Knowledge Base Articles
How to Ingest Data from AWS S3 to Incorta using Incorta PySpark MV
Learn how to and why you would choose to ingest data from S3 with an MV
Loading JSON data to Incorta using Materialized View - Part 1
SymptomsDiagnosisSolution1. Read Data into Spark2. Create JSON Schema3. Parse JSON and Create a Temporary Column4. Explode Nested JSON Array5. Save the Final DataFrame Download Symptoms JSON data are provided by the source system for sharing with ...
Issue with converting a Pandas DataFrame to a Spark DataFrame
What to do if you receive the 'iteritems' error when trying to convert a Pandas DataFrame to Spark DataFrame.
Dynamic Forms in Materialized Views
Streamline your data investigation with the use of dynamic forms!
How to Check the Version of a Python Package
Sometimes you need to check the version of a Python package. Find out how!
Incorta Notebook Best Practices
We bet there is one thing in here you didn't know about working with notebooks in Incorta.
Use hash keys to compare two records in PySpark
Use hash keys to simplify duplicate record identification.
Informatica ETL migration to Incorta
Introduction When replacing your current Informatica ETL processes, there are many approaches you can take to seamlessly transition to Incorta. This document will cover some of the best practices around how to manage an Informatica migration to Inco...
Saving a Pandas DataFrame as a Materialized View (Convert to Spark)
Quickly save Pandas DataFrames as materialized views without manually defining each column's data type and function. These simple functions allow the switch between Spark and Pandas more efficiently.
How to Implement Oracle LISTAGG() Function in Incorta
Learn out to implement the Oracle LISTAGG() function in Incorta
Query physical schema data from PySpark using the incorta_sql() function
Introduction The incorta_sql() function lets you directly query physical schema data in a PySpark MV without going through the old and cumbersome way. What you need to know before reading this article Previously, you could use spark.sql() to query ...
