Data validation, data transformation and de-identificationcan be complex and time-consuming. As data volumes grow, new downstream use cases and applications emerge, and expectations of timely delivery of high-quality data increase the importance of fast and reliable data transformation, validation, de-duplication and error correction.
How the City of Spokane improved data quality while lowering costs
To abstract their entire ETL process and achieve consistent data through data quality and master data management services, theCity of SpokaneleveragedDQLabsandAzure Databricks. They merged a variety of data sources, removed duplicate data and curated the data in Azure Data Lake Storage (ADLS).