Connection configuration - Precisely Data Integrity Suite

Data Integrity Suite
Product
Spatial_Analytics
Data_Integration
Data_Enrichment
Data_Governance
Precisely_Data_Integrity_Suite
geo_addressing_1
Data_Observability
Data_Quality
dis_core_foundation
Services
Spatial Analytics
Data Integration
Data Enrichment
Data Governance
Geo Addressing
Data Observability
Data Quality
Core Foundation
ft:title
Data Integrity Suite
ft:locale
en-US
PublicationType
pt_product_guide
copyrightfirst
2000
copyrightlast
2026

This section provides you the details on various fields required to configure Azure Data Lake Storage (ADLS) in Data Integrity Suite.

Field Description
Connection name Specifies the unique name that appears in the Data Integrity Suite interface and helps distinguish between multiple ADLS connections. Example: Production ADLS
Description An optional field to specify the purpose, environment, or additional context for this connection. Example: Production data lake for enterprise analytics
Agent to access data Select the agent from the dropdown that connects to the database. It helps securely access data from your network.
Account Name Specifies the name of your Azure Data Lake Storage account. This is the unique identifier for your ADLS instance within Azure.
Azure cloud environment Specifies the Azure cloud deployment model where your ADLS account resides. Select the appropriate environment based on your organizational requirements.

Advanced Options

 Allows you to set the connector logging level and define additional parameters for the ADLS connection.

Debugging parameters

Advanced options include several parameters that allow you to customize how your data is processed in Azure Data Lake Storage. These settings control aspects such as:

  • Delta Lake support
  • Spark driver memory allocation
  • Spark executor memory allocation

Proper configuration of these parameters helps ensure your data is read and cataloged accurately.

Table 1.
Parameter Default Description
enableDeltaLakeSupport false The on/off switch for Delta Lake. When set to false, everything stays as plain Parquet. Set to true to turn on _delta_log/ detection, Delta metadata extraction, version tracking, and partition column identification. Defaults to off because data quality profiling doesn't support Delta yet.
spark.driver.memory 1g RAM for the coordinator pod. Increase this value for wide tables, heavily partitioned datasets, or when processing larger volumes of data.
spark.executor.memory 1g RAM for each worker pod. Increase this value for wide tables, heavily partitioned datasets, or when processing larger volumes of data.