Top 10 ETL interview questions and answers

You will often come across this ETL testing interview question. ETL testers are in high demand because ETL testing is so crucial. Data sources are validated, data is extracted, transformation logic is applied, and data is loaded into target tables by ETL testers. An ETL tester's primary responsibilities are as follows:

• In-depth understanding of ETL tools and methods.
• Thoroughly test the ETL software.
• Examine the data warehouse testing component.
• Carry out the data-driven backend test.
• Create and execute test cases, test plans, test harnesses, and so on.
• Identifies issues and recommends the best solutions.
• Examine and sign off on the requirements and design specifications.
• Creating SQL queries for testing purposes.
• Various sorts of tests, such as primary keys, defaults, and checks of other ETL-related functions, should be performed.
• Carry out frequent quality inspections.

Despite the necessity of ETL testing, businesses may encounter significant difficulties when attempting to integrate it into their systems. ETL testing is difficult due to the volume of data involved or the diverse nature of the data. Some of these challenges are as follows:

• Changes in client requirements necessitate the re-run of test cases.
• Changing client needs may demand a tester creating/modifying new mapping papers and SQL scripts, which can be a time-consuming and labor-intensive procedure.
• Uncertainty over company requirements or personnel who are unaware of them.
• Data loss may occur during migration, making source-to-destination reconciliation problematic.
• A data source that is incomplete or corrupt.
• Incorporating real-time data may have an influence on reconciliation between data sources and goals.
• Due to the enormous number of historical data, the system may experience memory difficulties.
• Testing with ineffective instruments or in an unstable environment

You will often come across this ETL interview question. Several analysis services databases rely on relational schemas, which are defined by the Data source view (the logical model of the schema). Furthermore, it is simple access cubes and dimensions, allowing users to select their measurements in an understandable manner. Without a DSV, a multidimensional model is incomplete. Consequently, you completely control the data structures in your project and may work independently of the underlying data sources (e.g., changing column names or concatenating columns without directly changing the original data source). Every model, no matter when or how it is developed, must have a DSV.

Staging layers, data integration layers, and access layers are commonly used in ETL tool-based data warehouses. The architecture is divided into three layers:

• Data retrieved from numerous data sources is stored in a staging layer, also known as a source layer.
• The data integration layer is responsible for transforming data from the staging layer to the database layer.
• Users can get data for analytical reporting and information retrieval via the Access Layer, also known as a dimension layer.

This is a crucial ETL testing interview question and answer. When data needs to be erased in bulk from the data warehouse, it can be a time-consuming process. Methods of permanently wiping and eliminating data from a data warehouse are referred to as data cleansing. Purging data, as opposed to deletion, involves a variety of procedures and strategies. When you delete data, you're only eliminating it temporarily; when you purge data, you're removing it permanently and freeing up memory or storage space. The data that is frequently eliminated is trash data, such as null values or unnecessary spaces in the row. Users can erase numerous files at once using this method while maintaining efficiency and speed.

Acquiring, cleaning, analyzing, integrating, and sharing data as a method of generating actionable insights and boosting corporate success is what Business Intelligence (BI) is all about. An efficient BI test evaluates staging data, the ETL process, and BI reports, as well as ensures that the implementation is trustworthy. In simple terms, business intelligence (BI) is a technique for gathering raw business data and transforming it into actionable information for a company. The correctness and legitimacy of insights from the BI process are evaluated by BI Testing.

Data cleaning is also known as data cleansing or data scrubbing. This is the process of deleting data from a dataset that is missing, duplicated, corrupted, or wrong. The importance of data cleaning grows when the necessity to combine multiple data sources becomes more evident, such as in data warehouses or federated database systems. Because the particular phases in a data cleaning process differ based on the dataset, creating a template for your process will help you accomplish it correctly and consistently.

This is a crucial ETL testing interview question and answer. The software Online Analytical Processing (OLAP) allows you to evaluate data from several databases at the same time. You can use an OLAP cube to store data in a multidimensional form for reporting purposes. The cubes make it easy to create and evaluate reports and streamline and improve the reporting process. End-users are in charge of monitoring and maintaining these cubes, which require manual data updates.

The cube is one of the critical components in the data processing. Cubes are data processing units that contain dimensions and fact tables from the data warehouse in their most basic form. It gives clients a multidimensional perspective of data, as well as querying and analytical tools.

The types of cubes are:

• OLAP Cubes: These are OLAP cubes built by clients, end-users, or third-party applications using the Microsoft® PivotTable® Service to access a data warehouse, relational database, or OLAP cube.

• Virtual Cubes: These are composites of one or more real cubes that don't take up any disc space. They only store the definitions of the referenced source cubes, not their data. They're similar to relational database views.

• MOLAP Cubes: On the Analysis Server computer, MOLAP cubes store data aggregations and a copy of the fact data in a multidimensional format. When some extra storage capacity on the Analysis Server machine is available and the best query performance is desired, this is the ideal option. MOLAP local cubes can be used offline and contain all of the essential data for calculating aggregates.

This is an important ETL interview question. ODS serves as a data repository between the staging area and the Data Warehouse. When you insert data into ODS, it will load all of it into the EDW (Enterprise data warehouse). The advantages of ODS are mostly related to business operations, as it consolidates current, clean data from numerous sources into a single location. Customers cannot change an ODS database because, unlike other databases, it is read-only.