Top 25 ETL Tester Interview Questions and Answers in 2022


ETL Tester Interview Questions and Answers

ETL is a crucial element in the design of a data warehouse. Through ETL, data is retrieved from source systems, changed into a standardized data type, and fed into a single repository (Extract, Transform, and Load). Data evaluation, evaluation, and qualification are crucial components of ETL testing. After extracting, converting, and loading the data, we do ETL testing to ensure that the final data is imported into the system in the proper format. Before entering your BI (Business Intelligence) reports, it ensures that the data is of the highest quality and gets to its destination securely.

1. What Role Does ETL Testing Play?

Accordingly, the benefits of ETL testing include;

  • ensuring that data is rapidly and effectively converted from one system to another.
  • ETL testing also allows the detection and prevention of data quality problems that may arise during ETL operations, such as duplicate data or data loss.
  • ensuring that the ETL process itself is unhindered and functions smoothly.
  • ensuring that every piece of data used produces the correct output and follows customer needs.
  • ensuring the complete and safe transfer of bulk data to the new location.

2. Describe The ETL Testing Procedure.

The ETL testing procedure comprises many stages, as follows:

  • Analyzing Business Needs: It is essential to comprehend and record the business requirements using data models, business flow diagrams,s, and reports.
  • Locating and Verifying the Data Source: Before moving on, it is crucial to find the source data and carry out preliminary tests such as schema checks, table counts, and table validation to ensure that the ETL process adheres to the requirements of the business model.
  • Design Test Cases and Gathering Test Data: The third step involves creating SQL scripts, establishing transformation rules, and constructing ETL mapping scenarios. Last but not least, check sure the documents meet business requirements by comparing them to them.
  • pre-execution checks are carried out after all test cases have been examined and passed. Our ETL procedures’ three phases—extracting, transforming, and loading—are each covered by a set of test cases.
  • Test execution with bug reporting and closure. If any flaws were discovered in the preceding stage, they are reported to the developer for correction before retesting.
  • Summary Report and Analysis Of the results: A test report is made at this stage, including the test cases and their current condition (passed or failed). This report will help decision-makers understand the bug and the outcome of the testing procedure, allowing them to maintain the delivery threshold as needed.
  • Test completion: The reports are closed once everything is complete.

3. Explain A Few Of The ETL Tools Deployed.

Some of the ETL testing tools I have frequently utilized include;

ETL for business software such as;

Why Should We Hire You? 5 Best Answ...
Why Should We Hire You? 5 Best Answers

  • PowerCenter Informatica
  • InfoSphere DataStage by IBM
  • Data Integrator for Oracle (ODI)
  • SAS Data Manager, SAP Data Services, and Microsoft SQL Server Integration Services.

Accessible ETL

The accessible ETL software I frequently utilize includes;

  • Hadoop,
  • Talend Open Studio,
  • Pentaho Data Integration

4. What Kinds Of ETL Testing Are There?

The types of ETL tests include;

  • Testing for production validation sometimes referred to as “production reconciliation” or “table balancing,” entails checking data in production systems and contrasting it with the source data.
  • Test of Source to Target Count: This verifies that the target is loaded with the expected number of records.
  • Testing of Source to Target Data ensures that no data is lost or truncated during data loading into the warehouse and that the data values are accurate after transformation.
  • Through metadata testing, one can determine whether the source and destination systems have the same schema, data types, lengths, indexes, and constraints.
  • Performance testing: Confirming that data loads into the data warehouse within pre-specified timeframes guarantees speed and scalability.
  • Data transformation testing: This ensures that data transformations are taken following different business rules and criteria.
  • Checking dates, numbers, nulls, accuracy, and other data elements is part of the testing process for data quality. Aside from reference tests, which determine if the data is structured correctly, testing also comprises Syntax Tests, which flag improper characters, and the wrong upper- and lower-case order.

5. What Are An ETL Tester’s Duties And Responsibilities?

My responsibilities as an ETL tester include;

  • A thorough understanding of ETL procedures and tools.
  • Testing of the ETL application.
  • Verification of the test component for the data warehouse.
  • Running of the data-driven test on the backend.
  • Creating and carrying out test plans and test cases, etc.
  • Identifying issues and recommending the best fixes.
  • Reviewing and providing my approval to the design requirements.
  • The creation of SQL queries for testing purposes.
  • Many tests, including those for keys, defaults, and other ETL-related functions, should be run.
  • Performing routine quality inspections

6. Describe Data Mart.

Subsets of an enterprise data warehouse, also known as data marts, can be separated and tailored to a business unit or department. Through the use of data marts, user groups may quickly access data without having to comb through a whole data warehouse.

Each data mart, as opposed to the data warehouse, has a distinct group of end-users. Because constructing a data mart is quicker and less expensive, it is better suited for small companies. A data mart has no duplicate (or useless) data and is regularly updated.

7. Describe The Differences Between A Data Warehouse And Data Mining.

  1. Data warehousing: This technique gathers and arranges data from many sources into a single database to produce insightful business information. Data is merged and consolidated in a data warehouse to assist management decision-making processes. A data warehouse may store integrated data, object-oriented, time-varying, and non-volatile.
  • Data mining: also known as KDD (Knowledge Discovery in Databases), is the process of looking for and spotting hidden patterns in massive data sets that are pertinent and may hold valuable information. Finding previously unidentified links between the data is one of data mining’s key objectives. Data mining allows for the extraction of insights applied to marketing, fraud detection, and scientific research, among other things.

8. How Does Data Purging Work?

Since it may be highly laborious to delete data in mass when it has to be removed from the data warehouse. Data purging describes techniques for permanently deleting and eliminating data from a data warehouse. Data purging, sometimes contrasted with data deletion, encompasses a variety of methods and tactics. When you purge data, you permanently remove it and free up memory or storage space. It is different to delete it, which removes it temporarily. The data eliminates useless information like null values or excessive row spaces. Users can erase many files simultaneously while maintaining efficiency and speed using this strategy.

9. Describe The Differences Between OLAP (Online Analytical Processing) And ETL Technologies.

The ETL tools extract, convert and load data into a data warehouse or mart. Before importing data into the target table, many transformations are required to perform business logic.

Tools for OLAP (Online Analytical Processing): OLAP tools are built to generate reports for business analysis from data marts and warehouses. It imports data into the OLAP repository from the target tables and makes the necessary adjustments for report generation.

10. What Exactly Is A Data Source View?

We use Relational schemas in several analytical service databases, while the data source view is in charge of designing these schemas (the logical model of the schema).

Furthermore, it is simple to generate cubes and dimensions, allowing users to select their measurements easily. Without a DSV, a multidimensional model is lacking. It gives you control over the data structures in your project and the ability to operate apart from the underlying data sources (e.g., changing column names or concatenating columns without directly changing the original data source). No matter when or how a model is made, it must have a DSV.

11. What Does “Business Intelligence” (BI) Mean?

Business intelligence (BI) entails collecting, purifying, integrating, and exchanging data. An efficient BI test validates the staging data, ETL procedure, and BI reports and confirms the accuracy of the implementation. BI is a method for gathering unprocessed business data and turning it into information that a firm can use. The correctness and legitimacy of insights from the BI process are checked by BI Testing.

12. By ETL Pipeline, What Do You Mean?

ETL pipelines are the tools used to carry out ETL. Many procedures or activities are involved when moving data from one or more sources into the data warehouse for analysis, reporting, and data synchronization. To deliver useful insights, it is crucial to transport, combine, and modify source data from many systems to meet the constraints and capabilities of the destination database.

13. Describe The Procedure For Cleansing Data.

Data scrubbing and data cleansing are other terms for data cleaning. It is the procedure for deleting data from a missing, duplicated, flawed, or inaccurate dataset. The importance of data cleansing grows when the necessity to combine numerous information in data warehouses or federated database systems, becomes increasingly evident. Creating a template for your process will guarantee that you follow it accurately and consistently because the particular phases in a data cleaning process may vary based on the dataset.

14. Do You Know What An OLAP Cube Is?

One of the components frequently used in data processing is the cube. Cubes are merely data processing units that hold dimensions and information tables from the data warehouse in their most basic form. It offers clients a multidimensional view of the data, querying, and analytical tools.

On the other hand, Online Analytical Processing (OLAP) software enables simultaneous data analysis from several databases. An OLAP cube can be used to store data in a multidimensional form for reporting needs. The cubes streamline and enhance the reporting process while making it simpler to create and examine reports. These cubes must be updated by end users, who are also in charge of administering and maintaining them.

15. What Exactly Does ODS (Operational Data Storage) Mean?

ODS functions as a store for data in between the staging area and the Data Warehouse. ODS will load all the data into the EDW after the data is inserted into it (Enterprise data warehouse). ODS has several advantages for corporate operations since it consolidates up-to-date, accurate data from various sources. The ODS database is read-only, and clients cannot edit it.

16. Describe The Staging Area’s Principal Function.

Staging areas and landing zones are used as interim storage areas during the extract, transform, and load (ETL) procedure. It serves as a temporary storage space between data warehouses and data sources. The purpose of staging areas is to swiftly collect data from the relevant sources, thus reducing the influence of such sources. After data has been imported, it is merged from various data sources, converted, verified, and cleaned using the staging area.

17. How Does The Snowflake Schema Work?

A snowflake schema is created by adding more dimension tables to a star schema. A primary fact table is surrounded by several tiers of dimension tables in the Snowflake schema paradigm. If a dimension table’s low-cardinality characteristic has been divided into different tables, it is referred to as a snowflake instead. Referential constraints (foreign key constraints) are used to link these normalized tables to the original dimension table. The degree of hierarchy in the dimension tables has a linear relationship with the complexity of the snowflake schema.

18. What Do You Mean By “Bus Schema”?

Dimension identification is a crucial component of ETL, and the Bus Schema handles this task. A BUS schema may be used for managing dimension identification across all organizations and is made up of many validated dimensions and consistent definitions. In other words, just like identifying conforming dimensions (dimensions with the same information/meaning when referring to multiple fact tables), the bus schema identifies the common dimensions and facts across an organization’s data marts. Information is provided in ETL with specified dimensions using the Bus model.

19. What Exactly Are Schema Objects?

A schema typically consists of a collection of database objects, including tables, views, indexes, clusters, database linkages, synonyms, and other elements. It is how the database is logically described or structured. In schema models created for data warehousing, schema objects are structured in many ways. Examples of data warehouse schema models include the star and snowflake schemas.

20. What Advantages Do Data Reader Destination Adapters Offer?

An ADO Recordset is a collection of records from a database table (including records and columns). When filling them out quickly, the Data Reader Destination Adapter comes in extremely handy. It makes the data available in a data flow for consumption by other programs using the ADO.NET DataReader interface.

21. By Factless Table, What Do You Mean?

No facts or measurements are present in factless tables. It only includes dimensional keys and only addresses informative, not calculational, levels of event occurrences. As the name indicates, factless fact tables don’t add any text or numbers, but they represent connections between dimensions. Two types of factual tables may be distinguished: those that describe occurrences and those that describe circumstances. Both might significantly affect the dimensional modeling you do.

22. Describe Scd (Slowly Change Dimension).

In a data warehouse, SCD (Slowly Changing Dimensions) essentially maintains and manages both recent and old data throughout time. SCD evolves gradually over time as opposed to a timetable. One of the components of ETL is SCD.

23. What Is ETL Partitioning?

Partitioning is essentially the division of a data storage space for better performance. We may use it to arrange our work. Without structure, having all your data in one location makes it harder for digital technologies to discover and evaluate the data. When my data warehouse is partitioned, it is simpler and quicker to analyze data.

24. When Using SSIS (SQL Server Integration Services), There Are Several Methods For Updating A Table.

I conduct the following actions in SSIS to update a table:

  • Implementing the SQL command.
  • Using staging tables to store stage data.
  • Keeping the information in a cache that takes up little space and requires regular refreshing.
  • Task scheduling can be done using scripts.
  •  Using the complete database name while upgrading MSSQL.

25. Briefly Describe The ETL Mapping Sheets.

ETL mapping papers often provide complete details about a source and a destination table, including each field and how to look them up in reference tables. At any stage of the ETL testing process, ETL testers may be required to create large queries with several joins to check data. ETL mapping sheets make it much simpler to build data verification queries.

Conclusion

ETL testing is becoming a common trend because there are many work prospects and great income alternatives. It is one of the pillars of data warehousing and business analytics. ETL Testing also has a sizable market share. On the other hand, ETL testing solutions have been offered by several software providers to organize and simplify this process.

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