Data & Schemas Knowledgebase
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Knowledge Base Articles

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.

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VinayR by Employee
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Data Engineering & Enrichment

Introduction When designing your data model in Incorta, the first step is typically to bring in all of the raw tables from each data source.  All tables from each data source can be brought in and loaded without any transformations being applied or ...

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JeffW by Employee
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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...

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Tristan by Employee
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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...

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suxinji by Employee
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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 ...

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suxinji by Employee
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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...

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suxinji by Employee
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