Guide on PySpark DataFrame Functionality

In this blog post I have covered some of the common PySpark DataFrame Functions, Joins, and Windows Functions.  All the commands were executed on the new job authoring Jupyter notebook available in preview in AWS Glue Studio. Import Libraries from awsglue.job import Job from awsglue.transforms import * from awsglue.context import GlueContext import pyspark.sql.functions as F …

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Export table from Aurora PostgreSQL to Amazon S3

In this blog post I discuss how to export 100GB non-partitioned table from Aurora PostgreSQL to Amazon S3. I will walk you through two approaches that you can use to export the data. Firstly I will demonstrate using aws_s3, a PostgreSQL extension which Aurora PostgreSQL provides and then using AWS Glue service. The post also …

Handling missing values in Pandas to Spark DataFrame conversion

Data transformation is an important aspect of Data Engineering and can be a challenging task depending on the dataset and the transformation requirements. A bug in data transformation can have a severe impact on the final data set generated leading to data issues. In this blog I am going to share my experience of having …

AWS Glue Job Fails with CSV data source does not support map data type error

AWS Glue is a serverless ETL service to process large amount of datasets from various sources for analytics and data processing. Recently I came across "CSV data source does not support map data type" error for a newly created glue job. In a nutshell, the job was performing below steps: Read the data from S3 …

AWS Glue and PySpark Guide

In this post, I have penned down AWS Glue and PySpark functionalities which can be helpful when thinking of creating AWS pipeline and writing AWS Glue PySpark scripts. AWS Glue is a fully managed extract, transform, and load (ETL) service to process large amount of datasets from various sources for analytics and data processing. While …