Apache Spark

Recently, I was working on one project where the ETL requirement was to have daily snapshot of the table. It was 15+ years old data model on which datawarehouse was designed and the client wanted to replicate it on Hadoop. So you can convert the ETL to Spark SQL however not everything works as-is on […]

You may need to add new columns in the existing SPARK dataframe as per the requirement. This new column can be initialized with a default value or you can assign some dynamic value to it depending on some logical conditions. Let’s see an example below to add 2 new columns with logical value and 1

What is Repartition in Spark ? Spark Repartition is the process of movement or shuffling of data into given number of logical partitions. Repartition is done on the basis of some column or expression or in a random manner. Default number of shuffle partitions in Spark is 200. Where do I use repartition in Spark

Spark dataframe add row number is very common requirement especially if you are working on ELT in Spark. You can use monotonically_increasing_id method to generate incremental numbers. However the numbers won’t be consecutive if the dataframe has more than 1 partition. Let’s see a simple example to understand it : So I have a dataframe

In this post, we will see how to Handle NULL values in any given dataframe. Many people confuse it with BLANK or empty string however there is a difference. NULL means unknown where BLANK is empty. Alright now let’s see what all operations are available in Spark Dataframe which can help us in handling NULL