In this Intro to PySpark Workshop, there are five main points: About Apache Spark Sample PySpark Application walkthrough with explanations Custom built Jupyter Azure Notebook to interactively demonstrate fundamental PySpark concepts Python-specific Spark advice Curated resources to learn more Slides PDF Version: Intro to PySpark Workshop Q&A Options: Twitter: #PySparkWorkshop Sample app from pyspark.sql import… Continue reading
Python is the de facto language of Data Science & Engineering. (IMHO R is grand for statisticians, but Python is for the rest of us.) As a prominent language in the field, it only makes sense that Apache Spark supports it with Python specific APIs. Spark makes it so easy to use Python that it… Continue reading
Often we log data in JSON, CSV or other text format to Amazon’s S3 as compressed files. This pattern is a) accessible and b) infinitely scalable by nature of being in S3 as common text files. However, there are some subtle but critical caveats that come with this pattern that can cause quite a bit… Continue reading
Apache Spark supports many different data sources, such as the ubiquitous Comma Separated Value (CSV) format and web API friendly JavaScript Object Notation (JSON) format. A common format used primarily for big data analytical purposes is Apache Parquet. Parquet is a fast columnar data format that you can read more about in two of my… Continue reading