The first reason emphasizes that the ultimate use of big data is its ability to give us actionable insight.
Poor quality data leads to poor analysis and hence to poor decisions. Errors in data in industries like pharmaceutical companies or banks can regulate regulations leading to legal complications.
it’s also very important for the data to give good quality to gain trust as a leader provider.
Ensuring accuracy of data will lead to correct human engagement and interaction with the data system.
We ensure the quality of the data by rigorous and detailed end to end testing across the different stacks of the Big Data ecosystem. As a team of Big Data SDETs, we understand the nitty-gritties of the Big data development and also masters in testing.
The primary purpose of the Data ingestion testing is to verify that the data adequately extracted from multiple sources and correctly loaded into storage layer or not. The storage can be on the premises HDFS or Azure Data Lake or Google Cloud or AWS S3. Tester has to ensure that the data properly ingests according to the defined schema and also have to verify that there is no data corruption. The tester validates the correctness of data by sampling the source data, and after ingestion, compares both source data and ingested data with each other. We achieve this manually to start with and eventually automate based on the complexities of the project.
Data processing is the core of the Big Data implementation. In this type of testing, the primary focus is on all the types of Big data processing tasks and Big Data Operations. Whenever the ingested data processes, validate whether the business logic is implemented correctly or not. And further, validate it by comparing the output files with input files.
The output stored in HDFS or Azure Data Lake or Google Cloud or AWS S3 or any other warehouse. The tester verifies the output data correctly loaded into the warehouse by comparing the output data with the warehouse data.
Big Data visualization testing involves the format, computations, flow of the analyzed data in tools like Tableau.
It involves validation of data in storage systems – HDFS or any Cloud Storage. It includes the comparison of source data with the added data.
After comparison, process validation involves Business Logic validation, Data Aggregation and Segregation, checks key-value pair generation in different parallel processing models like Apache Spark.
It involves the elimination of data corruption, successful data loading, maintenance of data integrity, comparing the Storage data with target data.