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Octopai Acquisition Enhances Metadata Management to Trust Data Across Entire Data Estate

Cloudera

It leverages knowledge graphs to keep track of all the data sources and data flows, using AI to fill the gaps so you have the most comprehensive metadata management solution. This guarantees data quality and automates the laborious, manual processes required to maintain data reliability.

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Building a Custom PDF Parser with PyPDF and LangChain

KDnuggets

It will be used to extract the text from PDF files LangChain: A framework to build context-aware applications with language models (we’ll use it to process and chain document tasks). It will be used to process and organize the text properly.

Building 102
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Bridging the Gap: New Datasets Push Recommender Research Toward Real-World Scale

KDnuggets

Its static snapshot and lack of detailed metadata limit modern applicability. While impressive in volume, it offers minimal metadata and prioritizes click-through rate (CTR) over recommendation logic. Netflix Prize A landmark dataset in recommendеr history (~100M ratings), though now dated. Yelp Open Dataset Contains 8.6M

Datasets 123
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Interesting startup idea: benchmarking cloud platform pricing

The Pragmatic Engineer

Results are stored in git and their database, together with benchmarking metadata. Code and raw data repository:   Version control: GitHub Heavily using GitHub Actions for things like getting warehouse data from vendor APIs, starting cloud servers, running benchmarks, processing results, and cleaning up after tuns.

Cloud 330
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Foundation Model for Personalized Recommendation

Netflix Tech

The impetus for constructing a foundational recommendation model is based on the paradigm shift in natural language processing (NLP) to large language models (LLMs). To harness this data effectively, we employ a process of interaction tokenization, ensuring meaningful events are identified and redundancies are minimized.

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Directory Tables, Python UDF and Streams for PDF Processing

Cloudyard

Snowflake provides powerful tools such as directory tables , streams , and Python UDFs to seamlessly process these files, making it easy to extract actionable insights. Pipeline Overview The pipeline consists of the following components: Stage : Stores PDF files and tracks their metadata using directory tables. PDF Extract Process 3.Automating

Python 52
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Modern Data Governance: Trends for 2025

Precisely

Key Takeaways: Prioritize metadata maturity as the foundation for scalable, impactful data governance. Recognize that artificial intelligence is a data governance accelerator and a process that must be governed to monitor ethical considerations and risk. Tools are important, but they need to complement your strategy.