Twilio inc. (20240330298). GROUPING CONTACTS USING TIERED WAREHOUSE LEVELS simplified abstract
Contents
- 1 GROUPING CONTACTS USING TIERED WAREHOUSE LEVELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GROUPING CONTACTS USING TIERED WAREHOUSE LEVELS - A simplified explanation of the abstract
- 1.4 Potential Applications
- 1.5 Problems Solved
- 1.6 Benefits
- 1.7 Commercial Applications
- 1.8 Prior Art
- 1.9 Frequently Updated Research
- 1.10 Questions about Contact Data Processing Technology
- 1.11 Original Abstract Submitted
GROUPING CONTACTS USING TIERED WAREHOUSE LEVELS
Organization Name
Inventor(s)
Anurag Sethi of San Carlos CA (US)
GROUPING CONTACTS USING TIERED WAREHOUSE LEVELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240330298 titled 'GROUPING CONTACTS USING TIERED WAREHOUSE LEVELS
The patent application describes techniques for deploying multiple data processing pipelines to execute contact grouping queries in a cloud-based virtual data warehouse. Each query is assigned to a pipeline based on characteristics and the count of contact records associated with the entity. The runtime for each query is obtained to calculate an average execution time and determine if additional pipelines are needed to meet service level objectives.
- Data processing pipelines for contact grouping queries in a virtual data warehouse
- Assignment of queries to pipelines based on characteristics and record count
- Calculation of average execution time for queries
- Determination of additional pipelines needed to meet service level objectives
- Optimization of data processing for efficient contact data upload
Potential Applications
The technology can be applied in various industries where contact data management is crucial, such as customer relationship management, marketing, and sales.
Problems Solved
This technology addresses the challenge of efficiently processing and uploading contact data into contact group tables in a cloud-based environment, ensuring optimal performance and meeting service level objectives.
Benefits
- Improved efficiency in processing contact data - Enhanced performance in uploading contact records - Better resource allocation based on query characteristics - Meeting service level objectives for contact grouping queries
Commercial Applications
Title: Efficient Contact Data Processing Technology for Cloud-Based Data Warehouses This technology can be utilized by companies offering CRM solutions, marketing automation platforms, and data analytics services to enhance their contact data management capabilities and improve overall performance.
Prior Art
Readers can explore prior research on data processing pipelines, cloud-based data warehouses, and contact data management systems to understand the evolution of this technology.
Frequently Updated Research
Stay informed about the latest advancements in data processing techniques, cloud computing technologies, and contact data management strategies to enhance the efficiency and effectiveness of this technology.
Questions about Contact Data Processing Technology
1. How does this technology improve the efficiency of contact data processing in a virtual data warehouse? 2. What are the key factors considered when assigning contact grouping queries to specific data pipelines?
Original Abstract Submitted
described herein are techniques for deploying a plurality of data processing pipelines to execute contact grouping queries to upload contact data into contact records of contact group tables in a cloud-based, virtual data warehouse. each contact grouping query is assigned to a data pipeline of a plurality of data pipelines based on characteristics of the contact grouping query and the count of contact records associated with the entity on whose behalf the contact records are being maintained. additionally, the execution runtime is obtained for each contact grouping query so that an average execution runtime for the contact grouping query can be cached. an expected execution runtime of all contact grouping queries allocated to a specific data pipeline can then be calculated and used in determining whether additional data pipelines may be necessary to meet a service level objective that indicates a time interval during which a contact grouping query is to be executed.
