How to Implement Oracle LISTAGG() Function in Incorta
Learn out to implement the Oracle LISTAGG() function in Incorta
Knowledge Base Articles
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.
Deploy Incorta Data Profiler
OverviewDeployment OverviewSample DataInstallationStep 1: Install the DataPrepAPI Wheel FileStep 2: Upload the Data_Profiler_Datasets data filesStep 3: Import SchemaStep 4: Import DashboardStep 5: Add Session Variables (Optional)Related Materials Ove...
Customize Incorta Data Profiler
IntroductionCalling DataProfiler APIBasic Table InfoSummary TableCorrelation TableFrequency TableHistogram TableIncremental LoadRelated Materials Introduction This article will show you how to adjust the output of the Incorta data profiler with diffe...
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 Incorta on-Premises Data Sharing with TAZI
OverviewSolutionAdd Incorta as a data source in TAZIUse the Incorta Table or Business View as the Input for a TAZI Business Model Overview The Machine Learning platform TAZI can receive the data from Incorta and use this data for training the mode...
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. ...
Integration of Incorta and TAZI
What is TAZI?Deployment Scenarios What is TAZI? TAZI is a machine learning platform that allows continuous learning and Auto-ML functionalities. When it is used with Incorta, Incorta can be used for ingesting, integrating the data, and preparing ...
Use Markdown lines/comments within an Incorta Notebook
OverviewGetting StartedFormatting Overview In this article, you'll learn some helpful tips to use Markdown within Incorta Notebook. Markdown helps you take notes, explain the logic, and insert links, tables, etc. in a notebook making your code more...
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...
