This guide explains how to create shortcuts in Microsoft Fabric to read schemas and tables directly from your Incorta cluster, whether stored in Azure Data Lake Storage Gen2 or Google Cloud Storage. Overview Microsoft Fabric shortcuts allow you to a...
Overview Incorta’s new MS Fabric data destination enables you to push curated, enriched, or transformed datasets directly into MS Fabric. Supported Incorta Versions: 2024.7.6 and above Configuration To enable the MS Fabric data destination, a set o...
Overview Incorta’s new Databricks data destination enables you to push curated, enriched, or transformed datasets directly into Databricks. Supported Incorta Versions: 2024.7.6 and above Configuration To enable the Databricks data destination, a ...
Overview Fabric provides two main data experiences: Warehouses: Traditional SQL-based data warehouses optimized for structured analytics and business intelligence workloadsLakehouses: Open data architecture that combines the flexibility of data lake...
Overview Incorta Data Delivery streamlines the integration between Oracle Cloud ERP and data destinations like Google BigQuery, Snowflake or Databricks. In addition, it accelerates data mart deployment and automates data model design and source sche...
Overview Incorta’s new Snowflake data destination enables you to push curated, enriched, or transformed datasets directly into Snowflake. Supported Incorta Versions: 2024.7.5 and above Configuration To enable the Snowflake data destination, a set of...
To ensure that the Incorta cluster is properly sized for the data being ingested from Oracle Cloud ERP (Fusion) we need to ascertain the source data size. There is no direct way to run ad hoc SQL queries on the Oracle Cloud ERP database, unlike tradi...
Learn how to ingest real time Kinesis data with PySpark.
Learn how to and why you would choose to ingest data from S3 with an MV
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 ...
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...
