Data relevance-based data retention in data lakehouses

1. A computer-implemented method for data relevancy-based data retention, the computer-implemented method comprising:

performing, by a computer, using a machine learning model, an analysis of a data retention relevancy value assigned to data;

determining, by the computer, using the machine learning model, whether the data retention relevancy value of the data needs to be adjusted based on the analysis;

adjusting, by the computer, using the machine learning model, the data retention relevancy value of the data in accordance with the analysis in response to the computer, using the machine learning model, determining that the data retention relevancy value of the data does need to be adjusted based on the analysis;

assigning, by the computer, the data to a logical data retention relevancy compartment of a plurality of logical data retention relevancy compartments in a data lakehouse;

determining, by the computer, whether the data retention relevancy value of the data is greater than a data retention threshold level of a data retention policy corresponding to the logical data retention relevancy compartment storing the data; and

transferring, by the computer, the data from a physical data storage unit corresponding to the logical data retention relevancy compartment of the plurality of logical data retention relevancy compartments in the data lakehouse to an archive in response to the computer determining that the data retention relevancy value of the data is greater than the data retention threshold level of the data retention policy corresponding to the logical data retention relevancy compartment storing the data.

2. The computer-implemented method of claim 1 , further comprising:

determining, by the computer, whether a query was received to access the data transferred to the archive; and

restoring, by the computer, the data to the physical data storage unit corresponding to the logical data retention relevancy compartment of the plurality of logical data retention relevancy compartments in the data lakehouse previously storing the data in response to the computer determining that a query was received to access the data transferred to the archive.

3. The computer-implemented method of claim 1 , further comprising:

determining, by the computer, using a machine learning model, physical data storage unit needs of the data lakehouse based on a data storage unit restore frequency of the data and an ingestion rate of the data; and

adjusting, by the computer, a number of a plurality of physical data storage units in the data lakehouse based on the physical data storage unit needs of the data lakehouse.

4. The computer-implemented method of claim 1 , further comprising:

determining, by the computer, data retention relevancy of the data based on at least one of query frequency of the data, ingestion rate of the data, ingestion time of the data, source of the data, repetition of the data, and visibility of the data in each respective data storage unit of a plurality of data storage units in the data lakehouse; and

assigning, by the computer, the data retention relevancy value to the data based on the data retention relevancy of the data.

5. The computer-implemented method of claim 1 , further comprising:

receiving, by the computer, the data from a plurality of data sources in a landing zone of the data lakehouse corresponding to the computer; and

assigning, by the computer, a default data retention period to the data received from the plurality of data sources.

6. The computer-implemented method of claim 1 , wherein the logical data retention relevancy compartment has a data retention policy that corresponds to the data retention relevancy value of the data.

7. A computer system for data relevancy-based data retention, the computer system comprising:

a communication fabric;

a storage device connected to the communication fabric, wherein the storage device stores program instructions; and

a processor connected to the communication fabric, wherein the processor executes the program instructions to:

perform, using a machine learning model, an analysis of a data retention relevancy value assigned to data;

determine, using the machine learning model, whether the data retention relevancy value of the data needs to be adjusted based on the analysis;

adjust, using the machine learning model, the data retention relevancy value of the data in accordance with the analysis in response to determining, using the machine learning model, that the data retention relevancy value of the data does need to be adjusted based on the analysis;

assign the data to a logical data retention relevancy compartment of a plurality of logical data retention relevancy compartments in a data lakehouse;

determine whether the data retention relevancy value of the data is greater than a data retention threshold level of a data retention policy corresponding to the logical data retention relevancy compartment storing the data; and

transfer the data from a physical data storage unit corresponding to the logical data retention relevancy compartment of the plurality of logical data retention relevancy compartments in the data lakehouse to an archive in response to determining that the data retention relevancy value of the data is greater than the data retention threshold level of the data retention policy corresponding to the logical data retention relevancy compartment storing the data.

8. The computer system of claim 7 , wherein the processor further executes the program instructions to:

determine whether a query was received to access the data transferred to the archive; and

restore the data to the physical data storage unit corresponding to the logical data retention relevancy compartment of the plurality of logical data retention relevancy compartments in the data lakehouse previously storing the data in response to determining that a query was received to access the data transferred to the archive.

9. The computer system of claim 7 , wherein the processor further executes the program instructions to:

determine, using a machine learning model, physical data storage unit needs of the data lakehouse based on a data storage unit restore frequency of the data and an ingestion rate of the data; and

adjust a number of a plurality of physical data storage units in the data lakehouse based on the physical data storage unit needs of the data lakehouse.

10. The computer system of claim 7 , wherein the processor further executes the program instructions to:

determine data retention relevancy of the data based on at least one of query frequency of the data, ingestion rate of the data, ingestion time of the data, source of the data, repetition of the data, and visibility of the data in each respective data storage unit of a plurality of data storage units in the data lakehouse; and

assign the data retention relevancy value to the data based on the data retention relevancy of the data.

11. The computer system of claim 7 , wherein the processor further executes the program instructions to:

receive the data from a plurality of data sources in a landing zone of the data lakehouse corresponding to the computer system; and

assign a default data retention period to the data received from the plurality of data sources.

12. A computer program product for data relevancy-based data retention, the computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:

perform, using a machine learning model, an analysis of a data retention relevancy value assigned to data;

determine, using the machine learning model, whether the data retention relevancy value of the data needs to be adjusted based on the analysis;

adjust, using the machine learning model, the data retention relevancy value of the data in accordance with the analysis in response to determining, using the machine learning model, that the data retention relevancy value of the data does need to be adjusted based on the analysis;

assign the data to a logical data retention relevancy compartment of a plurality of logical data retention relevancy compartments in a data lakehouse;

determine whether the data retention relevancy value of the data is greater than a data retention threshold level of a data retention policy corresponding to the logical data retention relevancy compartment storing the data; and

transfer the data from a physical data storage unit corresponding to the logical data retention relevancy compartment of the plurality of logical data retention relevancy compartments in the data lakehouse to an archive in response to determining that the data retention relevancy value of the data is greater than the data retention threshold level of the data retention policy corresponding to the logical data retention relevancy compartment storing the data.

13. The computer program product of claim 12 , wherein the program instructions further cause the computer to:

determine whether a query was received to access the data transferred to the archive; and

restore the data to the physical data storage unit corresponding to the logical data retention relevancy compartment of the plurality of logical data retention relevancy compartments in the data lakehouse previously storing the data in response to determining that a query was received to access the data transferred to the archive.

14. The computer program product of claim 12 , wherein the program instructions further cause the computer to:

determine, using a machine learning model, physical data storage unit needs of the data lakehouse based on a data storage unit restore frequency of the data and an ingestion rate of the data; and

adjust a number of a plurality of physical data storage units in the data lakehouse based on the physical data storage unit needs of the data lakehouse.

15. The computer program product of claim 12 , wherein the program instructions further cause the computer to:

determine data retention relevancy of the data based on at least one of query frequency of the data, ingestion rate of the data, ingestion time of the data, source of the data, repetition of the data, and visibility of the data in each respective data storage unit of a plurality of data storage units in the data lakehouse; and

assign the data retention relevancy value to the data based on the data retention relevancy of the data.

16. The computer program product of claim 12 , wherein the program instructions further cause the computer to:

receive the data from a plurality of data sources in a landing zone of the data lakehouse corresponding to the computer; and

assign a default data retention period to the data received from the plurality of data sources.

17. The computer program product of claim 12 , wherein the logical data retention relevancy compartment has a data retention policy that corresponds to the data retention relevancy value of the data.