Knowledge Base Articles
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 ...
Query business schema data from PySpark using the incorta_sql_pg() function
Introduction The incorta_sql_pg() function lets you directly query business 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 quer...
Incorta MV execution succeeds in the Incorta Notebook but fails to save
Your MV ran successfully in Incorta Notebook, but when you tried to validate and save the MV, it failed.
How to Make a REST API Call from a Materialized View
Learn how to leverage MVs to build connections to pull from REST APIs.
Introduction to Incorta Data Profiler
Learn how Incorta Data Profiler can help you understand the quality of your data before you bring it into Incorta.
Integration of Incorta Cloud with TAZI AI Cloud
OverviewSolutionCreate GCS Data Source in TAZICreate the destination in TAZI to write to the shared GCS BucketLoad the data from GCS via the Incorta Data Lake Connector Overview This article discusses integrating Incorta Cloud with TAZI Cloud. ...
Getting Started with Spark SQL
IntroductionWhat you need to know before reading this articleStepsNavigationIncorta Notebook for SparkSQLAutocomplete for SparkSQLUse the DESC command to get the list of columnsUse Incorta Notebook Visualization Introduction Incorta Materialized Vi...
A/B Testing Analysis with Incorta PySpark
OverviewWhat is A/B testing?Case StudyData SetTest HypothesesAnalysis in PySparkLoad the data into SparkClean up the dataCalculate the summary of controlled and treatment groupZTest PySpark UDFCall the PySpark UDFConvert the Resulted Array to DataFra...
Data Harmonization for Multi-Source
Introduction When creating visualizations based on multiple source systems, tables must be aligned to achieve a wholistic view of a business. For example, organizations that migrate from one ERP to another align invoice tables from each ERP to see al...
Multi-Source Table Design
Struggling to wrangle data from multiple sources? Learn about how and when to combine similar data points from disparate sources with three different approaches using the Incorta Platform.
Share Prediction Results from AI/ML Platform with an Incorta on-premise deployment
Create analytics dashboards in Incorta with scored data from an outside AI/ML platform.
