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. ...
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...
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...
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...
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.
Create analytics dashboards in Incorta with scored data from an outside AI/ML platform.
You can generate the data profiling report using SweetViz from Incorta Notebook. This article shows you step by step instructions.
Learn how to generate profile reports using Pandas Profiling package in Incorta Notebook
Incorta Notebook enables you to write SQL queries to create your data pipeline in Incorta Materalized Views.
Overview This is the second part of the articles that discuss about level-based measure. The essential of a level based measure is an aggregation that are calculated at a specific level of details and the result can be repeated at the lower level...
Learn how to install the python package in Incorta Cloud!
Introduction Incorta supports the following types of joins: One-to-one (1:1) Many-to-one (M:1) Many-to-many (M:M) joins using Materialized Views Example: Simple Many-to-one (M:1) Join Tables: Sales table Customer table The Sales table has mult...
Use Incorta to prepare data and create an ML model in DataRobot. You can then send the data as part of an Incorta data pipeline to DataRobot for making predictions. The DataRobot Batch Prediction API can be called from an Incorta MV to complete the prediction job.
String matching is a very common problem in business. You will learn how to perform address matching using FuzzyWuzzy in Incorta.
