Showing results for 
Search instead for 
Did you mean: 
Data Engineering
Join discussions on data engineering best practices, architectures, and optimization strategies within the Databricks Community. Exchange insights and solutions with fellow data engineers.
Showing results for 
Search instead for 
Did you mean:


Spark Dataframes Schema

Schema inference is not reliable.

We have the following problems in schema inference:

  1. Automatic inferring of schema is often incorrect
  2. Inferring schema is additional work for Spark, and it takes some extra time
  3. Schema inference is conflicting with the schema validation

4. It might also change the column order

We have two approaches to do it.

  1. Schema DDL String
  2. Struct Type Object

Further Detailed description please refer this link

Please like,share,comment

Happy New year 2023


Honored Contributor II

Thanks for sharing

Rishabh Pandey

Esteemed Contributor III

good post thanks

New Contributor III

one other difference between those 2 approaches is that In Schema DDL String approach we use STRING, INT etc.. But In Struct Type Object approach we can only use Spark datatypes such as StringType(), IntegerType(), etc..

Join 100K+ Data Experts: Register Now & Grow with Us!

Excited to expand your horizons with us? Click here to Register and begin your journey to success!

Already a member? Login and join your local regional user group! If there isn’t one near you, fill out this form and we’ll create one for you to join!