While working in Databricks, I needed to plot some images. I wrote some code that does this in IPython notebooks, but nothing that works on a Dataframe. I decided to change the code a bit, so it works in Databricks. This solution uses PIL and Matplotlib.
This week we’ve been working on processing the access logs from Cloudflare with Databricks (Spark). We now have a job that generates a huge CSV file (+1GB) and sends it on towards by FTP for further processing with an external tool. Creating a DataFrame with the right data was easy. Now, let’s explore how to do a CSV export, secrets management and an FTP transfer!
I operate from the Netherlands and that makes my time zone Central European Summer Time (CEST). The data I handle is usually stored in UTC time. Whenever I need to crunch some data with Spark I struggle to do the right date conversion, especially around summer or winter time (do I need to add 1 or 2 hours?). In this blog, I’ll show how to handle these time zones properly in PySpark.
At Wehkamp we use Apache Kafka in our event driven service architecture. It handles high loads of messages really well. We use Apache Spark to run analysis. From time to time, I need to read a Kafka topic into my Databricks notebook. In this article, I’ll show what I use to read from a Kafka topic that has no schema attached to it. We’ll also dive into how we can render the JSON schema in a human-readable format.