US-20260126982-A1 - SEAMLESS CONTENT TRANSPORT ACROSS DATA REPOSITORIES IN ANALYTIC NETWORKS

US20260126982A1US 20260126982 A1US20260126982 A1US 20260126982A1US-20260126982-A1

Abstract

In an example embodiment, a solution is provided that splits data storage for Analytical Content Network (ACN) packages between an Analytics Cloud platform and another analytics platform, such as SAP Datasphere (DSP), from SAP SE of Walldorf, Germany. More particularly, object metadata is stored in SAC while object data itself is stored in DSP. This allows for the planning tools of SAC to be utilized efficiently based on locally stored object metadata, while extending the data limits so that large amounts of object data, stored in DSP, can be shared among tenants.

Inventors

Sahana Durgam Udaya

Assignees

SAP SE

Dates

Publication Date: 20260507
Application Date: 20241107

Claims (20)

1 . A system comprising: at least one hardware processor; and a computer-readable medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising: receiving, at a first analytics application, a request to store an analytics package, the analytics package comprising package metadata and a plurality of analytics objects, each analytics object comprising object data and object metadata; storing, by the first analytics application, the package metadata and the object metadata of each analytics object in a first object store of the first analytics application, the first object store assigned to a fixed size space in memory of a first database; storing, by the first analytics application, the object data of each analytics object, in a second object data store of a second analytics application, the second object store assigned to a dynamically sized space in memory of a second database; receiving a request from a first tenant of the first analytics application to share the analytics package with a second tenant of the first analytics application; and importing the analytics package to the second tenant by retrieving the object data of each analytics object from the second data store and retrieving the package metadata and the object metadata of each analytics object from the first data store.
2 . The system of claim 1 , wherein the package metadata comprises dependency information among the plurality of analytics objects.
3 . The system of claim 2 , wherein the package metadata further comprises an overview summarizing the object data.
4 . The system of claim 1 , wherein the object metadata comprises an indication of a type of a corresponding analytics object.
5 . The system of claim 4 , wherein the type is a story.
6 . The system of claim 4 , wherein the type is a data model.
7 . The system of claim 4 , wherein the type is a dimension.
8 . A method comprising: receiving, at a first analytics application, a request to store an analytics package, the analytics package comprising package metadata and a plurality of analytics objects, each analytics object comprising object data and object metadata; storing, by the first analytics application, the package metadata and the object metadata of each analytics object in a first object store of the first analytics application, the first object store assigned to a fixed size space in memory of a first database; storing, by the first analytics application, the object data of each analytics object, in a second object data store of a second analytics application, the second object store assigned to a dynamically sized space in memory of a second database; receiving a request from a first tenant of the first analytics application to share the analytics package with a second tenant of the first analytics application; and importing the analytics package to the second tenant by retrieving the object data of each analytics object from the second data store and retrieving the package metadata and the object metadata of each analytics object from the first data store.
9 . The method of claim 8 , wherein the package metadata comprises dependency information among the plurality of analytics objects.
10 . The method of claim 9 , wherein the package metadata further comprises an overview summarizing the object data.
11 . The method of claim 8 , wherein the object metadata comprises an indication of a type of a corresponding analytics object.
12 . The method of claim 11 , wherein the type is a story.
13 . The method of claim 11 , wherein the type is a data model.
14 . The method of claim 11 , wherein the type is a dimension.
15 . A non-transitory machine-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving, at a first analytics application, a request to store an analytics package, the analytics package comprising package metadata and a plurality of analytics objects, each analytics object comprising object data and object metadata; storing, by the first analytics application, the package metadata and the object metadata of each analytics object in a first object store of the first analytics application, the first object store assigned to a fixed size space in memory of a first database; storing, by the first analytics application, the object data of each analytics object, in a second object data store of a second analytics application, the second object store assigned to a dynamically sized space in memory of a second database; receiving a request from a first tenant of the first analytics application to share the analytics package with a second tenant of the first analytics application; and importing the analytics package to the second tenant by retrieving the object data of each analytics object from the second data store and retrieving the package metadata and the object metadata of each analytics object from the first data store.
16 . The non-transitory machine-readable medium of claim 15 , wherein the package metadata comprises dependency information among the plurality of analytics objects.
17 . The non-transitory machine-readable medium of claim 16 , wherein the package metadata further comprises an overview summarizing the object data.
18 . The non-transitory machine-readable medium of claim 15 , wherein the object metadata comprises an indication of a type of a corresponding analytics object.
19 . The non-transitory machine-readable medium of claim 18 , wherein the type is a story.
20 . The non-transitory machine-readable medium of claim 18 , wherein the type is a data model.

Description

TECHNICAL FIELD This document generally relates to computer analytics software. More specifically, this document relates to seamless content transport across data repositories in analytic networks. BACKGROUND Analytics software allows individuals and entities such as businesses to obtain various analytics content, such as summaries, predictions, models, stories, visualizations, and value-driver trees (VDTs), typically regarding the functioning of an organization. An example of analytics software is the SAP Analytics Cloud™ (SAC), from SAP SE of Walldorf, Germany, which combines business intelligence, planning, and predictive capabilities. BRIEF DESCRIPTION OF DRAWINGS The present disclosure is illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements. FIG. 1 is a diagram illustrating an architecture for Analytical Content Network (ACN) in accordance with an example embodiment. FIG. 2 is a diagram illustrating an example of a package in accordance with an example embodiment. FIG. 3 is a block diagram illustrating a system, in accordance with an example embodiment. FIG. 4 is a sequence diagram illustrating a method, for performing a package import process, in accordance with an example embodiment. FIG. 5 is a sequence diagram illustrating a method, for performing a package creation process, in accordance with an example embodiment. FIG. 6 is a flow diagram illustrating a method, in accordance with an example embodiment. FIG. 7 is a block diagram illustrating an architecture of software, which can be installed on any one or more of the devices described above. FIG. 8 illustrates a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, according to an example embodiment. DETAILED DESCRIPTION The description that follows discusses illustrative systems, methods, techniques, instruction sequences, and computing machine program products. In the following description, for purposes of explanation, numerous specific details are set forth to provide an understanding of various example embodiments of the present subject matter. It will be evident, however, to those skilled in the art, that various example embodiments of the present subject matter may be practiced without these specific details. In any business intelligence/analytics platform, the analytics content plays a central role in discovering the unseen patterns in an organization. Hence, sharing of the analytics content across users is very helpful for better collaboration. Additionally, standard content templates can be reused by different users, who may then have those templates applied to their own data. The infrastructure for sharing analytics content is an Analytical Content Network (ACN). Shared SAC content is called a “package.” ACN allows for the sharing of analytics content across tenants, which leads to better collaboration. For example, analytics content can be created by a development system tenant and then shared with a quality assurance tenant for testing, before being distributed to customer tenants for use. An enterprise can use multiple systems for storing and processing data. For example, an enterprise can use a system that stores data in a database system and provides metadata that defines how the data is stored and how the data is accessed. Analytics systems have been introduced that provide advanced analytics capabilities and improved data processing performance as compared to that provided by other systems, such as a system within which the enterprise stores and maintains its data. Such analytics systems can include cloud-based analytics systems that include an analytics engine that is executed directly within the underlying database system. Such an analytics engine can be referred to as a database (DB) analytics engine (DB-based analytics engine). By way of non-limiting example, an example cloud-based analytics system includes SAP Analytics Cloud (SAC) provided by SAP SE of Walldorf, Germany. SAC can be described as an all-in-one platform for business intelligence, planning, and predictive analytics to support enterprise operations. In some examples, SAP SAC uses multi-dimensional services (MDS), which provide a DB-based analytics engine. SAP SAC provides requests to the MDS in a particular protocol (e.g., information access (InA) protocol), which enables more complex data analytics requests to be formulated and executed (e.g., as compared to data analytics requests submitted through the DW). A user may operate a graphical user interface of an Analytics Cloud to create one or more models. A model is a representation of the data of an organization or segment. One type of model a user can create is an analytic model, which is used to analyze data, such as by looking for trends and anomalies. Data mo