Getting Started with PySpark for Big Data Analytics, using Jupyter Notebooks and Docker
An updated version of this popular post is published in Towards Data Science: Getting Started with Data Analytics using Jupyter Notebooks, PySpark, and Docker IntroductionThere is little question, big data analytics, data science, artificial intelligence (AI), and machine learning (ML), a subcategory of AI, have all experienced a tremendous surge in popularity over the last few years. Behind the h
Efficient UD(A)Fs with PySpark
Nowadays, Spark surely is one of the most prevalent technologies in the fields of data science and big data. Luckily, even though it is developed in Scala and runs in the Java Virtual Machine (JVM), it comes with Python bindings also known as PySpark, whose API was heavily influenced by Pandas. With respect to functionality, modern PySpark has about the same capabilities as Pandas when it comes to
20170927 pydata tokyo データサイエンスな皆様に送る分散処理の基礎の基礎、そしてPySparkの勘所
20170927 pydata tokyo データサイエンスな皆様に送る分散処理の基礎の基礎、そしてPySparkの勘所
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