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• In order to update the master table and slowly CHANGING dimensions (SCD) type 1, it is necessary to use the dynamic cache.
• In the case of flat files, a static cache is used.

With ABUNDANT job opportunities and lucrative salary options, ETL testing has become a popular trend. ETL Testing has an extensive market share and is one of the cornerstones of data warehousing and business analytics. To make this process more organized and simpler, many software vendors have introduced ETL testing tools. Most employers who seek ETL testers look for candidates with specific technical skills and experience that meet their needs. No worries, this platform is a great resource for both beginners and professionals. In this article, we have covered 35+ ETL testing interview questions ranging from freshers to EXPERIENCED level questions typically asked during interviews. Preparation is key before you go for your job interview. 

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Data migration projects commonly use ETL tools. As an example, if the organization managed the data in Oracle 10g earlier and now they want to MOVE to SQL Server cloud database, the data will need to be migrated from Source to Target. ETL tools can be very HELPFUL for carrying out this type of migration. The user will have to spend a lot of TIME writing ETL code. The ETL tools are therefore very useful since they make CODING simpler than P-SQL or T-SQL. Hence, ETL is a very useful process for data migration projects. 

Different kinds of vendors develop different kinds of applications for big companies. Consequently, no single vendor manages everything. Consider a Telecommunication project in which billing is handled by one COMPANY and CRM by another. For instance, if a CRM requires DATA from the company that is managing the billing, now that company will be able to receive the data feed from another company. In this case, we will use the ETL PROCESS to load data from the feed.

Typically, ETL mapping sheets include full information about a SOURCE and a destination table, including every column as well as their LOOKUP in reference tables. As PART of the ETL testing process, ETL testers may NEED to write big queries with multiple JOINS to validate data at any point in the testing process. Data verification queries are significantly easier to write using ETL mapping sheets. 

Among the most common ETL test cases are:  

• Mapping DOC Validation: Determines whether the Mapping Doc contains ETL information.
• Data Quality: In this case, every aspect of the data is tested, including number Check, Null Check, Precision Check, etc.
• Correctness Issues: Tests for MISSING, incorrect, non-unique, and null data.
• Constraint Validation: Make sure that the CONSTRAINTS are PROPERLY defined for each table.

In ORDER to update a table in SSIS, the following steps can be taken:  

• Use the SQL command.
• For storing stage DATA, use staging tables.
• Keep data in a cache that occupies a limited amount of space and needs to be refreshed frequently.
• Scripts can be used for scheduling tasks.
• When UPDATING MSSQL, use the full database NAME.

Essentially, partitioning is the process of dividing up a data storage area for IMPROVED performance. It can be used to organize your work. Having all your data in one place without organization makes it more difficult for DIGITAL tools to find and analyze the data. It is easier and faster to locate and analyze data when your data warehouse is partitioned. The following reasons make partitioning important: 

• Facilitate easy data MANAGEMENT and enhance performance.
• Ensures that all of the SYSTEM's requirements are balanced.
• Backups/recoveries made easier.
• Simplifies management and optimizes hardware performance.

Types of Partitioning -

• Round-robin Partitioning: This is a method in which data is evenly spread among all partitions. Therefore, each partition has approximately the same number of ROWS. Unlike hash partitioning, the partitioning columns do not need to be specified. New rows are assigned to partitions in round-robin style.
• Hash Partitioning: With hash partitioning, rows are evenly distributed across partitions based on a partition key. Using a hash function, the server creates partition keys to group data.