Qlik Compose November SR provides new data quality and profiling capabilities for Google Cloud BigQuery data warehouses

[Anand_Rao]

High-quality data allows organizations to extract more value from their data while reducing the risks and costs of cloud analytics, machine learning, and business intelligence initiatives. When siloed data systems are brought together in a cloud data warehouse or data lake, data quality projects are often needed.

[Anand_Rao]

Data profiling analyzes the candidate data sources for a data warehouse to clarify the data's structure, content, relationships, and derivation rules. In short, data profiling helps users understand their data and model it correctly.

Qlik Compose lets you profile the data in the landing zone tables when designing their data warehouse, allowing users to identify uniqueness, cardinality, format discrepancies, value ranges, and other profile information, which helps better design the model.

For data quality, Qlik Compose allows you to detect issues in the data, then either reject insufficient quality data into an error mart or make some simple repairs before loading. All this in a rule-driven approach.

With this release of Qlik Compose, we have expanded this data profiling and data quality rule functionality to be available on Google Cloud's Big Query data warehouse and Snowflake, AWS Redshift, Azure Synapse well Oracle and SQL Server.

For more information on how Qlik Compose can improve data quality in your Google BigQuery and other data warehouses, check us out here. Qlik Compose users can find and download the latest release of Qlik Compose here (filter for Qlik Data Integration – Qlik Compose – 2021.8.0 – SR1).