How Do You Automate Big Data Testing? Everything To Know

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Big Data Automation Testing

Big Data encompasses structured, semi-structured, and unstructured data from various sources, such as text files, images, and audio. Traditional databases struggle with the unstructured nature of this data, making storage, retrieval, and analysis challenging. The five V’s – Volume, Velocity, Variety, Veracity, and Value of data – characterize Big Data, highlighting the scale, speed, formats, trustworthiness, and utility of information.

Key Characteristics of Big Data Automation Testing

Understanding the core characteristics of Big Data is essential:

· Volume: Massive amounts of data collected from diverse sources.

· Velocity: High speed in handling and processing data.

· Variety: Diverse data formats – structured, semi-structured, or unstructured.

· Veracity: Ensuring data legitimacy and trustworthiness.

· Value: The utility and significance of data for analysis.

Big Data Layers

To comprehend the complexities of Big Data, it’s essential to grasp its layered structure:

· Data Source Layer: Accumulates data from various sources.

· Data Storage Layer: Stores collected data.

· Data Processing Layer: Analyzes data to derive insights.

· Data Output Layer: Transfers insights to end-users.

Big Data Automation Testing Strategy

In the realm of Big Data testing, critical areas demand attention to uncover key business insights. Poor data quality can result in errors, revenue loss, and wasted resources. Reports from Experian Data Quality and Gartner underscore the financial implications of neglecting data quality.

Functional Testing

Functional testing evaluates the front-end application based on user requirements. It encompasses three stages:

· Pre-Hadoop Process Testing: Validates data extraction, HDFS loading, file partitioning, and synchronization with source data.

· MapReduce Process Validation: Validates business logic, key-value pair creation, and data compression.

· ETL Process Validation and Report Testing: Ensures data unloading, transformation, and loading into EDW, and validates report output against business requirements.

Non-functional Testing

Non-functional testing focuses on performance and failover scenarios:

· Performance Testing: Evaluates job completion time, memory utilization, data throughput, response time, data processing capacity, and velocity: Assesses performance limitations, storage validation, connection timeout, and query timeout.

· Failover Testing: Verifies seamless data processing in case of node failure and validates the recovery process using metrics like Recovery Time Objective and Recovery Point Objective.

Big Data Automation Testing Approach

Given the complexities of Big Data, automation is a game-changer. Our automation framework operates in two stages:

· Automated Testing: Streamlines the testing process, ensuring accuracy and efficiency.

· Deployment and Analysis: Facilitates deployment and provides powerful business insights, enhancing decision-making.

Embrace the power of automation to conquer the challenges posed by complex and challenging data sets. Please contact Datagaps to begin a robust Big Data Automation Testing journey and unlock your software solutions’ true potential

Big Data Testing is Critical
Are You Looking For Big Data Testing Tools?

Big Data is quickly making science fiction become science fact. Disciplines like machine learning and artificial intelligence were still in the realm of sci-fi even 10 years ago. Now they’re available for anybody to benefit from!

If you’re ready to find out how data-driven tools like Big Data testing can empower you and your business, Sign Up for a Demo today!

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Established in the year 2010 with the mission of building trust in enterprise data & reports. Datagaps provides software for ETL Data Automation, Data Synchronization, Data Quality, Data Transformation, Test Data Generation, & BI Test Automation. An innovative company focused on providing the highest customer satisfaction. We are passionate about data-driven test automation. Our flagship solutions, ETL ValidatorDataFlow, and BI Validator are designed to help customers automate the testing of ETL, BI, Database, Data Lake, Flat File, & XML Data Sources. Our tools support Snowflake, Tableau, Amazon Redshift, Oracle Analytics, Salesforce, Microsoft Power BI, Azure Synapse, SAP BusinessObjects, IBM Cognos, etc., data warehousing projects, and BI platforms.  www.datagaps.com 

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