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Top 6 Key Points for Big Data Testing by Calidad Infotech: Data Ingestion, Storage, Processing, Quality, Performance, and Security Testing. Ensuring robust, efficient, and secure handling of large-scale data systems.<br>For more: https://calidadinfotech.com/big-data-testing/
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TOP 6 KEY POINTS FOR BIG DATA TESTING www.calidadinfotech.com
DATA INGESTION TESTING: Objective: Verify that data is correctly imported from various sources into the Big Data system. Actions: Test the data pipelines, ensuring that data is accurately captured, transformed, and loaded (ETL) without loss or corruption.
DATA STORAGE TESTING: Objective: Ensure that data is properly stored and accessible in the Big Data storage systems, such as HDFS, NoSQL databases, or data lakes. Actions: Validate the storage architecture, test data replication, and confirm that the system can handle the expected volume of data.
DATA PROCESSING TESTING: Objective: Validate the data processing logic, such as MapReduce jobs, Spark processing, or real-time stream processing. Actions: Check that the processing workflows execute as expected, processing data correctly and within acceptable time limits.
DATA QUALITY TESTING: Objective: Assess the quality of the data to ensure it is accurate, complete, consistent, and reliable. Actions: Run tests to identify duplicates, missing values, data format issues, and data integrity problems. Automated Software Testing
PERFORMANCE TESTING: Objective: Evaluate the performance of the Big Data system under various data loads. Actions: Conduct tests to measure system response times, throughput, and resource utilization, ensuring the system can handle large-scale data efficiently.
SECURITY TESTING: Objective: Ensure that the Big Data system is secure and that sensitive data is protected. Actions: Test access controls, encryption, data masking, and other security measures to prevent unauthorized access or data breaches.
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