Quality Assurance

Products and applications are being built using polyglot persistence that involves multiple SQL & NoSQL databases to solve a use case. Testing on such diversed persistence storage systems involve good knowledge of SQL to perform complex queries on RDBMS and database proprietary query languages for NoSQL. The following tools and query languages across different databases need to be mastered to perform high quality testing:

MySQL/Oracle/Postgres: Testing using complex SQL queries, validating stored procedures, triggeres, functions, and views, checking constraints and referential integrity, and verify database schema with the reference model for data types, data length, character set, etc..

NoSQL: MongoDB query language to verify JSON documents and collections, CQL for Cassandra, Cypher QL for Neo4j, OrientDB SQL to verify nodes and relationships in OrientDB, Lucene queries to verify documents in ElasticSearch, and others

Tools:Tools such as Compass, Metabase, Talend, Zeppelin, Jupyter, and programming languages like Python and Java are extremely useful while dealing with hybrid database systems

Big Data Testing is the most complex testing process due to huge volume of data flowing thru the system that are processed by multiple tools and technologies from ingestion to analytics. It’s very important to understand the source of the data, multiple transformation and filtering process it goes thru, identifying data lineage graph, different calculations and aggregations performed on the data, and have data sense towards outliers.

Tools and technologies such as Talend, Zeppelin, Jupyter, and decent query abilities on Hive and SparkSQL are necessary to perform testing on Big Data that should cover entire data pipeline as below:

1. Big Data Infrastructure Testing
2. Big Data Governance Testing
3. Big Data Ingestion Testing
4. Big Data Profile Testing
5. Big Data Extract, Load & Transform (ELT) Testing

Continuous development, integration, and deployment with containers on cloud using Agile iterative principles is the proven combination for faster time to market. Testing different moving parts at a fast rate in less than 2-week sprint cycle needs different testing strategies on top of regular full cycle testing such as below:

1. Test the infrastructure & network topology and validate against server operational documents
2. Automate scripts using AWS CLI & SDK to check various attributes and operations such as network in/out, S3 Object TTL, EBS snapshots, AMI frequency, and auto scaling
3. Test user authentication & authorization by accessing AWS services based on IAM users
4. Download and test Docker container images on multiple OS across multiple deployment environments
5. Run Docker console and verify multiple container links and interaction are working as expected
6. Run builds using Jenkins and validate CI & CD are working as expected
7. Test blue-green deployments for continuous deployment with near zero downtime
8. Test feature flags to make sure same applications behave different for different user groups