Introduction Managing incremental data loads efficiently is crucial for keeping your data warehouse up-to-date without putting unnecessary strain on your data sources. Incorta has traditionally relied on a query-based approach using either the LAST_...
Introduction As data volumes grow, especially with the proliferation of sensors and IoT devices or long-term usage of Incorta, it's essential to manage the amount of data stored on disk effectively. Increasing data size can lead to storage issues an...
Incremental data loads in Incorta insert new records and update existing ones, but they don't automatically delete records when they're removed from the source system (like Oracle Cloud ERP). Since source systems often allow data deletion, this can ...
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 ...
Learn about the implications of de-activating NULL handling for your analytics.
This article elaborates the steps to convert complex Oracle SQLS or views to Incorta.
Use the Incorta OTBI Lineage dashboard to determine the mapping between Oracle tables and VOs.
Question How do I parse a JSON string from column to flattened structure in Incorta? Answer Assuming a Incorta table has a column with JSON string then here is a sample pyspark code which you can use to create a Materialized view in Incorta - from...
Learn how to access AWS services from an Incorta Materialized View.
What to do if you receive the 'iteritems' error when trying to convert a Pandas DataFrame to Spark DataFrame.
Streamline your data investigation with the use of dynamic forms!
This overview of time series analysis in Incorta will ensure that you can move into the predictive analytics space with your data!
