Knowledge Base Articles
Performance of Materialized Views (MV)
Introduction Incorta Materialized Views are a powerful way to enrich data contained in Incorta tables. Leveraging Spark's processing engine, Materialized Views (MVs) can be defined to introduce enrichments and advanced analytics to reshape your dat...
How to add a Custom Database Connection to Incorta
Setup to a connection a wide variety of connectors for SQL-based, Cloud-based, file system-based sources.
Build Machine Learning Models using Incorta Materialized Views
IntroductionIncorta Materialized Views (MV) provide a way to run PySpark, Scala, and Spark R and can be used for building machine learning (ML) models. We will discuss PySpark, Scala, and Spark R separately. Here are the best practices of using Incor...
Split a Dataset Into Training and Testing Data Sets
IntroductionWhat you should know before reading this articleApplied toLet's GoMulti-class classification data Introduction The train-test split procedure is used to evaluate the performance of machine learning (ML) algorithms. As part of data pre...
Convert Data Type in Incorta Notebook
As a part of data cleaning in Machine Learning, you may need to convert the data from one data type to the other data type. In this article, you will learn how to use Incorta Notebook to covert the data. After you read data in Incorta Notebook, you c...
Save Spark ML Model
Introduction Incorta supports the Machine Learning(ML) model creation process by using Incorta Materialized Views (MV). While you can put the logic of applying the ML model testing and actually use of the ML model for inference in the same MV, the ...
Preview Data in an Incorta Notebook
Preview Data in Incorta Notebook In this article, you will learn how to preview data in Incorta Notebook. Incorta notebook provide methods to let you preview data. using show(), head(), printSchema(), and describe(). incorta.show(df) incorta.hea...
Find and Fill Missing Data in Incorta Notebook
Find and Fill Missing Data in Incorta NotebookIn this article, you will learn how to find and fill missing values in Incorta Notebook. Copy below find null value reusable codes. from pyspark.sql.functions import isnan, when, count, col, lit def colu...
Regular vs. Analyzer Views
Introduction Incorta offers two types of business schema views- regular and analyzer (Runtime or Incorta) views and both are useful to support various use cases. What you should know before reading this article We recommend that you be familiar with...
Handling Deletes
There are a number of strategies that can be implemented depending on the size of your data and whether the deleted records can be easily identified.
Using Formula Columns in Business Views
Introduction A formula column contains an expression that, depending on the expression and the expression context, returns a scalar or an array of values of a specific data type. An expression can consist of built-in functions, built-in system variab...
Connecting to Data
Introduction Incorta has a wide variety of external data source connectors. A connector specifies how Incorta can connect to an external system or application , ingest data and publish to destinations. Incorta includes many out-of-the-box connector...
