If you're looking for ETL Testing Interview Questions & Answers for Experienced or Freshers, you are in the right place. There are a lot of opportunities from many reputed companies in the world. According to research ETL Testing has an extension market share. So, You still have the opportunity to move ahead in your career in ETL Testing Analytics. Mindmajix offers Advanced ETL Testing Interview Questions 2021 that helps you in cracking your interview & acquire a dream career as an ETL Testing Analyst.
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|Compare ETL Testing and DB Testing|
|ETL Testing||DB Testing|
|Business Intelligence reporting||The goal is to integrate data|
|Business flow environment based on earlier data||Applicable to business flow systems|
|Informatica, Cognos, and QuerySurge can be used||QTP and Selenium tools for automation|
|Analyzing data may have a potential impact||Architectural implementation involves high impact.|
|Dimensional model||Entity-relationship model|
|Analytics are processed||Transactions are processed|
|Denormalized data is used||Data used is normalized|
Ans: ETL stands for Extract Transform Load and is widely regarded as one of the essential Testing tools in the data warehousing architecture. Its main task is to handle data management for the business process which is complex and is useful to the business in many ways. Extracting simply means reading the data from a database. Transformation means converting the data into a form that is suitable for analysis and reporting. The load on the other side handles the process of writing and managing the data into the database which users want to target simply.
Ans: It is called a Session.
Ans: Well, there are certain important tasks that are opted in this. It simply means verifying the data in terms of its transformation in the correct or the right manner as per the needs of a business. It also includes the verification of the projected data. The users can also check whether the data is successfully loaded in the warehouse or not without worrying about the loss of data. The improvement in scalability, as well as the performance, can also be assured from this directly. In addition to this, the ETL simply replaces the default values which are not always useful to the users.
Ans: Power Mart is good to be considered only when the data processing requirements are low. On the other side, the Power Center can simply process bulk time in a short span of time. Power Center can easily support ERP such as SAP while no support of the same is available on the ERP. The local repository can be supported by the Mart while the center cannot perform this task reliably.
Ans: The transactions are always needed to be divided for better performance. The same processes are known as Partitioning. It simply makes sure that the server can directly access the sources through multiple connections.
Ans: There are many tools that can be considered. However, it is not always necessary that a user needs all of them at the same time. Also, which tool is used simply depends on the preference and the task that needs to be accomplished. Some of the commonly used ones are Oracle Warehouse Builder, Congos Decision Stream, SAS Enterprise ETL server, and SAS Business warehouse.
Ans: Basically, it is regarded as a federal component that generally belongs to a model that has multiple dimensions. The same can also be used when it comes to considering the measures that belong to analyzation. The facts are generally useful for providing the dimensions that largely maters in the ETL. The commonly used types of facts in ETL are Semi-additive facts, Additive Facts, as well as Non-additive Facts.
Ans: There are file logs and there is a limit on them when it comes to storing data in them. The Tracing level is nothing but the amount of data that can be easily stored on the same. These levels clearly explain the tracing levels and in a manner that provides all the necessary information regarding the same. There are two types of same and they are:
Ans: Well, the warehousing applications that are important generally include the analytical process, Information processing, as well as Data mining. There are actually a very large number of predictive that needs to be extracted from the database with a lot of data. The warehousing sometimes depends on the mining for the operations involved. Data mining is useful for the analytical process while the other doesn’t. The data can simply be aggregated from the sources that are different through the warehousing approach while the same is not possible in case of mining.
Ans: The fact information can be stored at a level that is known as grain fact. The other name of this is Fact Granularity. It is possible for the users to change the name of the same when the need for the same is realized. There are multiple files that are associated with the same and the users can use this for changing the name of all of them directly.
Ans: Yes, in ETL it is possible. This task can be accomplished simply by using the Cache. The users must make sure that the Cache is free and is generally optimized before it is used for this task. At the same time, the users simply make sure that the desired outcomes can simply be assured without making a lot of effort.
Ans: It is defined as the table without measures in the ETL. There are a number of events that can be managed directly with the same. It can also record events that are related to the employees or with the management and this task can be accomplished in a very reliable manner.
Ans: It is basically regarded as the repository object which is capable to produce the data and can even modify and pass it in a reliable manner. The two commonly used transformations are Active and Passive.
Ans: It is actually very beneficial for the extracting of data and from the systems that are based on legacy.
Ans: Well, they can be called the number data which is generally based on the columns and is generally present in a fact table by default.
Ans: It is one of the finest and in fact one of the very useful approaches in the ETL. It simply makes sure that the users can get a related value from a table and with the help of a column value that seems useless. In addition to this, it simply makes sure of boosting the performance of a dimensions table which is changing at a very slow rate. Also, there are situations when the records already remain present in the table. Dealing with such issues can be made possible with the help of Lookup transformation.
Ans: There are needs and situations when the data needs to be deleted from the data warehouse. It is a very daunting task to delete the data in bulk. The Purging is an approach that can delete multiple files at the same time and enable users to maintain speed as well as efficiency. A lot of extra space can be created simply with this.
Ans: Dimension identification is something that is very important in the ETL and the same is largely handled by the Bus Schema.
Ans: When it comes to updating the master table, the dynamic cache can opt. Also, the users are free to use it for changing the dimensions. On the other side, the users can simply manage the flat files through the Static Cache. It is possible to deploy both the Dynamic and the Static Cache at the same time depending on the task and the overall complexity of the final outcome.
Ans: It is an area that is used when it comes to holding the information or the data temporary on the server that controls the data warehouse. There are certain steps that are included and the prime one among them is Surrogate assignments.
Ans: There are two types of Partitioning that are common in ETL and they are:
Ans: There are ADO recordsets that generally consist of columns and records. When it comes to populating them in a simple manner, the Data Reader Destination Adapter is very useful. It simply exposes the data flow and let the users impose various restrictions on the data which is required in many cases.
Ans: There is a power connect option that simply lets the users perform this task and in a very reliable manner. It is necessary to import the source code in the analyzer before you accomplish this task.
Ans: This is actually an approach that is useful for creating or arranging the different sets in the transformation. It simply let user accomplish other tasks also that largely matters and are related to the data warehouse.
Ans: The data warehouse fact table is the central table of the star schema.
Ans: There are 3 types of measures:
Ans: Grain fact functionality defined as a level/stage where the fact information will be stored. Also called Fact Granularity.
Ans: In ETL, Transformation involves, data cleansing, Sorting the data, Combining or merging, and applying the business rules to the data for improving the data for quality and accuracy in the ETL process.
Ans: The Lookup transformation accomplished lookups by joining information in input columns with columns in a reference dataset. You utilize the lookup to get extra data in a related table that depends on values in common columns.
Ans: Yes, it is actually possible and the users can perform this task simply and in fact without worrying about anything. The users generally have several options to accomplish this task easily. The methods that can be helpful in this matter are using a staging cable, using a SQL command, using MSSQL, as well as using the Cache.
Ans: It is basically a group that contains instructions that let the server perform the executions related tasks.
Ans: Mapping is common in the connected lookups while it is not so common in the latter. It is only used in the Unconnected lookup only when the function is already defined. . There are several values that can be returned from the Connected Lookup while the Unconnected Lookup has a strict upper limit on the same.
Ans: There are several analysis services databases that largely depend on the relational schema and the prime task of the Data source is to define the same. They are also helpful in creating the cubes and dimensions with the help of which the users can set the dimensions in a very easy manner.
Ans: These are basically considered the logical structures that are related to the data. They generally contain tables, views, synonyms, clusters as well as function packages. In addition to this, there are several database links that are present in them.
Ans: There are things on which the data processing depends largely and cubes are one among them, they are generally regarded as the units of the same that provide useful information regarding the dimensions and fact tables. When it comes to multi-dimensions analysis, the same can simply be assured from this. On the other side, the Online Analytics Processing stores a large data in the dimensions that are more than one. This is generally done for making the reporting process more smooth and reliable. All the facts in it are categorized and this is exactly what makes them easy to understand.
|Connected Lookup||Unconnected Lookup|
|Either dynamic or Static Cache can be used.||Can use only Static Cache.|
|We can return multiple rows from the same row||Can return only one output port|
|It supports user-defined values||It won't support user-defined values|
|We can pass any number of values to another transformation.||Can pass one output value to one transformation|
|Cache has all lookup columns that are used in the mapping.||Cache has all the lookups or output ports of lookup conditions and returns ports.|
Ans: In a data warehouse, BUs Schema is used for identifying the most common dimensions in the business process. In one word its is a definite dimension and a standardized equivalent of facts.
Ans: Data Purging - Common word that used in data warehousing for deleting or erasing data from the storage.
Ans: Schema objects can be defined as logical structures, whereas DB stores the schema object logically within a database tablespace. Schema Objects can be the tables, clusters or views, sequence or indexes, functions packages and db links.