Pulling data from a source system and putting it into a data warehouse is a process commonly known as extract, transform, and load, or ETL. Testing the process can be a chore—you need to be sure all appropriate data is extracted, that it is transformed correctly to match the data warehouse schema, and that it's all imported. Instead of testing the ETL process as a black box, you can pull it apart, testing each piece in isolation.
Many automation tools have a mechanism for storing data used in their test scripts. Typically, the specifics of this mechanism is different across tools, making it difficult to use this data outside the tool itself. Using an external, reusable data source allows organizations to avoid the cost of migrating or duplicating existing data, thereby future-proofing their frameworks.
You’ve probably heard the buzz about big data and business intelligence data warehouses. Both deal with collecting information for analysis, but how are they different? When should you use one or the other? This article explains these two data solutions in a user-friendly way with real-world examples.
Business analysis is only as good as the quality of the data. If the testing process is weak and the data quality and data integrity tests are suspect, then the business could be at risk. Learn how to get the most out of your data, warehouse, and business intelligence testing.
To fully detect, isolate, and resolve quality issues in a traditional, large-scale data warehouse requires that several approaches be used together. Wayne identifies types of data quality issues and then illustrates how to best attack and resolve those pesky issues.
In this interview, Matt Coatney discusses the importance of asking bold questions, the big misconceptions behind big data, the best way to start your approach to big data, and his vision for a future where technology and big data make the world a better place.
In this interview, Manish Arora demystifies big data by covering some of the biggest misperceptions and pain points held by businesses and SMEs. Arora also talks about his recent article featured on LinkedIn and why it's important to put good teams and technology into proper perspective.
In this interview, Mike Trites, a senior test consultant, talks about his upcoming presentation at STAREAST 2014, the future of metrics, the importance of improving the efficiency of your metrics, and even an interesting take on the old phrase that numbers never lie.
As more applications are hosted on servers, they produce immense quantities of logging data. Quality engineers should verify that apps are producing log data that is existent, correct, consumable, and complete. Otherwise, apps in production are not easily monitored, have issues that are...
Imagine a world where operational data is continuously flowing from applications and devices at an extremely high rate. Now imagine services intercepting this data and analyzing it real time. Sounds futuristic? It's not—it's here today. Mark Richards describes what streaming architecture...