Updated June 15, 2023
Introduction To ETL Interview Questions
ETL stands for Extract-Transform-Load. It is a system foundation of a Data warehouse, Where the data is extracted from different sources. 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 get the information easily and can make a decision.
If you are looking for a job that is related to ETL, you need to prepare for the 2023 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 are the top 2023 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
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. What is The significance of the ETL system in the Data warehouse?
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?
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.
4. Data formats in the ETL system?
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?
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?
ETL Validator is a data testing tool that greatly simplifies the testing of Data Integration, Data Warehouse, and Data Migration projects. It uses 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?
The following three operations: Extract the data from your transnational system, which can be an Oracle, Microsoft, or any other relational database, and 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?
- 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?
The roles of the ETL team at the highest level are to build the backroom 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 query tools can use,
- Report writers and dashboards.
10. What is the role of impact analysis in the ETL system?
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.
11. How do you process the fixed-length flat file?
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 input the file layout of the flat file once manually. 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. 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?
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.
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