What Are The Challenges Of A Data Factory
DataOps Platform for Integrated Data Testing & Production Monitoring
DataOps is a set of practices and tools used by Big Data teams to increase velocity, reliability, and quality of data analytics. It emphasizes communication, collaboration, integration, automation, measurement and cooperation between data scientists, analysts, data/ETL (extract, transform, load) engineers, information technology (IT), and quality assurance/governance. It aims to help organizations rapidly produce insight, turn that insight into operational tools, and continuously improve analytic operations and performance.
TDD | Agile | Unit Test | Regression Test | Release Sign-off | User Acceptance | Monitoring | Compliance | Dashboard | Alerts & Notification
Challenges of a Data Factory: Managing | Building | Operating
|The Data-Centric projects with Linear Waterfall methodologies are longer to finish. According to Gartner More than 50% of data Integration projects have limited acceptance or outright failures. 83% of Data migration projects exceed their budgets or schedules. With iCEDQ, the sequential development model can be transformed into a TDD – Test Driven Development and/or Agile development. Not only, it will shrink the development pipeline but also quicker release cycle. Business users are involved earlier in defining audit requirements thus greatly improving the chances of success.|
|Human Error||Both the complexity of data projects and volume of data has increased. It is humanly impossible to manually test or keep track of it. The data error can be significantly reduced by QA automation (Unit testing, Regression Testing). The automation testing a large volume of data and higher test coverage.
|Operations||In production, data flows must be monitored every day. However, most systems today only monitor the jobs and are not the data transformation. This creates a blind spot in operations. The result, data issues are only known when the users complain.iCEDQ provides complete data flow monitoring capabilities. You can monitor data trends, exceptions, provide alerts and warning as defined. You can build your custom dashboards and provide complete traceability.
Data Auditing – The Missing Component of the Data Strategy
iCEDQ is specialized in-memory rules engine designed to Validate and reconcile data. Users create and store these rules permanently in the repository.
- A Practical Guide for Data Centric Testing: Automated ETL Testing
- Overcome Data Testing Challenges
- Agile Data Warehouse Testing & Data Migration Testing
- Migrating Database to Redshift, Snowflake, Azure DW and Test with iCEDQ
- Data Migration Testing Techniques to Migrate Data Successfully
- The Data Migration Process & the Potential Risks
- ETL Development & ETL Testing – a Pipeline for Data Warehouse Testing
- ETL Testing and Data Quality Governance Software - The Missing Link
- DataOps Implementation Guide
- AML Software Implementation & Production Monitoring with iCEDQ DataOps Platform
- What Are The Challenges Of A Data Factory
- 3 Reasons Why You Need to Perform ETL Testing
- ETL Testing - Unit Testing vs. Quality Assurance for Data Warehouse
- ETL Testing Vs. Application Testing - The Fundamental Difference