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SymptomsDiagnosisSolution1. Read Data into Spark2. Create JSON Schema3. Parse JSON and Create a Temporary Column4. Explode Nested JSON Array5. Save the Final DataFrame Download
Symptoms
JSON data are provided by the source system for sharing with ...
OverviewWhat is Chidori?Incorta and Sparkflows IntegrationConfigure the ConnectionsCreate Project and workflow in SparkflowsML Model RegistryCreate an Incorta MV using the PySpark generated by Sparkflows
Overview
Sparkflows provides a no code or low...
Introduction
Incorta MV Notebooks offer an alternative way to edit Spark code used to generate materialized views (MVs). This article provides a couple of tips that allows you to save your work while doing development.
What you need to know before r...
Scenario
You need to access AWS services, such as S3, Kafka, or Kinesis, from an Incorta Materialized View (MV) for extracting data.
Issues
You prefer not to list the credentials in the MV script.
Solution
You can set up AWS to use temporary secur...
Manually editing the schema XML files may result in a corrupted Incorta metadata and can cause the entire system not workable. Even a small typo with the schema name, which can be sensitive, caused us a lot of time to debug as the symptom is on the ...
We support the migration using
CLI - You can access the command line interface to export and import objectsUI - You can manually export and import objects from the UI
Here is a community article about content migration from DEV to UAT to PROD. https://community.incorta.com/t5/administration-knowledgebase/migrating-content-between-environments/ta-p/1865
The lookup function is for looking up another lookup table using the key.
To get the value, we will need to aggregate across these two rows:
if(distinct(schemaX.tableN.column3)>1, "V", max(schemaX.tableN.column3))
To repeat the same value, we may nee...
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