Helping you Learn Spark Scala.

Recent Spark Scala Examples

• Lpad and Rpad: Pad Strings in Spark Scala DataFrames

  The lpad and rpad functions pad a string column to a specified length by adding characters to the left or right side. They're commonly used for zero-padding numbers, aligning text output, and formatting fixed-width fields.

• InitCap: Convert Strings to Title Case in Spark Scala DataFrames

  The initcap function converts a string column to title case — capitalizing the first letter of each word and lowercasing the rest. It's useful for normalizing names, addresses, and other text where consistent capitalization matters.

• Lower and Upper: Convert String Case in Spark Scala DataFrames

  The lower and upper functions convert string columns to lowercase and uppercase respectively. They're commonly used to normalize data for case-insensitive comparisons and consistent formatting.

• SBT Assembly Jar Naming in Shell Scripting

  When using sbt in spark scala projects it can be useful to access the name of the assembly that will be created with sbt within your bash or zsh shell scripts. Thankfully extracting the values is rather straight forward.

• Building MapType Columns in Spark Scala DataFrames for Enhanced Data Structuring

  Using a MapType in Spark Scala DataFrames can be helpful as it provides a flexible logical structures that can be used when solving problems such as: Machine Learning Feature Engineering, Data Exploration, Serialization, Enriching Data and Denormalization. Thankfully building these map spark scala columns is very stright fwd from existing data within a data frame.

• Converting a Map Type to a JSON String in Spark Scala

  Using a MapType in Spark Scala DataFrames provides a more flexible logical structures, hierarchical data and of course working with arbitrary data attributes.

• Spark Scala isin Function Examples

  The isin function is defined on a spark column and is used to filter rows in a DataFrame or DataSet.

• Hashing Functions, Spark Scala SQL API Function

  Hash functions serve many purposes in data engineering. They can be used to check the integrity of data, help with duplication issues, cryptographic use cases for security, improve efficiency when trying to balance partition sizes in large data operations and many more.

• When and Otherwise in Spark Scala -- Examples

  The when function in Spark implements conditionals within your DataFrame based etl pipelines. It allows you to perform fallthrough logic and create new columns with values based upone the conditions logic.

• Coalesce in Spark Scala for Data Cleaning

  The coalesce function returns the first non-null value from a list of columns. It's a common technique when you have multiple values and you want to prioritize selecting the first available one from them.