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...
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 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 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...
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...
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.
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...
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...
Introduction This article discusses the procedures and best practices for Data Load Strategy. What you should know before reading this article We recommend that you be familiar with these Incorta concepts before exploring this topic further. Importi...
IntroductionIf you need to represent hierarchical data in Incorta, we provide several features that make this data modeling technique easier. Common use cases for hierarchy include HR org chart, chart of accounts, and even your Incorta dashboards' fo...
Introduction If you need to create point in time reporting in Incorta, you may need to implement a snapshotting strategy. Often the source application itself will track changes to its data and thus you can get a full historical view of changes to d...
Introduction As a Universal Data Analytics Platform (UDAP), Incorta provides a fully featured and rich experience from data acquisition, to data enrichment all the way through to data visualization - all with massive data sets and at incredible spee...
Join definitions are what allow you to create the needed relationships between your data to guarantee you get the results that you expect on your Incorta dashboards.
Introduction Incorta is not a real-time data platform, however given its unique approach that allows customers to leave data “in-shape”, paired with its speed when it comes to extracting and preparing data, it can support very frequent data refreshe...
