Big Data Testing: The Solution to Deal With Volume, Velocity, and Variety
With huge volumes of data getting generated in most processes, Big Data Solutions and Big Data Testing is becoming the trend ahead.
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Big Data typically refers to data that is more than one terabyte. Along with high volume, it is also characterized by high velocity and variety. As it includes different variety of formats, including structured, unstructured, and semi-structured, the testing of such Big Data has to be defined accordingly. With huge volumes of data getting generated in most processes, Big Data Solutions and Big Data Testing is becoming the trend ahead.
Stages in Big Data Testing
Big Data Testing primarily comprises three broad-level stages:
- Data Staging and Validation
The first stage collects data from different sources and stores it in a big data storage, followed by matching the data from the source to the Hadoop storage. This is followed by taking the right data and loading it in the proper Hadoop Distributed File System (HDFS).
- Business Logic Validation
The second stage verifies data and business logic at multiple nodes. It is repeated several times to ensure that data aggregation, as well as segregation rules, are working as defined. Further, MapReduce checks the algorithms if they are working properly. It then further triggers a validation process to check the output. This is followed by the validation of MapReduce. Further, the authentication process is triggered at multiple nodes.
- Output Validation
The third stage checks and verifies the transformation logic, data integrity, and key-value pairs for accuracy. In this stage, the output is verified if the data is contiguous and intact and then moved to the database or data warehouse.
Big Data Testing can be used for unit testing, functional testing, performance testing, and fail-over testing.
Tools for Big Data Testing
A wide range of tools exists for Big Data Testing. A different set of tools are used for different processes:
- For data ingestion, the tools used are Kafka, Nifi, and ZooKeeper.
- For data processing, the tools used are Athena, MapR, Hive, and Pig.
- For data storage, the tools used are Amazon S3 and HDFS.
- For data migration, the tools used are Talend, Kettle, CloverDX, and S3 Glacier.
- For test automation, the tools used are Spark and Python.
Best Practices for Big Data Testing
- Define the test objective.
- Plan coverage of the entire load for testing at the beginning itself instead of taking a sampling approach.
- Retrieve different patterns and learnings from drill-down charts.
- Use MapReduce process validation at every stage.
- Integrate testing based on requirements.
- Fix bugs on time.
- Stay within context.
- Automate to the maximum extent possible.
Due to huge amounts of data that gets generated in most processes, big data solutions and big data testing is becoming the norm. Though the testing is conducted in stages, it must be an overall integrated approach.
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