How Automation of Big Data Testing can help avoid the pitfalls of Bad Data

Presented by

Eric Smyth, Director of Partner Services, RTTS and Joseph John, Test Architect, UST Global

About this talk

At the frontier of Big Data and the real-time decision-making, Quality Assurance teams are fighting multiple battles. 1. Volume: Need for tools and expertise to test data amounting to TeraBytes, ZetataBytes, and beyond. 2. Variety: Testing semi-structured and unstructured data generated from Tweets, Google search queries, Connected Car or IoT Sensors, Internet/Mobile Banking transactions. 3. Velocity: Need for speed in testing for data streaming at light speed from devices, digital platforms, transactions, and networks. 4. Veracity: Critical role of testing teams in ensuring conformity, accuracy, consistency, and validity of Big Data. It is the holy grail of the four V’s of Big Data that has made ETL and Data Warehouse testing teams the new stakeholders of the decision-making ability of your organization. After-all, no organization can afford to make decisions regarding new product launches, customer engagements, or digital transformation based on Bad Data. The virtues of Big Data also make a business case for Automation of Big Data Testing. So, how are Quality Engineering teams adopting the required tools and developing expertise to liberate their organizations from bad data? Join us for this interesting discussion to find some answers. You will be hosted by: 1. Eric Smyth, Director, Educational Services & Partner Alliances, RTTS. 2. Joseph John, Test Specialist, UST Global. We will discuss: 1. Challenges related to Testing of Data in a Big Data Project: a. How to address the need to validate a large Volume of Data. b. How to also validate all the transformations of this Volume of Data, throughout the Big Data architecture. c. How to test data streaming services. 2. Find out how these challenges can be overcome by developing expertise while adopting the right tools specifically designed to validate Big Data architectures. 3. Demo of how these tools can be integrated into your DataOps pipeline.

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