DESC:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION

(See Section 10 and Rule 13)

Title of invention:
A SYSTEM FOR GEOSPATIAL DATA ANALYTICS AND A METHOD THEREOF

APPLICANT:

AARAV UNMANNED SYSTEMS PRIVATE LIMITED

An Indian entity having address as:
#3, 80 Feet Main Road, MCHS Layout, Jakkur, Bangalore – 560064

The following specification describes the invention and the manner in which it is to be performed.
CROSS-REFERENCE TO RELATED APPLICATIONS
The present application claims priority from the Indian patent application, having application number 202341023025, filed on 29th March 2023, incorporated herein by a reference.
TECHNICAL FIELD
The present disclosure relates to the field of data analytics. More particularly, the present disclosure relates to an integrated system and a method for enabling data analytics platform. More specifically, the present specification discloses the integrated system for ingesting, processing, hosting and delivering the digital geospatial data, that may be used in the context of drones and other unmanned aerial vehicle applications.
BACKGROUND
The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
Structural and geospatial data are valuable in areas such as, for example but not limited to, real estate transaction, planning, or insurance. Non-limiting examples of the structural and geospatial data include the following: the area of real property including land and/or buildings; the square footage of a building; the roof size and/or type; the presence of a pool and its size and/or location; and the presence of trees and its type, size, and/or location. Traditionally, structural and geospatial information can be obtained by (1) manually checking real estate records from relevant agencies; or (2) manually surveying the underlying real properties. These traditional methods suffer from a number of drawbacks and deficiencies such as outdated records, missing records, or destroyed records, labor intensive manual checking and surveying and costly processes.
In recent developments, structural and geospatial data are being obtained by using imaging techniques. Generally, the imaging is being captured through drones or through remote satellites. The market for image analysis has also historically been limited by the high cost of obtaining images to analyze. Starting in the 1970s with the Landsat satellite, this began to change as low-resolution satellite images became publicly available. A series of new satellites has opened up progressively more applications as the resolution, spectral coverage, geographic coverage, and cost per image have all continuously improved; accordingly, a significant market in commercial remote sensing imagery has emerged. But even this market has been limited from achieving its full potential because of the still-present requirement for expensive, scarce image analysis talent. Some progress has been made in automated image analysis technologies, but for a vast range of current and potential applications, large scale image analysis (such as would be needed when analyzing satellite images of a large region) remains too expensive and too supply-constrained to use.
Currently, geospatial data at scale are generally being obtained from sources such as satellite imagery. Further, aerial images at scale are obtained from drones or aircraft. The aerial and satellite imagery generates an ortho-mosaic and digital surface model, which is raster data. Further, vector data is generated through automated and manual digitization on the raster data. Further, terrain data are generally obtained through fields surveying through Global Positioning System (GPS) receivers. Conventionally, an individual system has been used for handling raster, terrain and vector geospatial or aerial data. These conventional systems are facing various limitations with respect to integration of data, format incompatibility, low scalability, low throughput of data processing, isolated data visualization for different types of data.

As is known in conventional art, there may be technologies available which provide web-based integration of agriculture geographic data with the agriculture equipment. Further, there may be technologies cloud computing based thermal anomaly-based ignition event detection from a set of satellite imagery data. But none of the conventional arts are able to provide an integrated platform for handling geospatial or aerial data.
Thus, there is this long-standing need for an integrated system for ingesting, processing, hosting and delivering digital geospatial data, which can scale to an extent based on the demand along with high throughput and optimum data visualization.
SUMMARY
The present disclosure overcomes one or more shortcomings of the prior art and provides additional advantages discussed throughout the present disclosure. Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.
The present disclosure has been made in order to solve the problems, and it is an object of the present disclosure to provide an integrated system for enabling data analytics by ingesting, processing, hosting and delivering the digital geospatial data.
In one implementation, an integrated system for enabling data analytics is disclosed. Further, the integrated system may comprise a cloud computing infrastructure (CCI). Further, the CCI may comprise one or more Application Programming Interface (API) load balancing services, one or more User Interface (UI) balancers, one or more container orchestration services, one or more container registry services, one or more storage services, one or more online data transfer services, one or more batch processing services, one or more event bus services, one or more delivery services, and one or more rendering services. Furthermore, one or more processors of the CCI are coupled with a memory and may execute instructions for receiving an input data from one or more data sources, wherein the input data comprises geospatial data, aerial data, raster data, vector data, terrain data and a combination thereof. Further, the integrated system may process the input data using one or more services for transforming the input data into data suitable format for hosting and delivery over the CCI. Further, the integrated system may store processed data on one or more storage services. Further, the integrated system may deliver the stored data to the one or more delivery services, and further, the one or more delivery services may be configured to deliver the stored data to a web application module. Furthermore, the web application module may be configured to provide a web user interface to access data. Further, the integrated system may render the stored data using one or more rendering services. The one or more rendering services may be configured to display or visualize the stored data to a client device.
In another implementation, a method for enabling data analytics is disclosed. The method may further comprise a step for receiving, via one or more processors of a cloud computing infrastructure (CCI), an input data from one or more data sources. Further, the input data may comprise geospatial data, aerial data, raster data, vector data, terrain data and a combination thereof. The method may further comprise a step for processing, via one or more processors, the input data using one or more services for transforming the input data into data suitable format for hosting and delivery over the CCI. The method may comprise a step for storing, via one or more processors, processed data on the one or more storage services. The method may further comprise a step for delivering, via one or more processors, a stored data to the one or more delivery services, wherein the one or more delivery services configured to deliver the stored data to a web application module, wherein the web application module is configured to provide a web user interface to access data. The method may further comprise a step for rendering, via one or more processors, the stored data using the one or more rendering services, wherein the one or more rendering services is configured to display or visualize the stored data to a client device.
BRIEF DESCRIPTION OF THE DRAWINGS
The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer like features and components.
Fig. 1 illustrates a network implementation of an integrated system (100) for enabling data analytics, in accordance with an embodiment of the present disclosure.
Fig. 2 illustrates a diagram (200) describing an exemplary embodiment of working of the integrated system (100) for enabling data analytics, in accordance with an embodiment of the present disclosure present disclosure.
Fig. 3 illustrates a method (300) for enabling data analytics, in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION
Reference throughout the specification to “various embodiments,” “some embodiments,” “one embodiment,” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases “in various embodiments,” “in some embodiments,” “in one embodiment,” or “in an embodiment” in places throughout the specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments.
An integrated system(s) and method(s) for geospatial data analytics is described.
Referring to Fig. 1, a network implementation of an integrated system (100) for enabling data analytics is illustrated, in accordance with an embodiment of the present subject matter. In one embodiment, the integrated system (100) may be configured to be hosted on a cloud computing infrastructure (CCI). In one embodiment, the integrated system (100) may be hosted on the cloud computing infrastructure to ingest, process, host and deliver digital geospatial data. Further, the CCI may comprise one or more Application Programming Interface (API) load balancing services (101), one or more User Interface (UI) balancers (102), one or more container orchestration services, one or more container registry services, one or more batch processing services (104), one or more storage services (105), one or more online data transfer service, one or more event bus service (106), one or more delivery services, and one or more rendering services. In one embodiment, one or more processors of the CCI may be coupled with a memory to execute instructions for receiving an input data from one or more data sources (201). The one or more data sources (201) may comprise drones, Unmanned Aerial Vehicles (UAVs), Remote satellites, remote sensing devices, aircrafts.
Further, the input data may comprise geospatial data, aerial data, raster data, vector data, terrain data and a combination of the same. Further, the integrated system (100) may process the input data using one or more services for transforming the input data into data suitable format for hosting and delivery over the CCI. Furthermore, the integrated system (100) may store processed data on one or more storage services (105). Furthermore, the integrated system (100) may deliver stored data to one or more delivery services. Moreover, one or more delivery services may be configured to deliver the stored data to a web application module. Further, the web application module is configured to provide a web user interface to access data. Moreover, the integrated system (100) may render the stored data using one or more rendering services. The one or more rendering services may be configured to display or visualize the stored data to a client device (202).
In an exemplary embodiment, the cloud computing infrastructure may be a service provided by, well-known cloud service providers include, by way of example and not limited in any way to many others, Amazon Web Services (AWS) by Amazon Inc., Google Cloud Platform or GCE/GCS, by Google, Inc., Microsoft cloud or Azure, by Microsoft Inc., OpenStack, VMware, IBM etc. In another embodiment, the cloud computing infrastructure may comprise at least one of, a virtual private cloud (VPC), a public cloud, a hybrid cloud and a combination thereof, wherein the virtual private cloud may be configured to create a logically isolated network to work on.
In another embodiment, the integrated system (100) may comprise a web application module and a backend module. The web application module may be configured to provide a web user interface for accessing data, wherein the web user interface may comprise a web browsing application. Further, the web user interface may include a mobile application accessible through a portable user device. The web user interface may enable a client to display the data through using the client device (202). Further, the web user interface may enable a user to access the data on the web browsing application, wherein the web browsing application may be accessible over a communication network.
In an exemplary embodiment, the communication network may be a wireless network, a wired network or a combination thereof. The communication network may comprising any one of the following: a cable network, a wireless network, a telephone network (e.g., Analog, Digital, POTS, PSTN, ISDN, xDSL), a cellular communication network, a mobile telephone network (e.g., CDMA, GSM, NDAC, TDMA, E-TDMA, NAMPS, WCDMA, CDMA-2000, UMTS, 3G, 4G, 5G, 6G), a radio network, a television network, an Internet, an intranet, a local area network (LAN), a wide area network (WAN), an electronic positioning network, an X.25 network, an optical network (e.g., PON), a satellite network (e.g., VSAT), a packet-switched network, a circuit-switched network, a public network, a private network, and/or other wired or wireless communications network configured to carry data. The aforementioned network 104 may support wireless local area network (WLAN) and/or wireless metropolitan area network (WMAN) data communications functionality in accordance with Institute of Electrical and Electronics Engineers (IEEE) standards, protocols, and variants such as IEEE 802.11 (“WiFi”), IEEE 802.16 (“WiMAX”), IEEE 802.20x (“Mobile-Fi”), and others.
In yet another embodiment, the backend module may be configured to store, serve and analyze the data. Further, the backend module may enable the web application module to display the data on the web user interface. Further, the backend module may be configured to use one or more RDS services.
In yet another embodiment, the integrated system (100) may be configured to be hosted on the CCI using one or more container orchestration services. Further the one or more container orchestration service may be configured to deploy, manage, and scale containerized applications. Further, the one or more container orchestration services may include clusters, a service and tasks, wherein clusters may be a logical group of services. Further, the service may be configured to provide scaling, maintaining and monitoring of one or more tasks. Furthermore, the one or more tasks may correspond to virtual resources including at least one or more virtual CPUs (vCPUs), virtual memory/storage or a combination thereof. Moreover, the one or more container orchestration services may be configured to deploy, manage, and scale containerized applications. Additionally, the one or more services of the integrated system (100) may be configured to execute on one or more tasks.
In yet another embodiment, the one or more container orchestration services may be configured to offer high-performance hosting, to reliably deploy application images and artifacts anywhere. Further, the combination of the one or more tasks or one or more virtual resources may be configured to execute on an isolated container created using an image stored on the one or more container registry services.
In yet another embodiment, the one or more tasks may be connected to one or more API load balancing services (101). Further, the one or more API load balancing services (101) may be configured to automatically distribute incoming application traffic across multiple targets and virtual appliances in one or more Availability Zones (AZs). Further, the one or more API load balancing services (101) may be configured to serve the web application module and the backend module of the integrated system (100). In an exemplary, the one or more API load balancing services (101) may receive a request from a client device (202), which may be forwarded to the one or more container orchestration service task which in turn may send the requested data back to the one or more API load balancing services (101), which may be responded back to the client device (202).
In yet another embodiment, the integrated system (100), hosted on the CCI, may be configured to upload the data on the one or more storage services. Furthermore, the one or more storage services (105) may include at least one of, but not limiting to, one or more relational database services (RDS) (105a), one or more file storage services (105b), one or more object storage services (105c) and a combination thereof. Furthermore, the one or more tasks may comprise the one or more RDS services (105a) for storing the data. Further, the one or more RDS services (105a) may be configured to set up, operate, and scale databases. In an exemplary embodiment, the one or more RDS services (105a) may comprise a server instance on which an application related to the Database Management System (DBMS) runs. In yet another exemplary embodiment, the backend module may be configured to use the one or more RDS service, wherein the one or more RDS services (105a) may execute PostgreSQL DBMS application. Moreover, the one or more tasks may be connected to multiple databases for storing the data.
In yet another embodiment, the one or more tasks may be connected to the one or more file storage service (105b), wherein the one or more file storage service (105b) may enable the sharing of the files among the one or more tasks for storing files. Further, the one or more file storage service (105b) may have a capability of automatically growing and shrinking as files are added or removed, with no need for management or provisioning.
In yet another embodiment, the one or more tasks may be connected to the one or more object storage service (105c) for storing data objects. Further, the one or more object storage services (105c) may be configured to offer industry-leading scalability, data availability, security, and performance storage, retrieval or accessing the data objects. Moreover, the one or more tasks may use the one or more object storage services (105c) for storage, retrieval or accessing the data objects.
In yet another embodiment, the one or more tasks may be connected to one or more online data transfer services for migration of data from the one or more file storage service (105b) to the one or more object storage service (105c), and vice versa. Further, the one or more online data transfer service may be configured to automate and accelerate moving data between on premises and one or more storage services (105). In another embodiment, the one or more online data transfer service may be configured to synchronize the data between the one or more file storage service (105b) and the one or more object storage service (105c).
In yet another embodiment, the one or more tasks may be connected to the one or more batch processing services (104) for processing or executing asynchronous compute intensive workload. Further, the one or more batch processing services (104) may efficiently run hundreds of thousands of batch and ML computing jobs while optimizing compute resources. Further, the one or more batch processing services (104) may execute the asynchronous compute intensive workloads as a job, wherein the job may be configured to execute in batches by the one or more batch processing services (104). Furthermore, the one or more file storage services (105b) and the one or more object storage services (105c) may be connected to the jobs of the one or more batch processing services (104). In one embodiment, the one or more file storage services (105b) and the one or more object storage services (105c) may be connected to the jobs for storing output of the jobs. In a related embodiment, the one or more file storage services (105b) and the one or more object storage services (105c) may be connected to the jobs for providing input data to the jobs. Furthermore, the output of the jobs may be shared with a server instance related to web application, executed by the one or more services hosted on the CCI.
In yet another embodiment, the one or more tasks may be connected to the one or more event bus service (106) for capturing state change events of the jobs. Further, the one or more event bus services (106) may be configured as a serverless event bus that can receive, filter, transform, route, and deliver events. Moreover, the one or more event bus services (106) may be configured to send notifications.
In yet another embodiment, the one or more delivery services may be configured to deliver the stored data to the web user interface, via the communication network. In a related embodiment, the one or more rendering services may be configured to display or visualize the stored data to the client device (202), via the web browsing application.
In an exemplary embodiment, one or more services may comprise containerization services, hosted on the CCI. Further, the processing of data may correspond to transforming the data into a data format suitable for hosting and delivery over the CCI. Further, the integrated system (100), hosted on the CCI may be configured to store the data on the one or more storage services, hosted on the cloud platform. Furthermore, the integrated system (100) may be configured to deliver the data to the one or more delivery services, hosted on the cloud platform. Further, the one or more delivery services may be configured to deliver the data to the web user interface using the Internet. Moreover, the integrated system (100) may be configured to render the data using the one or more rendering services, hosted on the cloud platform. Further, the one or more rendering services may be configured to display or visualize the data to the client device through a web browser.
Now referring to Fig. 2, a diagram (200) describing an exemplary embodiment of working of the integrated system (100) for enabling data analytics is illustrated, in accordance with an embodiment of the present disclosure present disclosure. The integrated system (100) may receive the input data from one or more data sources (201). Further, the input data received from the one or more data sources (201) may configured to be hosted on a cloud computing infrastructure (CCI) to ingest, process, host and deliver digital geospatial data. Further, the one or more rendering services may be configured to display or visualize the stored data to the client device (202), via the web browsing application.
In one embodiment, the received data from one or more data sources (201) may be stored on a Google Cloud Service (GCS) (201a) which may further be connected to a processing workstation (201b). This received data from one or more data sources (201) further may be processed through the one or more services of the integrated system (100), hosted on CCI.
Now referring to Fig. 3, a method (300) for a data analytics platform is illustrated, in accordance with an embodiment of the present disclosure. The method (300) may involve a variety of steps for enabling the data analytics platform.
The method (300) may involve a step (301) for receiving, via one or more processors of a cloud computing infrastructure (CCI), the input data from one or more data sources. In an exemplary embodiment, the one or more data sources (201) may include drones, Unmanned Aerial Vehicles (UAVs), Remote satellites, remote sensing devices, aircraft. Further, the input data received from the one or more data sources (201) may include geospatial data, aerial data, raster data, vector data, terrain data and a combination of the same.
Further, the method (300) may involve a step (302) for processing, via one or more processors, the input data using one or more services for transforming the input data into data suitable format for hosting and delivery over the CCI.
Furthermore, the method (300) may involve a step (303) for storing, via one or more processors, the processed data on the one or more storage services (105).
Moreover, the method (300) may involve a step (304) for delivering, via one or more processors, a stored data to the one or more delivery services, wherein the one or more delivery services configured to deliver the stored data to a web application module, wherein the web application module is configured to provide a web user interface to access data.
Additionally, the method (300) may involve a step (305) for rendering, via one or more processors, the stored data using the one or more rendering services, wherein the one or more rendering services is configured to display or visualize the stored data to a client device (202).

The presently disclosed integrated system for ingesting, processing, hosting and delivering the digital geospatial data, may have the following advantageous functionalities over the conventional art:
• Providing an integrated system for handling raster, terrain and vector data, rather than individual handling.
• Providing a scalable mechanism to process geospatial data.
• Enabling delivery of high throughput of processed data.
• Enable easy ingestion of large amount of input data.
• Resolve limitations of various data format incompatibility while processing data obtained from different sources.
• Providing an integrated user interface for visualizing different types of data.
• Enabling easy backup of large amounts of data.

Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.

The foregoing description shall be interpreted as illustrative and not in any limiting sense. A person of ordinary skill in the art would understand that certain modifications could come within the scope of this disclosure.

The embodiments, examples and alternatives of the preceding paragraphs or the description and drawings, including any of their various aspects or respective individual features, may be taken independently or in any combination. Features described in connection with one embodiment are applicable to all embodiments unless such features are incompatible.

,CLAIMS:WE CLAIM:

1. An integrated system (100), for enabling data analytics, comprising:
a cloud computing infrastructure (CCI) comprises one or more Application Programming Interface (API) load balancing services (101), one or more User Interface (UI) balancers (102), one or more container orchestration services, one or more container registry services, one or more batch processing services (104), one or more storage services (105), one or more online data transfer services, one or more event bus services (106), one or more delivery services, and one or more rendering services,
wherein one or more processors of the CCI are coupled with a memory and execute instruction for:
receiving an input data from one or more data sources, wherein the input data comprises geospatial data, aerial data, raster data, vector data, terrain data and a combination thereof;
processing the input data using one or more services for transforming the input data into data suitable format for hosting and delivery over the CCI;
storing a processed data on the one or more storage services (105);
delivering stored data to the one or more delivery services, wherein the one or more delivery services configured to deliver the stored data to a web application module, wherein the web application module is configured to provide a web user interface to access data; and
rendering the stored data using the one or more rendering services, wherein the one or more rendering services is configured to display or visualize the stored data to a client device (202).

2. The integrated system (100) as claimed in claim 1, wherein the one or more data sources (201) comprises drones, Unmanned Aerial Vehicles (UAVs), Remote satellites, remote sensing devices, aircrafts.

3. The integrated system (100) as claimed in claim 1, wherein the CCI comprises at least one of, a virtual private cloud (VPC), a public cloud, a hybrid cloud and a combination thereof, wherein the virtual private cloud is configured to create a logically isolated network.

4. The integrated system (100) as claimed in claim 1, wherein the web user interface comprises a web browsing application, wherein the web browsing application is a mobile application accessible through a portable client device.

5. The integrated system (100) as claimed in claim 1, wherein a backend module enables the web application module to display the data on the web user interface, wherein the backend module is configured to store, serve, and analyze the input data.

6. The integrated system (100) as claimed in claim 1, wherein the one or more storage services (105) comprises at least one of one or more relational database service (RDS) (105a), one or more file storage service (105b), one or more object storage service (105c) and a combination thereof.

7. The integrated system (100) as claimed in claim 1, wherein the one or more container orchestration service comprises clusters, service and tasks, wherein clusters is a logical group of services, wherein the service is configured to provide scaling, maintaining and monitoring of one or more tasks, wherein the one or more tasks corresponds to virtual resources including at least one or more virtual CPUs (vCPUs), virtual memory/storage or a combination thereof, wherein the one or more container orchestration service is configured to deploy, manage, and scale containerized applications..

8. The integrated system (100) as claimed in claim 7, wherein the combination of the one or more tasks or one or more virtual resources is configured to execute on an isolated container created using an image stored on the one or more container registry service, wherein the one or more container registry service is configured to hosting and deploying of application images and artifacts anywhere.

9. The integrated system (100) as claimed in claim 7, wherein the one or more tasks is connected to the one or more API load balancing services (101), wherein one or more API load balancing services (101) is configured to automatically distribute incoming application traffic across multiple targets and virtual appliances in one or more Availability Zones (AZs).

10. The integrated system (100) as claimed in claim 1, wherein the one or more API load balancing services (101) is configured to serve the web application module and the backend module.

11. The integrated system (100) as claimed in claim 1, wherein the one or more API load balancing services (101) receives a request from the client device, which is forwarded to the one or more container orchestration service task, wherein one or more container orchestration service task sends the requested data back to the one or more API load balancing services (101), which is further responded back to the client device (202).

12. The integrated system (100) as claimed in claim 7, wherein the one or more tasks comprises the one or more RDS service (105a) for storing the data, wherein the one or more RDS service (105a) is configured to set up, operate, and scale databases.

13. The integrated system (100) as claimed in claim 1, wherein the backend module is configured to use the one or more RDS service (105a), wherein the one or more RDS service execute PostgreSQL DBMS application.

14. The integrated system (100) as claimed in claim 7, wherein the one or more tasks is connected to the one or more file storage services (105b), wherein the one or more file storage services (105b) enables the sharing of the files among the one or more tasks for storing files.

15. The integrated system (100) as claimed in claim 6, wherein the one or more tasks is connected to the one or more object storage services (105c) for storage, retrieval or accessing the data objects.

16. The integrated system (100) as claimed in claim 7, wherein the one or more tasks is connected to the one or more online data transfer service for migration of data from the one or more file storage services (105b) to the one or more object storage services (105c), and vice versa.

17. The integrated system (100) as claimed in claim 7, wherein the one or more tasks is connected to the one or more batch processing services (104) for processing or executing asynchronous compute intensive workload.

18. The integrated system (100) as claimed in claim 17, wherein the one or more batch processing services (104) execute an asynchronous compute intensive workloads as a job, wherein the job is configured to execute in batches by the one or more batch processing services (104).

19. The integrated system (100) as claimed in claim 6 and 18, wherein the one or more file storage services (105b) and the one or more object storage services (105c) is connected to the job of the one or more batch processing services (104) for providing the input data to the jobs and storing output of the jobs.

20. The integrated system (100) as claimed in claim 1 and 7, wherein the one or more tasks is connected to the one or more event bus service (106) for capturing state change events of the jobs, wherein the one or more event bus service (106) is configured as a serverless event bus that can receive, filter, transform, route, and deliver events as well as send notifications.

21. The integrated system (100) as claimed in claim 1, wherein the one or more delivery services is configured to deliver the stored data to the web user interface, via a communication network.

22. The integrated system as claimed in claim 1, wherein the one or more rendering services is configured to display or visualize the stored data to the client device (202), via the web browsing application.

23. A method for enabling data analytics, comprising:
receiving, via one or more processors of a cloud computing infrastructure (CCI), an input data from one or more data sources, wherein the input data comprises geospatial data, aerial data, raster data, vector data, terrain data and a combination thereof;
processing, via one or more processors, the input data using one or more services for transforming the input data into data suitable format for hosting and delivery over the CCI;
storing, via one or more processors, a processed data on the one or more storage services;
delivering, via one or more processors, a stored data to the one or more delivery services, wherein the one or more delivery services configured to deliver the stored data to a web application module, wherein the web application module is configured to provide a web user interface to access data; and
rendering, via one or more processors, the stored data using the one or more rendering services, wherein the one or more rendering services is configured to display or visualize the stored data to a client device (202).

Dated this 29th Day of March 2023

Deepak Pawar
Agent for the Applicant
IN/PA-2052