Overview When loading data from Oracle Cloud, BICC (Business Intelligence Cloud Connector) is the most commonly used tool. It provides access to a majority of Oracle Cloud tables via View Objects (VOs) prepared by Oracle specifically for this purpos...
When loading data incrementally, Incorta inserts new records and updates existing records that have already been loaded, but by default it does not delete records. Source systems like Oracle Cloud ERP, however, may allow users to delete data. This ...
This guide explains how to create External Locations in Databricks to read schemas and tables directly from your Incorta cluster. Overview Databricks' external locations allow you to access your Incorta data stored in Azure Data Lake Storage Gen2 wi...
Introduction A Materialized View (MV) fails during the transformation stage in Incorta. The error shows executors repeatedly being lost during Spark job execution. Example Error Logs ERROR: [00:01:45] [default | 81d7 | ETL | EXECUTION | RUNNING | Sa...
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_...
