Achieving Quality and Consistency in Data Ingestion

About Client 

The client is a billion-dollar home leasing company with an estimated 80,000 rental homes in 16 markets across the United States.


The client initiated a modernization effort for their data architecture. As part of this process, the Enterprise Data Warehouse was moved from SQL server to the cloud-based Snowflake platform. 

At the same time, data from nine different sources got added with plans to incorporate more in the future. 

However, ensuring data quality and consistency throughout the ingestion process became crucial for minimizing errors and achieving robust data governance. The client desired for a modern, scalable, and cost-effective solution that could handle complex analytical tasks, reduce operational complexity, and facilitate efficient data ingestion. 


The lack of a systematic approach posed our client with the following challenges:

  • Errors and difficulties in data integration and transformation during ingestion.
  • Monitoring errors during the pipeline execution and capturing source-related data inconsistencies became tedious. 

The client aimed to minimize errors during ingestion through a quality data framework.


  • Implemented a comprehensive data quality framework, encompassing quality checks like duplicate detection, range verification, element validation, data profiling, numeric metric assessment, and many more to ensure data consistency.
  • Employed AWS Glue for ETL, DBT for transformation, and DataDog for cloud infrastructure monitoring.
  • Facilitated infra and code deployments for multiple tools, such as Snowflake, DBT, AWS Glue, AWS ECR, and Airflow.
  • Created a comprehensive data dictionary, established data lineage, and built a centralized catalog. 
  • Implemented monitoring for compliance and verified data quality after ingestion.


  • Achieved seamless data ingestion from various sources and SQL server to Snowflake, streamlining data access and analysis.
  • Gained reliable and accurate insights from the generated data profile.
  • Improved data management with a 30-35% reduction of efforts to build a pipeline for data ingestion.
  • Data ingestion to specific business groups like the customer relationship team and business analysts for financial management empowered accurate predictions and informed decision- making.

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