Granted Patent B2

US 12,248,490 · App. 17/076,164 · Granted Mar 11, 2025

Inventors: Krishnan Ramanathan (Bengaluru, IN); Aman Madaan (Pittsburgh, PA); Somashekhar Pammar (San Jose, CA)

Assignee: ORACLE INTERNATIONAL CORPORATION

G06F16/254G06F16/2282G06F16/24578G06F16/283

Abstract

Claims (71)

1. A system for use with an analytic applications environment, for ranking of database tables for use in controlling extract, transform, load (ETL) processes, comprising:

a computer including one or more processors, that includes an analytic applications environment operating theron that provides access to a data warehouse for storage of data by a plurality of tenants, wherein the analytic applications environment includes:

a first warehouse customer tenancy for a first tenant, that comprises a first database instance, a first staging area, and a first data warehouse instance of a plurality of data warehouses or data warehouse instances; and

a second customer tenancy for a second tenant, that comprises a second database instance, a second staging area, and a second data warehouse instance of the plurality of data warehouses or data warehouse instances;

wherein the analytic applications environment includes a data pipeline or other processing component that performs an extract, transform, load (ETL) process to extract data from an enterprise application or database environment, to be loaded into the data warehouse;

wherein the data stored within the data warehouse comprises customer data associated with the plurality of tenants, the customer data including a plurality of database tables and/or table columns, across different tenants of the plurality of tenants, associated with a set of data; and

wherein in association with the ETL process to extract the data from the enterprise application or database environment, to be loaded into the data warehouse, the computer operates as a table ranker that performs a ranking process to rank the database tables and/or table columns associated with the set of data across the plurality of tenants, including:

determining within each tenant's customer data one or more database tables and/or table columns indicative of importance to the tenant, including:

determining, for the first tenant, a first table and/or column ranking associated with a first customer data, for use by the ETL process in loading the first customer data in the first data warehouse instance; and

determining, for the second tenant, a second table and/or column ranking associated with a second customer data, for use by the ETL process in loading the second customer data in the second data warehouse instance;

which first and second table and/or column rankings are used by the analytic applications environment to prioritize processing by the ETL process to extract the tenant's first and second customer data to be loaded into the data warehouse.

2. The system of claim 1 , wherein the ranking process includes determining

a global rank;

a business rank; and

a tenant or customer-specific rank, for a plurality of tables and columns in a customer's database; and

aggregating or otherwise using the determined rankings to control the ETL process for a particular customer or tenant, to load their data into the data warehouse.

3. The system of claim 2 , wherein the aggregating comprises computing an aggregated ranking as a weighted combination of the global rank, the business rank, and the tenant or customer-specific rank.

4. The system of claim 2 , wherein the global rank is a customer- independent rank for each database table, and each column in the database table, and a relative importance of the database table or table column is derived for each database table or table column based on relationships with other important database tables or table columns and having the same table columns across different database tables.

5. The system of claim 2 , wherein the one or more processors prioritize database tables based on the global rank using a computed a frequency vectorization of keywords based on a simple count of keywords, or on a term frequency-inverse document frequency (tf-idf) vectorization, wherein the tf-idf vectorization is configured to account for commonly-occurring table names.

6. The system of claim 2 , wherein the tenant or customer-specific rank is computed based on assessing factors including a frequency of updates of the database table and/or table column or how often the database table and table column is used by one or more downstream applications.

7. The system of claim 1 , wherein the analytic applications environment is provided by a cloud computing environment.

8. The system of claim 1 , wherein each tenant of the analytic applications environment is associated with a data warehouse instance associated with a schema for use by the tenant.

9. The system of claim 1 , wherein the one or more processors prioritize table columns having a higher importance based on the ranking process, and check the integrity of the prioritized table columns having a higher importance before checking the integrity of other table columns.

10. The system of claim 1 , wherein the one or more processors prioritize generation of alerts in connection with one or more database tables having a higher importance based on the ranking process, wherein the alerts correspond to table data size errors.

11. The system of claim 1 , wherein the ranking process includes:

(i) determining a plurality of ranks including a global rank, a business rank, and a tenant or customer-specific rank, for the database tables and/or table columns associated with the set of data across the plurality of tenants; and

(ii) aggregating the determined rankings to control the ETL process for a particular customer or tenant, to load the associated data for the customer or tenant into the data warehouse, wherein aggregating the determined rankings comprises:

computing an aggregated ranking as a weighted combination of the global rank, the business rank, and the tenant or customer-specific rank, and

computing the importance of the one or more database tables and/or table columns comprises counting or otherwise determining a number of same table columns used across various different tables sourced by the plurality of tenants, to determine the table columns that are the more important columns in an enterprise-independent way.

12. The system of claim 1 ,

wherein each of the plurality of tenants, including a first tenant and a second tenant, are associated with a respective customer data, including:

a first customer data associated with the first tenant, and

a second customer data associated with the second tenant,

each of said first customer data and second customer data including a plurality of database tables and/or table columns of data;

wherein the system computes for the database tables and/or table columns in a respective customer data, a table/columns ranking, including:

for the first tenant, a first table/columns ranking of the first customer data associated with the first tenant, and

for the second tenant, a second table/columns ranking of the second customer data associated with the second tenant; and

wherein the system prioritizes ETL processing of the first customer data and the second customer data, for each of the first tenant and the second tenant, according to the rankings computed for the respective database tables and/or table columns.

13. A method for use with an analytic applications environment, for ranking of database tables for use in controlling extract, transform, load (ETL) processes, comprising:

providing, at a computer including one or more processors, an analytic applications environment operating theron that provides access to a data warehouse for storage of data by a plurality of tenants, wherein the analytic applications environment includes:

a first warehouse customer tenancy for a first tenant, that comprises a first database instance, a first staging area, and a first data warehouse instance of a plurality of data warehouses or data warehouse instances; and

a second customer tenancy for a second tenant, that comprises a second database instance, a second staging area, and a second data warehouse instance of the plurality of data warehouses or data warehouse instances;

performing, by the analytic applications environment, an extract, transform, load (ETL) process to extract data from an enterprise application or database environment, to be loaded into the data warehouse, wherein the data stored within the data warehouse comprises customer data associated with the plurality of tenants, the customer data including a plurality of database tables and/or table columns, across different tenants of the plurality of tenants, associated with a set of data; and

in association with the ETL process to extract the data from the enterprise application or database environment, to be loaded into the data warehouse, performing a ranking process to rank the database tables and/or table columns associated with the set of data across the plurality of tenants, including:

determining within each tenant's customer data one or more database tables and/or table columns indicative of importance to the tenant, including:

determining, for the first tenant, a first table and/or column ranking associated with a first customer data, for use by the ETL process in loading the first customer data in the first data warehouse instance; and

determining, for the second tenant, a second table and/or column ranking associated with a second customer data, for use by the ETL process in loading the second customer data in the second data warehouse instance;

which first and second table and/or column rankings are used by the analytic applications environment to prioritize processing by the ETL process to extract the first and second customer data to be loaded into the data warehouse.

14. The method of claim 13 , wherein the ranking process includes determining

a global rank;

a business rank; and

a tenant or customer-specific rank, for a plurality of tables and columns in a customer's database; and

aggregating or otherwise using the determined rankings to control the ETL process for a particular customer or tenant, to load their data into the data warehouse.

15. The method of claim 13 , wherein the analytic applications environment is provided by a cloud computing environment.

16. The method of claim 13 , wherein each tenant of the analytic applications environment is associated with a data warehouse instance associated with a schema for use by the tenant.

17. A non-transitory computer readable storage medium having instructions thereon, which when read and executed by a computer including one or more processors cause the computer to perform a method comprising:

providing, at a computer including one or more processors, an analytic applications environment operating theron that provides access to a data warehouse for storage of data by a plurality of tenants, including wherein

a first warehouse customer tenancy for a first tenant comprises a first database instance, a first staging area, and a first data warehouse instance of a plurality of data warehouses or data warehouse instances;

a second customer tenancy for a second tenant can comprise a second database instance, a second staging area, and a second data warehouse instance of the plurality of data warehouses or data warehouse instances;

performing, by the analytic applications environment, an extract, transform, load (ETL) process to extract data from an enterprise application or database environment, to be loaded into the data warehouse, wherein the data stored within the data warehouse comprises customer data associated with the plurality of tenants, the customer data including a plurality of database tables and/or table columns, across different tenants, including:

determining within each tenant's customer data one or more database tables and/or table columns indicative of importance to the tenant, including:

determining, for the first tenant, a first table and/or column ranking associated with a first customer data, for use by the ETL process in loading the first customer data in the first data warehouse instance; and

determining, for the second tenant, a second table and/or column ranking associated with a second customer data, for use by the ETL process in loading the second customer data in the second data warehouse instance;

which first and second table and/or column rankings are used by the analytic applications environment to prioritize processing by the ETL process to extract the tenant's first and second customer data to be loaded into the data warehouse.

18. The non-transitory computer readable storage medium of claim 17 , wherein the ranking process includes determining

a global rank;

a business rank; and

a tenant or customer-specific rank, for a plurality of tables and columns in a customer's database; and

aggregating or otherwise using the determined rankings to control the ETL process for a particular customer or tenant, to load their data into the data warehouse.

19. The non-transitory computer readable storage medium of claim 17 , wherein the analytic applications environment is provided by a cloud computing environment.

20. The non-transitory computer readable storage medium of claim 17 , wherein each tenant of the analytic applications environment is associated with a data warehouse instance associated with a schema for use by the tenant.

Assignments (1)

Priority Claims (4)

Continuity (2)

References Cited (191)