scale
