Data & Schemas Knowledgebase
A solid data foundation leads to analytics. Build that foundation here.
cancel
Showing results for 
Search instead for 
Did you mean: 

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.

calendar.jpg
VinayR by Employee Alumni
  • 1314 Views
  • 0 comments
  • 0 kudos

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...

spark.jpg
Tristan by Employee
  • 2342 Views
  • 0 comments
  • 0 kudos

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...

suxinji_0-1646338922057.png suxinji_1-1646338971707.png suxinji_2-1646338971735.png
suxinji by Employee Alumni
  • 903 Views
  • 0 comments
  • 1 kudos

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 ...

bulb.jpg
suxinji by Employee Alumni
  • 2534 Views
  • 0 comments
  • 1 kudos

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...

suxinji_0-1646671882423.png suxinji_1-1646671882411.png suxinji_2-1646671882405.png suxinji_3-1646671882427.png
suxinji by Employee Alumni
  • 612 Views
  • 0 comments
  • 1 kudos

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...

suxinji_0-1646672041325.png suxinji_1-1646672041312.png
suxinji by Employee Alumni
  • 824 Views
  • 0 comments
  • 0 kudos

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...

tile.jpg

Data Load Strategy

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...

Screen Shot 2022-03-03 at 3.22.21 PM.png
RichC by Employee
  • 3350 Views
  • 0 comments
  • 0 kudos
Best Practices Index
Best Practices

Just here to browse knowledge? This might help!