What is the importance of ETL testing?
Following are some of the notable benefits that are highlighted while endorsing ETL Testing:
- Ensure data is transformed efficiently and quickly from one system to another.
- Data quality issues during ETL processes, such as duplicate data or data loss, can also be identified and prevented by ETL testing.
- Assures that the ETL process itself is running smoothly and is not hampered.
- Ensures that all data implemented is in line with client requirements and provides accurate output.
- Ensures that bulk data is moved to the new destination completely and securely.
Name some tools that are used in ETL.
The use of ETL testing tools increases IT productivity and facilitates the process of extracting insights from big data. With the tool, you no longer have to use labor-intensive, costly traditional programming methods to extract and process data.
Technology evolved over time, so did solutions. Nowadays, various ways can be used for ETL testing depending on the source data and the environment. There are several ETL vendors that focus on ETL exclusively, such as Informatica. Software vendors like IBM, Oracle, and Microsoft provide other tools as well. Open source ETL tools have also recently emerged that are free to use. The following are some ETL software tools to consider:
Enterprise Software ETL
- Informatica PowerCenter
- IBM InfoSphere DataStage
- Oracle Data Integrator (ODI)
- Microsoft SQL Server Integration Services (SSIS)
- SAP Data Services
- SAS Data Manager, etc.
Open Source ETL
- Talend Open Studio
- Pentaho Data Integration (PDI)
- Hadoop, etc.
What are the roles and responsibilities of an ETL tester?
Since ETL testing is so important, ETL testers are in great demand. ETL testers validate data sources, extract data, apply transformation logic, and load data into target tables. The following are key responsibilities of an ETL tester:
- In-depth knowledge of ETL tools and processes.
- Performs thorough testing of the ETL software.
- Check the data warehouse test component.
- Perform the backend data-driven test.
- Design and execute test cases, test plans, test harnesses, etc.
- Identifies problems and suggests the best solutions.
- Review and approve the requirements and design specifications.
- Writing SQL queries for testing scenarios.
- Various types of tests should be carried out, including primary keys, defaults, and checks of other ETL-related functionality.
- Conducts regular quality checks.
What are the different challenges of ETL testing?
In spite of the importance of ETL testing, companies may face some challenges when trying to implement it in their applications. The volume of data involved or the heterogeneous nature of the data makes ETL testing challenging. Some of these challenges are listed below:
- Changing customer requirements result in re-running test cases.
- Changing customer requirements may necessitate a tester creating/modifying new mapping documents and SQL scripts, resulting in a long and tedious process.
- Uncertainty about business requirements or employees who are not aware of them.
- During migration, data loss may occur, making it difficult for source-to-destination reconciliation to take place.
- An incomplete or corrupt data source.
- Reconciliation between data sources and targets may be impacted by incorporating real-time data.
- There may be memory issues in the system due to the large volume of historical data.
- Testing with inappropriate tools or in an unstable environment.
What do you mean by data purging?
When data needs to be deleted from the data warehouse, it can be a very tedious task to delete data in bulk. The term data purging refers to methods of permanently erasing and removing data from a data warehouse. Data purging, often contrasted with deletion, involves many different techniques and strategies. When you delete data, you are removing it on a temporary basis, but when you purge data, you are permanently removing the data and freeing up memory or storage space. In general, the data that is deleted is usually junk data such as null values or extra spaces in the row. Using this approach, users can delete multiple files at once and maintain both efficiency and speed.
What is data source view?
Several analysis services databases rely on relational schemas, and the Data source view is responsible for defining such schemas (logical model of the schema). Additionally, it can be easily used to create cubes and dimensions, thus enabling users to set their dimensions in an intuitive way. A multidimensional model is incomplete without a DSV. In this way, you are given complete control over the data structures in your project and are able to work independently from the underlying data sources (e.g., changing column names or concatenating columns without directly changing the original data source). Every model must have a DSV, no matter when or how it's created.
What is BI (Business Intelligence)?
Business Intelligence (BI) involves acquiring, cleaning, analyzing, integrating, and sharing data as a means of identifying actionable insights and enhancing business growth. An effective BI test verifies staging data, ETL process, BI reports, and ensures the implementation is reliable. In simple words, BI is a technique used to gather raw business data and transform it into useful insight for a business. By performing BI Testing, insights from the BI process are verified for accuracy and credibility.
Explain the data cleaning process.
There is always the possibility of duplicate or mislabeled data when combining multiple data sources. Incorrect data leads to unreliable outcomes and algorithms, even when they appear to be correct. Therefore, consolidation of multiple data representations as well as elimination of duplicate data become essential in order to ensure accurate and consistent data. Here comes the importance of the data cleaning process.
Data cleaning can also be referred to as data scrubbing or data cleansing. This refers to the process of removing incomplete, duplicate, corrupt, or incorrect data from a dataset. As the need to integrate multiple data sources becomes more apparent, for example in data warehouses or federated database systems, the significance of data cleaning increases greatly. Because the specific steps in a data cleaning process will vary depending on the dataset, developing a template for your process will ensure that you do it correctly and consistently.
Define Grain of Fact
Accordingly, grain fact refers to the level of storing fact information. Alternatively, it is known as Fact Granularity.
What is fact? What are the types of facts?
It is a central component of a multi-dimensional model which contains the measures to be analysed. Facts are related to dimensions.
Types of facts are
- Additive Facts
- Semi-additive Facts
- Non-additive Facts
