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http://rubykaigi.org/2016/presentations/the_thagomizer.html Many people strive to be armchair data scientists. Google BigQuery provides an easy way for anyone with basic SQL knowledge to dig into large data sets and just explore. Using the rubygems.org download data we'll see how the Ruby and SQL you already know can help you parse, upload, and analyze multiple gigabytes of data quickly and easily without any previous Big Data experience. Aja Hammerly, @the_thagomizer Aja lives in Seattle where she is a developer advocate at Google and a member of the Seattle Ruby Brigade. Her favorite languages are Ruby and Prolog. She also loves working with large piles of data. In her free time she enjoys skiing, cooking, knitting, and long coding sessions on the beach.
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In the video "Exploring Big Data with rubygems.org Download Data," Aja Hammerly, a developer advocate at Google, discusses how to harness Big Data using SQL and Ruby, specifically through the rubygems.org download data. The presentation aims to show how even those with basic SQL knowledge can explore vast data sets, using Google BigQuery to analyze downloads and trends in the Ruby gem ecosystem. ### Key Points Discussed: - **Introduction to Speaker and Topic:** Aja introduces herself and her role at Google, emphasizing her interest in engaging with the Ruby community. The focus of the talk is on analyzing Ruby gem usage through data. - **Data Sources:** Hammerly utilizes two major data sources: - **Rubygems.org Download Data:** A PostgreSQL dataset that lists Ruby gems and their download counts. - **GitHub Data:** An extensive dataset of public repositories, specifically looking at files like Gemfile and Gemfile.lock to understand gem usage in projects. - **Overview of the PostgreSQL Dataset:** The primary focus is the 'RubyGems' table, detailing the structure and the importance of the 'gem downloads' table for analyzing the popularity of gems based on download counts. - **Engaging with BigQuery:** Aja highlights the advantages of Google BigQuery, a powerful data warehouse tool that can handle large datasets efficiently, supporting standard SQL and enabling quick queries, making it ideal for analyzing the large GitHub dataset (about 14 terabytes). - **Data Analysis and Queries:** The presentation details how Aja wrote specific SQL queries to derive insights, such as identifying the most downloaded gems, comparing usage of MiniTest vs. RSpec, and determining which versions of Rails were most popular based on download data. - **Challenges with Data Quality:** Aja discusses the importance of ensuring data quality, highlighting instances where the data was not well-formed and the need for careful analysis. - **Comparative Insights:** Findings included: - Active gems such as Rake and Rack topped the download list, while Rails was among the top downloads but not in the top ten. - MiniTest had more downloads compared to RSpec, and Rails 3 and 4 had similar download counts. - Analysis of gem support based on Ruby version requirements showed a trend away from supporting older versions like Ruby 1.9. - **Final Thoughts and Conclusions:** The presentation concludes with the notion that data-driven decisions often reveal insights that challenge initial assumptions. Aja encourages the community to engage with the data rather than rely on guesses. Hammerly also invites attendees to interact during her office hours and highlights the importance of data analysis in shaping product support for the Ruby community. Overall, the session illustrates the power of data exploration using familiar tools like SQL and Ruby, demonstrating that significant insights into the Ruby ecosystem can be derived without extensive experience in Big Data.
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