Use SparkR DataFrame for Selecting, Filtering, and Grouping your Data
Use select(), filter(), arrange(), and group_by() functions to handle Spark DataFrame on R.
Knowledge Base Articles
Getting Started with R in Incorta Notebook
Overview R is a popular language in Data Science. Incorta supports creating materialized views (MV) with R. Your first Spark Program The minimum requirement for creating an MV includes the following: # Read data from Incorta into Spark df <- read(...
Use SparkSQL with SparkR
Overview In this recipe, you’ll learn how to run SQL queries from SparkR. After using the read() Incorta Notebook extension function to load data in Incorta Notebook, the data is in a SparkR DataFrame. You can use sql() to write SQL queries after cr...
Creating Snapshot tables with Incorta
A snapshot table holds the same transactional data as its source system, with additional fields for tracking the snapshot date.
ML with IRIS data set using Incorta
This article shows how to use a classification Machine Learning (ML) algorithm on the Iris Flower data set. It first creates a model based on a training data set and then scores the the test data set and gives the prediction of the flower based on th...
How to connect to Incorta from external sql query tools like DbVisualizer or PSequel
Use external sql query tools like DbVisualizer or PSequel to connect to Incorta and run postgres sqls against Incorta data.
How to output a csv file for schema.table.column details
This python script takes an unzipped tenant export file or an unzipped schema export file and produces a csv file which has all the table column details like datatype, etc... in a schema. This csv file can then be imported into Incorta and a dashboar...
Record Level Security
Learn about how Incorta allows you to restrict user data access in the Incorta Direct Data Platform.
Modeling Schemas, Tables, and Joins
The purpose of this article is help users understand data modeling concepts and best practices in Incorta .
Supporting Slowly Changing Dimension (SCD) Type2 dimension in Incorta
A slowly changing dimensions (SCD) is a dimension that stores and manages both current and historical data over time.
Getting Started with Machine Learning in Incorta Guide
A take-home guide for an introduction to Machine Learning in the Incorta platform.
Data Validation Tips
How to validate data between a Incorta report and my legacy BI report.
Modeling Schemas, Tables, and Joins
The purpose of this article is help users understand data modeling concepts and best practices in Incorta.
Date Dimension
Date is a common dimension used in most application deployments. It is primarily used to roll up data so it can be viewed across a broad time range, facilitating trend analysis.
