Profiling CSV files in DQOps
Watch the video to learn how you can quickly analyze data quality in CSV files using DQOps data quality platform
This 60-second video shows the process of connecting a new data source, profiling the data, running data quality checks, and reviewing results. Once the profiling is done, DQOps will keep monitoring the data quality of the CSV file and will alert you if any issues arise that make the file no longer valid.
DQOps is a data quality platform that covers the entire data lifecycle, from profiling new data sources to full automation of data quality monitoring
The approach to managing data quality changes throughout the data lifecycle. The preferred interface for the data quality platform also changes: user interface, Python code, REST API, command line, editing YAML files, running locally or configuring a shared server. DQOps supports all of these options.
Evaluating new data sources
Data scientists and data analysts want to review the data quality of new data sources or understand the data present on the data lake by profiling data.
Creating data pipelines
The data engineering teams want to verify data quality checks required by the Data Contract on both source and transformed data.
Testing data
An organization has a dedicated data quality team that handles quality assurance for data platforms. Data quality engineers want to evaluate all sorts of data quality checks.
Operations
The data platform matures and transitions to a production stage. The data operations team watches for schema changes and data changes that make the data unusable.