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...

Incorta Cloud with DataRobot GCP Real-time Prediction API

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.

Exploratory Data Analysis using SweetViz and Incorta Notebook

You can generate the data profiling report using SweetViz from Incorta Notebook.  This article shows you step by step instructions.

Generate profile reports using Pandas Profiling package within Incorta Notebook

Learn how to generate profile reports using Pandas Profiling package in Incorta Notebook

Run SQL Queries from PySpark and Incorta Notebook

Incorta Notebook enables you to write SQL queries to create your data pipeline in Incorta Materalized Views.

Level Based Aggregate using Incorta Materialized View

  Overview This is the second part of the articles that discusses level-based measures.  The essential part of a level based measure is an aggregation that is calculated at a specific level of detail with a result can be repeated at the lower level ...