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Data Migration Interview Questions
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Data Migration Interview Questions 

The career scope of a data migration specialist is ever-growing. Leading recruiters have a lot of openings for data migration experts with good pay. Also, you cannot forget that data migration is a highly competitive domain. You are likely to get surrounded by people having in-depth migration knowledge when appearing for an interview. Understandably, it can be a challenging time for you if you aren’t prepared enough. Sounds scary, right? Don’t worry!

The merry news is that you can prepare for such hiring processes and edge out your competitors effortlessly. All you need is to get a glimpse of the most common data migration interview questions and get more prepared than ever. Here’s how an interview questionnaire can benefit you.

  • They make you more confident for a data migration job interview.
  • You can identify your strong and weak concepts and re-prepare accordingly.
  • They assist you in maintaining a calm temperament before interviews.

Now, it’s time to walk through the questionnaire regardless of whether you are a self-learner or a Data Science bootcamp grad. Let’s get started!

Top 18 Data Migration Interview Questions

Here’s the ultimate data migration interview questionnaire, which you can refer to after wrapping up your preparations. Follow along!

1. Mention the difference between ETL and data migration?

Data migration and ETL involve moving data from a source to the destination. However, migration retains the data format, whereas ETL changes it in the “extract” process.

2. What are some of the data migration challenges?

Some of the challenges in a data migration process are:

  • Not knowing everything about the data source
  • Undermining the data analysis process
  • Inefficient use of integrated processes
  • Lack of a suitable data migration strategy
  • Lesser or no collaboration among teams
  • Insufficient use of data migration expertise

3.  How to ensure data integrity during migration?

We can maintain data integrity during migration by the following measures:

  • Ensuring rigorous quality control
  • Creating and monitoring the audit trails
  • Designing process maps
  • Mitigating security vulnerabilities during migration
  • Implementing error detection software

4. Explain the data migration process in the SQL server.

Typically, a data migration process in an SQL server comprises four steps.

  • Data extraction from source to an intermediate server
  • Changing data formats to the one prescribed at the destination
  • Cleansing and aggregating data
  • Loading the cleansed and aggregated data to the desired database

5. What are the things to consider in a data migration plan?

We must keep an eye on the following parameters during migration:

  • Auditing the source database is essential and should not be overlooked.
  • Cleansing the dataset is a prerequisite to further migration processes.
  • Maintaining data integrity is a must for high-performing migration strategies.
  • Auditing and governance should lie parallel during all data migration steps.

6. How do you clean a database before migration?

The steps to clean a database are:

  • Filtering out unnecessary data from a database
  • Evaluate if the database has invalid data
  • Replacing or removing missing data
  • Investing in data backup for unexpected accidents

7. What is the meaning of data cleansing in a migration process? Why is it important?

Data cleansing refers to the process of determining missing or invalid records in a dataset and modifying the columns and tuples accordingly. Cleansing is an essential step in data migration as it enhances database quality, making the process more productive.

8.  What is dirty data? Give some examples.

Data that is responsible for the loss of data integrity in a database is called dirty data. Some examples of dirty data are misspelled words, typing mistakes, redundant data, and so on.

9.  What are the differences between data cleansing and processing?

The differences between data cleansing and processing are:

  • Data processing is the process of collecting and manipulating data for further steps. In contrast, data cleansing is the method of eliminating invalid data from a database.
  • Data processing comes after data cleansing.
  • Data processing relies on hardware components like RAM and GPU. However, data cleansing has no such requirements.
  • Data cleansing is a more straightforward process than data processing.

10. What are the factors determining the time taken in a data migration process?

The factors determining the length of a data migration process are:

  • The size of the database
  • The speed of data transfer between the source/intermediate server to the target database
  • Occurrence and removal of errors in the database
  • The chosen data migration path.

11. Name any four types of data migration.

The four types of data migration are:

  • Storage migration
  • Application migration
  • Cloud migration
  • Database migration

12. Why is data migration necessary?

Data migration is necessary because:

  • It lets you upgrade to the latest infrastructure.
  • It ensures cost-effectiveness and security.
  • It allows you to scale according to current paradigms.

13. Do you know the difference between data migration and data conversion?

Data migration is transferring data from a source to the destination while maintaining its integrity. In contrast, data conversion involves moving the data from one format to the other.

14. What do you mean by the incremental load in ETL?

Incremental load refers to applying dynamic changes within a stipulated deadline or regularly in installments.

15. What is an operational data store?

The intermediate storage between data staging and warehousing is the operational data store. We can find data having low granularity in the Operational data store.

16. What is data integrity?

Data integrity is the process of maintaining consistency and precision of data throughout the data migration lifecycle. A strategy that supports data integrity is a good one.

17. Name some ETL tools.

Some of the best ETL tools are:

  • Business Objects XI
  • SAP Business Warehouse
  • Oracle Warehouse Builder
  • Cognos Decision Stream
  • SAS Enterprise ETL Server

18. What do you mean by tracing level?

Tracing level refers to the amount of data that a log file contains. It has two types:

  • Normal
  • Verbose

You are all set to slay at a data migration job interview once you prepare the above questions. Besides, you can practice some SQL and ETL questions to remain on the safest side. Also, the interviewer might ask you questions about your experiences with data migration. Finally, walk into the interview confidently and bag any job effortlessly. Good luck!

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