Hyderabad, Telangana, India
Social Good & Community Development
Other
Marlabs
Overview
Date: Apr 1, 2025
Location: Any Marlabs Office Location, IN
Company: Marlabs Innovations Pvt Ltd
Description:
Key Areas of Focus:
Data Pipeline Testing:
ETL/ELT Process Validation: Ensuring data extraction, transformation, and loading (ETL/ELT) processes in Azure Data Factory pipelines are functioning correctly and efficiently.
Data Transformation Accuracy: Verifying that data transformations within pipelines are accurate and produce the expected results.
Data Quality: Testing data quality aspects, such as completeness, consistency, and accuracy, within Azure Data Factory pipelines.
Data Mapping Validation: Ensuring that data mappings between source and target systems are correct and that data is being transferred to the appropriate locations.
Data Migration Testing: Testing the migration of data to Azure Synapse Analytics, including schema conversion and data integrity verification.
Azure Synapse Analytics Testing:
Data Ingestion Testing: Ensuring data is ingested into Azure Synapse Analytics correctly and meets data quality requirements.
Data Transformation Testing: Verifying the accuracy and performance of data transformations within Azure Synapse Analytics.
Report Testing: Testing the functionality and performance of reports created on top of Azure Synapse Analytics data.
Data Validation: Automating data validation in Azure Synapse Analytics using data quality rules and data observability.
Data Storage Testing:
Data Integrity: Verifying the integrity of data stored in Azure Blob Storage, Azure Data Lake Storage, or other Azure storage services.
Data Access and Security: Testing data access permissions and security configurations to ensure data is accessed only by authorized users.
Testing Tools and Techniques:
Azure Load Testing: Using Azure Load Testing to simulate high-scale loads on data pipelines and databases to identify performance bottlenecks.
Test Automation: Automating data pipeline and data transformation tests using tools like Azure DevOps and test frameworks.
Data Profiling and Analysis: Using data profiling tools to analyze data characteristics and identify potential issues.
Regression Testing: Conducting regression testing to ensure that changes to data pipelines or transformations do not introduce new issues.
Collaboration and Communication:
Documentation: Thoroughly documenting test cases, including inputs, expected outputs, and actual results, to facilitate tracking and issue resolution.
Collaboration with Developers and Stakeholders: Working closely with developers and stakeholders to understand requirements and ensure data quality.
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