by Shubhanshu Dixit | Sep 5, 2023 | Data Quality, Data Validation, Dataflow, DataOps, ETL Testing
Introduction In today’s data-driven world, organizations are continually seeking ways to optimize data operations, enhance data quality, and ensure robust governance practices. The integration of powerful tools like DBT (Data Build Tool) and Datagaps DataOps... 
by Surya Golagani | May 29, 2023 | Cloud Data Migration, Dataflow, DataOps, ETL Testing, Snowflake
What Is ETL Testing? ETL Testing refers to the testing, validation, and analysis of the Extraction, Transformation, and Loading Processes that are part of ETL and ELT Pipelines. As ETL testing refers to “Data-in-Motion” Testing, the unit test architecture... 
by Shubhanshu Dixit | Feb 14, 2023 | Cloud Data Migration, Data Quality, Data Validation, Dataflow, DataOps, ETL Testing
Data profiling is a crucial step in the data management process, especially in the pharmaceutical industry where accurate and reliable data is essential for making informed decisions. Data profiling involves examining and summarizing the characteristics of a dataset... 
by Shubhanshu Dixit | Jan 13, 2023 | Data Quality, Data Validation, Dataflow, DataOps, ETL Testing
DataOps is managing data in its raw form or transforming it from one format to another. It ensures that the data flowing through your production pipelines is accurate and reliable. DataOps combines software development and infrastructure management best practices with... 
by Shubhanshu Dixit | Dec 29, 2022 | Cloud Data Migration, Data Validation, Dataflow, DataOps, ETL Testing
Understanding Validation of Salesforce Objects, Uploads, Updates and How Datagaps DataOps Can Help Intro to Salesforce Salesforce is a cloud-based CRM platform that helps businesses manage and analyze customer interactions and data throughout the customer lifecycle.... 
by Shubhanshu Dixit | Dec 6, 2022 | Cloud Data Migration, Dataflow, DataOps, ETL Testing
What Is Data Drift? What is Data Drift? Within the data space, the only constant thing is “change”. The drift in data here refers to a multitude of changes in the input data primarily in terms of frequency, aggregates, and heterogeneity. These are not...
