Big Data Online Testing Training teaches students how to validate, process, and analyze huge datasets that are spread out across different systems. It is all about testing Big Data apps for functionality, performance, and automation.
Level
Advanced
Duration
8 weeks



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Rated #1 Recognized as the No.1 Institute for Big Data Online Testing Training Course. Techpratham gives you a lot of information about testing Hadoop ecosystems, data pipelines, and distributed systems. The course focuses on checking the accuracy of data, the logic behind transformations, scalability, and system performance. Students will learn how to use automation frameworks, test environments, and quality checks on both structured and unstructured data. At the end of the program, participants will know how to plan, run, and keep up with good Big Data testing plans for business projects.
Working professional who is carrying more then 10 years of industry experience.
Access to updated presentation decks shared during live training sessions.
E-book provided by TechPratham. All rights reserved.
Module-wise assignments and MCQs provided for practice.
Daily Session would be recorded and shared to the candidate.
Live projects will be provided for hands-on practice.
Expert-guided resume building with industry-focused content support.
Comprehensive interview preparation with real-time scenario practice.
Introduction to Big Data and Testing
Introduces big data concepts and the role of testing in big data environments.
Big Data Architecture and Components
Explains the architecture, storage, and processing components of big data systems.
Data Ingestion and ETL Testing
Focuses on validating data ingestion and ETL pipelines for accuracy and completeness.
Hive and HBase Testing
Teaches testing strategies for Hive data warehouses and HBase NoSQL databases.
Spark Testing and Validation
Explains testing of Spark applications for performance and data correctness.
Big Data Quality and Data Profiling
Focuses on ensuring data quality, profiling, and governance in big data systems.
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HDFS Data Validation Project
Design and execute test cases to validate large datasets stored in HDFS. The project covers schema validation, data completeness, and error handling in distributed storage.
ETL Workflow Testing for Retail Data
Automate testing of ETL pipelines for a retail company where data flows from raw ingestion to structured reporting. Validate transformation logic, aggregation, and accuracy of business metrics.
Performance Testing of Spark Jobs
Conduct load and scalability testing on Apache Spark jobs running on a cluster. Measure execution times, identify bottlenecks, and ensure optimized performance under heavy data loads.

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