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stephhazlitt committed Aug 9, 2024
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Expand Up @@ -232,7 +232,7 @@ <h1 class="title">Big Data in R with Arrow</h1>
<hr>
<p>🗓️ August 12th, 2024<br>
⏰ 09:00 - 17:00<br>
🏨 ADD-ROOM<br>
🏨 305 | Chelais<br>
✍️ <a href="http://pos.it/conf">pos.it/conf</a></p>
<hr>
<section id="workshop-overview" class="level3">
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46 changes: 16 additions & 30 deletions _site/search.json
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Expand Up @@ -1092,25 +1092,32 @@
"text": "We Assume\n\nYou know \nYou are familiar with the dplyr package for data manipulation \nYou have data in your life that is too large to fit into memory or sluggish in memory\nYou want to learn how to engineer your data storage for more performant access and analysis"
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"title": "Big Data in R with Arrow",
"section": "Posit Workbench: Login 🛠️",
"text": "Posit Workbench: Login 🛠️\n\nJoin Workbench via URL in the #workshop-arrow Discord channel\nSelect Posit Workbench &gt;&gt; Sign in with OpenID\nUse your GitHub credentials to log in (click the icon)"
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"text": "Posit Workbench 🛠️\n\nJoin Workbench via URL in the #workshop-arrow Discord channel\nSelect Posit Workbench &gt;&gt; Sign in with OpenID\nUse your GitHub credentials to log in (click the icon)"
"section": "Posit Workbench: Setup 🍽️",
"text": "Posit Workbench: Setup 🍽️\n\n🖱 +New Session\n🖱 Start Session (defaults are fine)\nRun usethis::use_course(\"posit-conf-2024/arrow\")"
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"text": "Setup 🍽️\n\nCreate a new session: 🖱️ + New Session\nLeave defaults: 🖱️ Start Session\nRun in console: usethis::use_course(\"posit-conf-2024/arrow\")\nProceed with default location: 🖱 2\nAllow unzipping of folder: 🖱 3\nOpen data/setup.R and run the script 🎉\n\n \n\n\n\n\n🔗 pos.it/arrow-conf24"
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"text": "Posit Workbench: Setup 🍽️\n\nDefault location: 🖱 2\nUnzip 📁: 🖱 3\nOpen Session dialog box: Resource Profile &gt;&gt; select Large\nOpen + run data/setup.R 🎉\n\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
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"text": "by Nic Crane & Steph Hazlitt\n\n🗓️ August 12th, 2024\n⏰ 09:00 - 17:00\n🏨 ADD-ROOM\n✍️ pos.it/conf\n\n\nWorkshop Overview\nData analysis pipelines with larger-than-memory data are becoming more and more commonplace. In this workshop you will learn how to use Apache Arrow, a multi-language toolbox for working with larger-than-memory tabular data, to create seamless “big” data analysis pipelines with R.\nThe workshop will focus on using the the arrow R package—a mature R interface to Apache Arrow—to process larger-than-memory files and multi-file datasets with arrow using familiar dplyr syntax. You’ll learn to create and use interoperable data file formats like Parquet for efficient data storage and access, with data stored both on disk and in the cloud, and also how to exercise fine control over data types to avoid common large data pipeline problems. This workshop will provide a foundation for using Arrow, giving you access to a powerful suite of tools for performant analysis of larger-than-memory data in R.\nThis course is for you if you:\n\nwant to learn how to work with tabular data that is too large to fit in memory using existing R and tidyverse syntax implemented in Arrow\nwant to learn about Parquet and other file formats that are powerful alternatives to CSV files\nwant to learn how to engineer your tabular data storage for more performant access and analysis with Apache Arrow\n\n\n\nWorkshop Prework\nAll participants need to bring is a laptop that can connect to wifi. We will be using Posit Workbench to learn together—Workbench will be setup with all the software and data needed for the day. If you would prefer to run code locally on your own laptop, detailed instructions for software requirements and data sources are covered in Packages & Data.\n\n\nWorkshop Schedule\n“This schedule is more what you would call a ‘guideline’ than an actual schedule” — Barbossa, Pirates of the Caribbean\n\n\n\n\n\n\n\nTime\nActivity\n\n\n\n\n09:00 - 10:30\nSession 1: Hello Arrow + Data Manipulation with Arrow I\n\n\n10:30 - 11:00\nCoffee break\n\n\n11:00 - 12:30\nSession 2: Data Engineering with Arrow\n\n\n12:30 - 13:30\nLunch break\n\n\n13:30 - 15:00\nSession 3: Arrow In-Memory Workflows\n\n\n15:00 - 15:30\nCoffee break\n\n\n15:30 - 17:00\nSession 4: Data Manipulation with Arrow II + Wrapping Up\n\n\n\n\n\nInstructors\nNic Crane is an R consultant with a background in data science and software engineering. They are passionate about open source, and learning and teaching all things R. Nic is part of the core team that maintain the Arrow R package, and a co-author of “Scaling up with R and Arrow”, due to be published by CRC Press later this year.\nSteph Hazlitt is a data scientist, researcher and R enthusiast. She has spent the better part of her career wrangling data with R and supporting people and teams in creating and sharing data science-related products and open source software. Steph is the Director of Data Science Partnerships with BC Stats.\n\n\nAcknowledgements\nSome of this Big Data in R with Arrow workshop materials draw on other open-licensed teaching content which we would like to acknowledge:\n\nuseR!2022 virtual Larger-Than-Memory Data Workflows with Apache Arrow tutorial authored by Danielle Navarro\nR for Data Science (2e) written by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund—with thanks to Danielle Navarro who contributed the initial version of the Arrow chapter\nHow to use Arrow to work with large CSV files? blog post by François Michonneau, which introduces the single vs multi-file API models for learning/teaching Arrow\nBig Data in R with Arrow 1-Day Posit::Conf (2023) Workshop by Steph Hazlitt & Nic Crane, an earlier version of this 1-day course.\n\n\n This work is licensed under a Creative Commons Attribution 4.0 International License."
"text": "by Nic Crane & Steph Hazlitt\n\n🗓️ August 12th, 2024\n⏰ 09:00 - 17:00\n🏨 305 | Chelais\n✍️ pos.it/conf\n\n\nWorkshop Overview\nData analysis pipelines with larger-than-memory data are becoming more and more commonplace. In this workshop you will learn how to use Apache Arrow, a multi-language toolbox for working with larger-than-memory tabular data, to create seamless “big” data analysis pipelines with R.\nThe workshop will focus on using the the arrow R package—a mature R interface to Apache Arrow—to process larger-than-memory files and multi-file datasets with arrow using familiar dplyr syntax. You’ll learn to create and use interoperable data file formats like Parquet for efficient data storage and access, with data stored both on disk and in the cloud, and also how to exercise fine control over data types to avoid common large data pipeline problems. This workshop will provide a foundation for using Arrow, giving you access to a powerful suite of tools for performant analysis of larger-than-memory data in R.\nThis course is for you if you:\n\nwant to learn how to work with tabular data that is too large to fit in memory using existing R and tidyverse syntax implemented in Arrow\nwant to learn about Parquet and other file formats that are powerful alternatives to CSV files\nwant to learn how to engineer your tabular data storage for more performant access and analysis with Apache Arrow\n\n\n\nWorkshop Prework\nAll participants need to bring is a laptop that can connect to wifi. We will be using Posit Workbench to learn together—Workbench will be setup with all the software and data needed for the day. If you would prefer to run code locally on your own laptop, detailed instructions for software requirements and data sources are covered in Packages & Data.\n\n\nWorkshop Schedule\n“This schedule is more what you would call a ‘guideline’ than an actual schedule” — Barbossa, Pirates of the Caribbean\n\n\n\n\n\n\n\nTime\nActivity\n\n\n\n\n09:00 - 10:30\nSession 1: Hello Arrow + Data Manipulation with Arrow I\n\n\n10:30 - 11:00\nCoffee break\n\n\n11:00 - 12:30\nSession 2: Data Engineering with Arrow\n\n\n12:30 - 13:30\nLunch break\n\n\n13:30 - 15:00\nSession 3: Arrow In-Memory Workflows\n\n\n15:00 - 15:30\nCoffee break\n\n\n15:30 - 17:00\nSession 4: Data Manipulation with Arrow II + Wrapping Up\n\n\n\n\n\nInstructors\nNic Crane is an R consultant with a background in data science and software engineering. They are passionate about open source, and learning and teaching all things R. Nic is part of the core team that maintain the Arrow R package, and a co-author of “Scaling up with R and Arrow”, due to be published by CRC Press later this year.\nSteph Hazlitt is a data scientist, researcher and R enthusiast. She has spent the better part of her career wrangling data with R and supporting people and teams in creating and sharing data science-related products and open source software. Steph is the Director of Data Science Partnerships with BC Stats.\n\n\nAcknowledgements\nSome of this Big Data in R with Arrow workshop materials draw on other open-licensed teaching content which we would like to acknowledge:\n\nuseR!2022 virtual Larger-Than-Memory Data Workflows with Apache Arrow tutorial authored by Danielle Navarro\nR for Data Science (2e) written by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund—with thanks to Danielle Navarro who contributed the initial version of the Arrow chapter\nHow to use Arrow to work with large CSV files? blog post by François Michonneau, which introduces the single vs multi-file API models for learning/teaching Arrow\nBig Data in R with Arrow 1-Day Posit::Conf (2023) Workshop by Steph Hazlitt & Nic Crane, an earlier version of this 1-day course.\n\n\n This work is licensed under a Creative Commons Attribution 4.0 International License."
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Expand Down Expand Up @@ -1195,26 +1202,5 @@
"title": "Big Data in R with Arrow",
"section": "Grab a sticker!",
"text": "Grab a sticker!\n\n\n\ngrab a hex sticker before you go!\n\n\n\n\n\n🔗 pos.it/arrow-conf24"
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