Introduction To ETL Interview Questions and Answers

ETL stands for Extract-Transform-Load. It is a system foundation of Data warehouse, Where the data is extracted from the different sources and then the data is transformed where the data is enforced or processed so as to make quality, consistency of the data in an appropriate presentation format and then finally the data is loaded in Data Warehouse in presentation format so that it can help end user to get the information easily and can make decision.

If you are looking for a job that is related to ETL, you need to prepare for the 2020 ETL Interview Questions. Though every interview is different and the scope of a job is also different, we can help you out with the top ETL Interview Questions and Answers, which will help you take the leap and get your success in your interview.

Below is the top 2020 ETL Interview Questions that are asked frequently in an interview.

Part 1 – ETL Interview Questions (Basic)

This first part covers basic Interview Questions and Answers

1. Expand the ETL system in the Data warehouse? Explain

Answer:
Extract-Transform-Load (ETL) system is the foundation of the data warehouse. A properly designed ETL system extracts data from the source systems, enforces data quality and consistency standards, confirms data so that separate sources can be used together, and finally delivers data in a presentation-ready format so that application developers can build applications and end users can make decisions.

2. The significance of the ETL system in the Data warehouse?

Answer:
Removes mistakes and corrects missing data. It provides documented measures of confidence in data. Captures the flow of transnational data for safekeeping. Adjusts data from multiple sources to be used together. Structures data to be usable by end-user tools.

3. What is the day-to-day process in the ETL System?

Answer:
Build the development/test/production suite of ETL processes. Understand the tradeoffs of various back-room data structures, including flat files, normalized schemas, XML schemas, and star join (dimensional) schemas. Analyze and extract source data. Build a comprehensive data-cleaning subsystem. Structure data into dimensional schemas for the most effective delivery to end users, business intelligence tools, data-mining tools, OLAP cubes, and analytic applications. Deliver data effectively both to highly centralized and profoundly distributed data warehouses using the same techniques. Tune the overall ETL process for optimum performance.

Popular Course in this category

CloverETL Tutorial (2 Courses)2 Online Courses | 4+ Hours | Verifiable Certificate of Completion | Lifetime Access
4.5 (5,344 ratings)

Course Price

View Course

4. Data formats in the ETL system?

Answer:
There is various data format in ETL some of them are flat files, XML datasets, independent DBMS working tables, normalized entity/relationship (E/R) schemas, and dimensional data models.

5. Data Profiling in ETL System?

Answer:
Data profiling is a systematic examination of the quality, scope, and context of a data source to allow an ETL system to be built. At one extreme, a very clean data source that has been well maintained before it arrives at the data warehouse requires minimal transformation and human intervention to load directly into final dimension tables and fact tables.

6. What is an ETL validator?

Answer:
ETL Validator is a data testing tool that greatly simplifies the testing of Data Integration, Data Warehouse, and Data Migration projects. It makes use of our patented ELV architecture to Extract, Load and Validate data from data sources such as databases, flat files, XML, Hadoop and BI systems.

Part 2 – ETL Interview Questions (Advanced)

Let us now have a look at the advanced Interview Questions.

7. What are the operations in the ETL System?

Answer:
The following three operations: Extracts the data from your transnational system which can be an Oracle, Microsoft, or any other relational database, Transforms the data by performing data cleansing operations. A load is a process of writing the data into the target database.

8. Name some of the ETL tools mostly used in the market?

Answer:
There are various ETL Tools used in the market but some of the moistly preferred ETL Tools

Oracle Warehouse Builder (OWB)
SAP Data Services
IBM Infosphere Information Server
SAS Data Management
Power Center Informatica
Elixir Repertoire for Data ETL
Data Migrator (IBI)
SQL Server Integration Services (SSIS)

9. What are the roles and responsibilities of the ETL Team?

Answer:
The roles of the ETL team at the highest level are to build the back room of the data warehouse.

Deliver data most effectively to end-user tools
Add value to data in the cleaning and conforming steps
Protect and document the lineage of data
Extracting data from the original sources
Quality assuring and cleaning data
Conforming the labels and measures in the data to achieve
consistency across the original sources
Delivering data in a physical format that can be used by query tools,
Report writers, and dashboards.

10. What is the role of impact analysis in the ETL system?

Answer:
Impact analysis examines the metadata associated with an object (in this case a table or column) and determines what is affected by a change in its structure or content. Changing data-staging objects can break processes that are crucial to properly loading the data warehouse. Allowing ad-hoc changes to data staging objects is detrimental to the success of your project. Once a table is created in the staging area, you must perform an impact analysis before any changes are made to it. Many ETL tool vendors provide impact analysis functionality, but this functionality is often overlooked during the ETL product proof-of-concept because it is a back-room function and
Not really important until the data warehouse is up and running and begins to evolve.

11. How do you process the fixed-length flat file?

Answer:
A fixed-length file layout should include the file name, where the field begins; its length; and its data type (usually text or number). Sometimes, the end position is supplied. If it is not, you have to calculate the end position of each field based on its beginning position and length if it is required by your ETL tool. In most ETL tools, you most likely have to manually input the file layout of the flat file once. After the layout is entered, the tool remembers the layout and expects that same layout each time it interacts with the actual flat file. If the file layout changes or the data shifts off of its assigned positions, the ETL process must be programmed to fail. When processing fixed length flat files, try to validate that the positions of the data in the file are accurate. A quick check to validate the positions is to test any date (or time) field to make sure it is a valid date. If the positions are shifted, the date field most likely contains alpha characters or illogical numbers. Other fields with very specific domains can be tested in the same way. XML offers more concrete validation abilities. If data validation or consistency is an issue, try to convince the data provider to deliver the data in XML format.

12.Why Real-Time ETL in Data warehouse?

Answer:
Data warehouse needed to provide an unwavering set of data to business decision-makers, providing a reliable information floor upon which to stand. For up-to-the-moment reporting against a twinkling database, business users were directed to the production applications that run the business. Therefore, users had to go to the data warehouse for a historical picture of what happened in the business as of yesterday and had to look across many OLTP systems for a picture of what was happening today. Business users never fully accepted this divide. Data warehouses of course absolutely need constant customer information streams from an operation, but increasingly operational systems rely on data warehouse enrichment of customer information too. Therefore, it is predictable that organizations have begun to explore architectural alternatives that can support more generalized integration scenarios moving operational data between applications and simultaneously into and out of the warehouse with ever-increasing urgency.