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How NVIDIA Uses Apache Arrow

20 engineering articles about Apache Arrow from NVIDIA's engineering team

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NVIDIA's Sirius, an open-source GPU-native SQL engine, has set a new performance record on ClickBench, enhancing DuckDB with GPU-accelerated analytics.
Xiangyao Yu
6 min read
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The article discusses how to read JSON Lines data using NVIDIA's cuDF library, achieving performance improvements of up to 100 times faster than traditional pandas methods.
Karthikeyan Natarajan
10 min read
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The article discusses how RAPIDS AI can accelerate predictive maintenance in manufacturing by leveraging advanced data analytics to minimize downtime and optimize maintenance schedules.
Amarnath Mohan
11 min read
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This article provides a comprehensive guide on encoding and compression techniques for string data in the Parquet format using RAPIDS.
Gregory Kimball
9 min read
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The article introduces five new technical courses offered by NVIDIA aimed at enhancing skills in AI and data science.
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The article discusses the use of nested data types in RAPIDS libcudf for optimizing ETL workflows.
Gregory Kimball
10 min read
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The article discusses how GPU-accelerated data analytics can enhance machine learning (ML) projects by improving speed and scalability.
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The article discusses the integration of distributed deep learning with Apache Spark 3. 4, highlighting new built-in APIs for both distributed model training and inference.
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The article discusses Accelerated WEKA, a project that integrates GPU acceleration into the WEKA machine learning software using RAPIDS libraries.
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This article discusses a novel approach to analyzing data stored in Apache Cassandra using GPU acceleration through the RAPIDS ecosystem.
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This article discusses the importance of efficient memory layouts and memory pools in machine learning frameworks to enhance interoperability and performance.
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The article discusses the advancements in Natural Language Processing (NLP) and text processing using RAPIDS, emphasizing performance improvements in string processing with cuDF and cuML.
Vibhu Jawa
6 min read
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This article is the second part of a series on building deep learning-powered recommender systems, focusing on the application of deep learning techniques to enhance recommendation quality.
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This article serves as an introductory guide to the RAPIDS ecosystem, focusing on GPU-accelerated DataFrames in Python through cuDF.
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This article provides an in-depth look at how to leverage machine learning techniques to detect fraud, specifically through the lens of the Kaggle IEEE CIS Fraud Detection competition.
Carol McDonald
20 min read
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The article announces the open beta of NVIDIA NVTabular, highlighting its new multi-GPU support and optimized data loaders for deep learning recommenders.
Vinh Nguyen
11 min read
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The article discusses the significance of deep learning-based recommender systems in enhancing personalized online experiences across various industries.
Nefi Alarcon
2 min read
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The article discusses how NVIDIA's RAPIDS Accelerator for Apache Spark enables GPU acceleration for data processing tasks in Apache Spark 3. 0.
Carol McDonald
9 min read
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The article discusses the use of the RAPIDS VM Image on Google Cloud Platform, highlighting its capabilities for accelerating data science workflows through GPU-accelerated libraries.
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NVIDIA announced RAPIDS, a suite of open-source software libraries designed to accelerate end-to-end data science and analytics pipelines entirely on GPUs.
Nefi Alarcon
1 min read
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