ETL, or Extract, Transform, and Load, is a three-step computer process that involves extracting data, modifying it (cleaning, sanitizing, and scrubbing it), and lastly, feeding it into an output data container. Data may be gathered from many sources and sent to a number of destinations. ETL processing is often performed with software programs, although system administrators can also perform it manually. The entire method is automated using ETL application software, which may be run manually or on a repeating schedule as individual tasks or as a batch of jobs.

The ETL process turned out to be popular in the 1970s and is currently being utilized in data warehousing. ETL solutions usually merge data from many applications (systems) that are typically produced and supported by separate vendors or housed on diverse computer hardware. Other stakeholders are typically in charge of managing and operating the separate systems that consist of the original data. For example, a cost accounting system might include data from payroll, sales, and buying.

What Do ETL Developers Do?

ETL developers generally design, automate, develop, and support complex programs that extract, convert, and load data. More specifically, ETL developers are responsible for the following tasks:

  • ETL developers identify data storage needs. They determine the storage requirements of the enterprise. They need a bird-eye view of the data situation in order to select the optimal option.
  • ETL developers create dependable data pipelines. It is a collection of processes and tools that put forward data to the user. It connects systems and transports data from one format to the next.
  • ETL developers build data warehouses. They create data warehouses customized to an enterprise's needs after determining its needs.
  • ETL developers are in charge of looking after ETL processes. When the data warehouse is complete, they extract and send the data to the new system.
  • ETL developers resolve any warehousing system issues.
  • ETL developers make sure what they develop passes through rigorous testing and quality checks before deploying. They execute tests to confirm its reliability and stability.

ETL vs. ELT - Differences Between the Two

The following are some of the major differences between ETL and ELT:

Parameter

ETL

ELT

Source Data

Allow for the storage of structured data from input sources.

It is suitable for unstructured, organized, and semi-structured data.

Latency

High, since transformations need to be executed before saving data.

Low, because minimal processing is performed before storage in the data warehouse.

Data Size

Ideal for lesser amounts of data.

Can handle massive volumes of data.

Scalability

Can be low since the ETL tool should allow for operation scalability.

High, since ELT tools may be simply adjusted to work with different data sources.

Storage Type

Can be leveraged for cloud storage or on-premises.

Designed specifically for cloud data warehousing.

Flexibility

Low, since data sources and transformations must be established at the start of the process.

High since transformation does not need to be established when integrating new sources.

Storage Requirement

Low since only modified data is saved.

High since raw data is saved.

Maintenance

Might require continuous maintenance if data formats or sources change.

Low maintenance because ELT tools often automate the process.

Compliance with Security Protocols

Simple to implement.

Might require support from ELT tool/data warehouse.

ETL Developer Interview Questions and Answers

ETL is a prominent trend nowadays, with many work prospects and competitive pay packages. To assist job searchers, we have provided a comprehensive collection of ETL interview questions and answers for both freshers and experienced candidates. We took great care to provide detailed answers to each of the queries. In the following parts, we will go over ETL Interview Questions and Answers one by one.

1. Please explain the concept of ETL.

ETL is an acronym that stands for Extraction, Transformation, and Loading. It's an important idea in data warehouse systems. The Data Integration Process is divided into three fundamental phases. Extraction is the process of obtaining information from multiple data sources, such as transactional systems or applications. The process of applying conversion rules to data to make it appropriate for analytical reporting is referred to as transformation. The loading procedure involves transferring data into the destination system, which is the Data Warehouse.

2. What is ETL Testing?

Almost every organization nowadays relies substantially on data, which is a good thing! With reliable and subjective data, we may grasp more than we may with our human minds. What is important is time. Data processing, like any other system, is susceptible to errors. What good is data if part of it is missing, inadequate, or irrelevant?

This is where ETL testing comes into play. ETL is generally recognized as a critical element of data warehouse design in business operations. ETL, or enterprise data integration, takes data from source systems, converts it into a consistent data format, and puts it into a single repository.

It necessitates data validation, qualification, and evaluation following data extraction, transformation, and loading. We do ETL testing to guarantee that the last data was imported into the system correctly. It guarantees that data reaches its destination safely and in excellent shape before inserting it into your Business Intelligence (BI) reports.

3. What are the steps in the ETL Testing process?

1.  Understand Business Requirements

2.  Identify Data Sources

3.  Develop Test Cases

4.  Authenticate Extracted Data

5.  Validate Data Transformation

6.  Verify the Data Loaded

7.  Execute Test

8.  Prepare Summary Report & Result Analysis

9.  Test Closure

4. Name the different types of ETL Testing. 

1.  Production Validation

2.  Source to Target Count Testing

3.  Source to Target Data Testing

4.  Metadata Testing

5.  Performance Testing

6.  Data Transformation Testing

7.  Data Quality Testing

8.  Data Integration Testing

9.  Report Testing

5. Explain the terms data warehousing and data mining.

Data warehousing involves gathering a huge amount of data from different data sources for decision-making. Data mining refers to the extraction of meaningful patterns from the available data.

6. What is partitioning?

Partitioning helps manage database objects better by dividing the storage area and organizing the data more conveniently. When the data warehouse is partitioned, finding and accessing data is faster.   

7. Describe the 3-layer architecture in the ETL cycle.

The 3 layers involved in the ETL cycle are:

1. Staging Layer is the layer that stores data extracted from multiple sources.

2. Data Integration Layer is the layer that transforms the data and moves it from the staging layer to the database layer.

3. Access Layer is the layer used by the end-user to recover data that can be used for analytical reporting.

8. Compare Star Schema and Snowflake Schema.

Both Star and Snowflake Schemas are multidimensional models. Star Schema comprises fact and dimensional tables and has a star-shaped formation. On the other hand, the Snowflake Schema consists of facts tables, sub-dimension tables, and three-dimension tables put together in a formation that resembles a snowflake. While Star Schema is essentially a top-down model, Snowflake schema is a bottom-up model. Compared to Star Schema, the Snowflake schema is more complex, has more foreign keys, and has lesser repetition.

9. Explain what you understand by the term Grain of Fact.

Grain of Fact is the term used to explain the fact level at which information is stored in the fact table.

10. What is Data Purging?

Data purging is the process of permanently deleting and removing data from the data warehouse. Eliminating unwanted data frees up storage and memory space.  

11. What is a factless table?

A factless table is a table that does not have any facts or measures. Its purpose is to demonstrate relationships between dimensions. A factless table does not hold text or numeric data.

12. What is Slowly Changing Dimensions (SCD)?

Slowly Changing Dimensions (SCD) are dimensions that store and manage current and past data in a data warehouse. This data in SCD changes very slowly over time and does not change as per any predefined schedule. 

13. What is a data source view?

A data source view defines the relational schema that is used to carry out analysis in the databases. Cubes and dimensions can also be created using the data source view instead of being built from data source objects. This allows users to construct dimensions inherently and offers superior control over the data structures.

14. Explain what is meant by data cleansing.

Data cleansing is the process of finding and eliminating corrupt and erroneous data along with errors in data due to duplication, and insufficiency. It is also known as data cleansing or scrubbing.

15. What is BI (Business Intelligence)?

Business Intelligence refers to gathering, storage, and analysis of data with the objective of converting raw data into actionable information which can be used to make better business decisions.

16. What is the Importance of ETL Testing?

ETL testing has several significant advantages, including the following:

  • Make sure data is transferred from one system to another swiftly and efficiently
  • ETL testing can also detect and avoid data quality concerns, such as duplicate data or data loss, during ETL processes
  • Verifies the ETL process is not hindered and is functioning smoothly
  • Accurate output is guaranteed by ensuring that all data is implemented following client specifications
  • To guarantee a seamless, error-free transfer

17. When Do We Need the Staging Area in the ETL Process?

An intermediary between the data sources and data warehouse/data mart systems is known as a "storage area." It serves as a temporary repository for data during the data integration process. Data is cleansed and verified for a duplicate in the staging area. The staging area is intended to provide a variety of advantages, but its primary purpose is to be used. As a result, it is utilized to boost productivity by ensuring the integrity and quality of the data.

18. What Are OLAP Cubes And Cubes?

Fact tables and dimensions from the data warehouse make up the building blocks of a cube. It provides numerous analytical choices.

Data gets stored in the multidimensional form in an OLAP cube, which stands for Online Analytical Processing. It's made up of facts known as measurements, which are arranged into categories based on their dimensions.

19. State the roles and responsibilities of an ETL Tester.

The demand for ETL testers is increasing on a daily basis since ETL testing has turned out to be so vital. ETL testers extract information, validate data sources, immediately load data into appropriate tables, and apply transformation logic. Listed below are the essential duties of an ETL tester:

  • In-depth knowledge of ETL tools and processes.
  • Test the ETL program thoroughly.
  • Examine the testing component of the data warehouse.
  • Run the data-driven backend testing.
  • Create and run test cases, test strategies, and test harnesses, among other things.
  • Recognizes problems and recommends the best solutions.
  • Review and approve the requirements and design specifications.
  • Developing SQL queries for testing.
  • A variety of tests, including main keys, defaults, and checks of other ETL-related operations, should be run.
  • Conduct regular quality inspections.

20. Explain data mart.

It is possible to create "data marts" within an enterprise data warehouse to better serve the needs of specific business units or departments. Using a data mart, you can quickly and readily access a subset of a data warehouse's data by a broader range of users than would otherwise be possible. 

In some cases, a company may have a data mart that is linked to the company's purchasing, sales, or inventory functions.

21. What Do You Mean by ETL Pipeline?

Because of its sheer importance, you must expect some ETL pipeline questions in your ETL testing interview questions. ETL pipelines, as the name implies, are the means through which ETL processes are carried out. Some functions and activities are necessary to move data from several sources into the data warehouse for analysis, reporting, and data synchronization. 

Some of the advantages include:

  • ETL pipelines can gain a competitive edge
  • It makes it easier to move data from old systems to new ones
  • It reduces errors, slowdowns, and latency, ensuring that information flows smoothly between systems
  • Data can be easily accessed and used by analysts and decision-makers thanks to the ETL pipeline because it centralizes and standardizes data

22. What Do You Mean by ODS (Operational Data Store)?

Between the data warehouse and the staging area, Operational Data Store functions as a store for data. It will load all of the data into the Enterprise Data Warehouse. The benefits of an Operational Data Store are generally related to corporate operations since it gives clean and updated data from numerous sources in one location. Unlike other databases, the Operational Data Store database is read-only and cannot be updated by users.

23. What Is a Staging Area, And What Is Its Primary Function?

In the ETL process, an intermediary storage space called a staging area or landing zone is utilized. It serves as a buffer between the data source and the data warehouse. When extracting data, staging zones get used mainly to minimize the influence of their respective data sources. 

After data has been loaded, it is transformed, checked, and cleaned in the staging area using several data sources.

24. Explain ETL Mapping Sheets.

Every column in the source and destination tables and their lookup in reference tables are often included in ETL mapping sheets. 

Big queries with several joins may be necessary during ETL testing at any point in the testing process to ensure that the data is correct. ETL mapping sheets make it substantially easier to write data verification queries.

25. How Is ETL Testing Utilized in the Handling of Third-Party Data?

Vendors of all stripes create a diverse range of software for large corporations. As a result, no single vendor is responsible for the entire process. To illustrate, imagine a telecom project in which one business handles invoicing, and another handles customer relationship management (CRM). 

Data feeds from other companies can now be received by CRMs, for example, if a CRM needs billing information from another company. As a result, you can use the ETL method to load data from the feed.

26. What are the advantages of ETL testing?

Some of the notable benefits that are highlighted when promoting ETL Testing are listed below:

  • Ensure that data is moved from one system to another in a timely and effective way.
  • ETL testing may also discover and avoid data quality issues such as duplicate data or data loss during ETL procedures.
  • Assures that the ETL process is proceeding successfully and without interruption.
  • Ascertain that all data is implemented in accordance with client specifications and that the output is accurate.
  • Assures that bulk data is transported safely and completely to the new location.

27. What are the ETL testing activities?

ETL testing entails the following:

  • Checks to see if the data is transformed rightly by the business needs.
  • Make certain that the projected data is put into the data warehouse without data loss or truncation.
  • Check to see whether the ETL application presents incorrect data and uses default settings.
  • Make sure data loads in the proper time range to enhance scalability and performance.

28. Mention the many types of data warehouse applications and the distinction between data warehousing and data mining.

The many sorts of data warehouse applications are as follows:

  • Data mining
  • Analytical processing
  • Info processing

Data mining is the procedure of extracting hidden predictive data from massive databases and interpreting the data, whereas data warehousing may employ a data mine for quicker analytical processing of the data. Data warehousing is the procedure of consolidating data from numerous sources into one common repository.

29. Explain what a staging area is and what it serves.

Data staging is the temporary storage of data on a data warehouse server. The following stages are involved in data staging.

  • Surrogate key assignments
  • Data transformation (value transformation, data cleansing)
  • Data transformation and source data extraction (restructuring)

30. Explain the terms - Workflow, Mapplet, Worklet, and Session.

  • Workflow: It is a collection of instructions that instructs the server on how to carry out tasks.
  • Mapplet: It organises or produces transformation sets.
  • Worklet: It denotes a certain collection of tasks.
  • Session: It’s a set of parameters that instructs the server on how to transport data from sources to destinations.

31. What exactly is real-time data warehousing?

  • When it comes to data warehousing, the entire warehouse is refreshed each time the system makes a transaction.
  • It displays real-time business data.
  • This implies that when the query is executed in the warehouse, the current status of the business will be returned.

32. Explain what's an ETL validator.

An ETL validator is generally a data testing tool that simplifies the testing of Data Warehouse, Data Migration, and Data Integration projects. It leverages patented EV architecture in order to extract, load, as well as validate data from databases, XML, flat files, BI systems, and Hadoop.

33. What function does impact analysis play in the ETL system?

Impact analysis usually examines the metadata connected with an object (such as a column or a table in this case) and evaluates what is affected by a change in its content or structure. Changing data-staging items might disrupt critical operations for correctly loading the data warehouse. Permitting ad-hoc modifications to data staging objects is damaging to the success of your project. Before making any modifications to a table that has been generated in the staging area, you must do an impact analysis. Most ETL tool vendors put forward impact analysis capabilities, but this feature is frequently missed during the ETL object proof-of-concept process since it is a backroom function that is not really significant until the data warehouse is seamlessly up and running and starting to expand.

34. Which SQL statements may be used to validate data completely?

To validate data completeness, utilise the intersect and minus commands. When you run a score minus target, as well as target minus score along with the minus query, and return a result, it indicates there are mismatched rows. 

If the minus query gives back a value and the count intersect is s lesser than the target table or the score count, then there is a duplicate record.

35. Explain the working of Hash Partitioning.

The Informatica server in Hash Partitioning would utilize a hash function in order to split keys to arrange data among partitions. It is used to verify that all rows in the same partition have the same partitioning key. 

36. What is a Data Pipeline?

Any processing unit that transmits data from one system to another is referred to as a data pipeline. A Data Pipeline may help any application that uses data to deliver value. It may be used to connect data across applications, generate data-driven online products, and undertake data mining activities. The data pipeline is built by data engineers.

37. What exactly is Data Profiling in the concept of an ETL System?

Data profiling is a thorough examination of a data source's quality, breadth, and the context in order to build an ETL system. At one extreme, a clean data source that's been meticulously maintained before arrival at the data warehouse needs fewer transformations as well as human involvement to load straight into final dimension tables and fact tables.

38. What data types are used in the ETL system?

XML datasets, flat files, normalized E/R (entity/relationship) schemas, separate DBMS working tables, and dimensional data models are a few of the data formats utilized in the ETL.

39. How does a dimension table differ from a fact table?

Fact tables include statistics and information about a company. Dimension tables are linked to fact tables by a foreign key and display the descriptive properties of the facts contained inside them. Dimension tables are wordy and complex, whereas fact tables are granular.

40. What are some ETL testing challenges?

To minimize bottlenecks later in the pipeline, it is critical to identify possible difficulties early in the ETL process. Some of the most typical difficulties and challenges of ETL testing are:

  • Data loss, corruption, or duplication during transit
  • Underperformance as a result of massive amounts of past data
  • Unachievable business objectives
  • Source data is in short supply.
  • Outdated ETL tools.

41. What exactly is regression testing in ETL?

Regression testing is utilized after creating functional fixes to the data warehouse. Its prime objective is to determine whether the repairs have hampered other aspects of the ETL procedure. Regression testing must always be conducted following system modifications to ensure that no new issues have been introduced.

FAQs

1. Name some of the ETL test cases.

1.  Structure Validation

2.  Validate Constraints

3.  Null Validation

4.  Data Validation Check

5.  Data Consistency Check

6.  Duplicate Validation

2. Which are the ETL partition types?

1.  Round-robin Partitioning

2.  Hash Partitioning

3. What is a data mart?

A data mart is a subset of a data warehouse that is dedicated to a business function or department. It makes it easier for users to retrieve specific data rather than searching the entire data warehouse.

4. What is a materialized view?

A materialized view is a database object that includes the result of a query. It pre-calculates the result and makes data fetching faster.

5. What are Schema Objects?

Schema objects are database objects such as tables, views, indexes, synonyms, database links, etc. These are the logical structures created by users that can either hold data or definitions and are arranged in models such as star or snowflake schema.  

6. What is a Bus Schema?

A BUS schema seeks standard dimensions across business processes in the same way that compliant dimensions are recognized. It contains a standard definition of facts as well as a standard dimension.

7. What is a workflow?

A set of instructions that explains how to execute a task is referred to as a workflow.

8. What is a session?

A session refers to the set of instructions that describes the flow of data from source to destination.

9. What do you understand from the term Mapping?

Mapping depicts the flow of data from source to target.

10. Describe the term Mapplet in brief.

A mapplet is a reusable object that creates and builds a set of transformations.

11. Explain the term worklet.

A set of tasks is referred to as a worklet.

12. Name some of the ETL tools available.

1.  Informatica PowerCenter

2.  IBM InfoSphere DataStage

3.  Oracle Data Integrator

4.  Talend Open Studio

5.  SAP Data Services

6.  SAS Data Manager

7.  Microsoft SQL Server Integration Services

8.  Hadoop

9.  Pentaho Data Integration

10.  Azure Data Factory

13. What are the types of facts in ETL?

1.  Additive

2.  Semi-Additive

3.  Non-Additive

14. Name some of the ETL bugs.

1.  Calculation Bug

2.  Input/Output Bug

3.  Source Bug

4.  Version Control Bug

5.  User Interface Bug

6.  Load Condition Bug

15. How many fact tables are there in Star Schema?

One

16. What are the modules in Power Mart?

1.  PowerMart Designer

2.  Server

3.  Server Manager

4.  Repository

5.  Repository Manager 

17. What are the 5 stages of ETL testing?

ETL testing consists of five stages:

  • Determining data sources and requirements
  • Data acquisition
  • Implementing Business logic and Dimensional Modeling
  • Compile and populate data
  • Create Reports

18. What are the three layers in ETL testing?

Typical ETL testing for data warehouses use staging areas, data integration layers, and access layers to accomplish their tasks.

19. What is ETL in testing?

ETL means extracting data from a source system, transforming it into a standard data type, and loading it into a single repository.

20. Is SQL required for ETL testing?

Yes. When performing ETL testing, you often compare tens of millions of records. It is necessary to test data from a variety of sources. Complex SQL queries may be required when comparing the data because of the frequent transformation of the data.

21. What are the ETL process interview questions?

With a high demand for ETL testing comes a high need for expertise to carry out these ETL testing processes. There is various employment accessible for this procedure nowadays.

However, you will only be considered for this position if you are well-versed in technological features and applications. To provide your best attempt, you must be well-versed in these fundamental ideas of ETL tools, methodologies, and procedures.

The following are some often-asked questions in ETL testing interviews:

  • What is ETL?
  • Why is ETS Testing needed?
  • What are the responsibilities of an ETL tester?
  • What are Dimensions?
  • Explain ETL mapping sheets.
  • State down a few ETL bugs.

22. What ETL developers must know?

An ETL developer should have:

  • Experience with ETL tools
  • A background in Architect/DBA/Database
  • Expertise in data analysis
  • Background in software engineering
  • Troubleshooting skills

23. Does an ETL developer require coding?

An ETL developer must have a minimum of two years of programming experience in at least one of the programming languages. They also require experience with ETL tools, as well as information relocation and data aggregation.

24. What is the ETL life cycle?

ETL is basically the procedure of taking out data from non-optimized data sources and shifting it to a centralized host. The specific phases of the operation may differ with ETL tools, but the end result is the same.

The ETL process necessitates data extraction, transformation, as well as loading. While the term suggests a clean three-step procedure - extract, transform, and load - this simplistic explanation misses:

  • The transmission of data
  • Each of these stages overlaps with one another.
  • What new technologies are doing to alter this flow?

Conclusion

Because of the plentiful career prospects and excellent income choices, ETL testing has become a popular trend. ETL Testing is one of the pillars of data warehousing and business analytics, with a considerable market share. Many software vendors have included ETL testing tools to help organize and simplify this process. The majority of enterprises seeking ETL testers want people with certain technical abilities and expertise. No worries; this blog on the list of ETL interview questions will help you prepare for your ETL interview.

This field provides several job opportunities with excellent benefits and pay. And if you are interested in making a career in this domain, Simplilearn can help you get started on the path that will lead to a long and successful career. 

With Simplilearn's Data Science Training and Certification Courses, you may get ahead of the competition and become a part of this dynamic domain, promoting the successful transformation of the corporate world through excellent raw data analysis. Begin right away - the data science tutorial as well as your bright future await!

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