Data Quality Checks (DQC) Framework

Unlock cost-friendly and unrestricted data quality checking

Since the overwhelming impact of Big Data's advent into financial business services, firms often struggle with complex and disorganized data in their Data Lake and warehouse environments. Poor data quality and lack of standardization often lead to inconsistent or misleading data analytics. Data warehouses can only implement basic data integrity constraints due to volume and performance requirements, thereby making periodic checks a crucial practice. Additionally, many data quality (DQ) checking solutions currently on the market are expensive yet lack optimization and functionality.

What if you could reduce data checking costs and increase productivity without performance constraints?

With Virtusa’s Data Quality Checks (DQC) Framework solution, businesses can conduct cost-efficient DQ checks with an extendible framework using open-source tooling. DQC Framework contains a suite of tools for implementing data quality checking and is built around the popular python-based, open-source data validation, Great Expectations (GE). Our solution uses SQL-based checks on Data Lakes and warehouses so that users can view data results and failures. DQC streamlines the management of testing, automation, and scheduling processes – without the expensive licensing fees attached to commercial ETL (extract, transform, and load) and governance tools.

Solution analysis and benefits

Virtusa’s Data Quality Checks (DQC) Framework provides streamlined data standardization and reduces the footprint of commercial tools. Our solution is universally applicable to all data warehouses and databases and is a valuable resource for the detection and remediation of data quality issues. DQC Framework is also easy to learn and operate and begins yielding results in a short span of time. 

With DQC, businesses can:

  • Implement data quality checks for any database with ease
  • Test automation and scheduling – run data checks periodically to detect any signs of regression
  • Conduct unit and end-to-end testing
  • Monitor data results and fix failures
  • Reduce technical debt in data pipelines 
Key features

Data Quality Checks Framework modernizes the data quality checking process so companies can cost-effectively standardize data. 

  • Automated data profiling and streamlined testing 
    • Automated documentation and testing management capabilities 
    • Scheduling and test orchestration in conjunction with Apache Airflow
  • Data results library 
    • Displays data quality results and failures in text files or HTML page format for viewing and analysis 
  • Expansive integration capabilities
    • Ability to integrate with a wide array of big data platforms and environments, including Spark, Databricks, AWS EMR (Amazon EMR), AWS Redshift, Google BigQuery, Snowflake, Slack, Airflow, Postgres, Notebooks, and more. 
    • Pluggable and extensible 
Why Virtusa?

Virtusa’s Data Quality Checks Framework is the cost-saving, adaptable answer to your data quality challenges. With DQC, you get comprehensive access to a premium data quality testing platform that integrates impeccably with the Great Expectations tool and like-minded DQ platforms on the market. Our solution is proven to circumvent limitations and override obstacles surrounding complex data checking scenarios, allowing users to innovate and expedite their data quality checking processes.

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