Cloud Data Collector Infrastructure Templates for Flexible Schema Ingestion
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Solution Overview
Problem
Analyzing and searching massive quantities of machine data generated in modern data centers and computing environments is challenging due to the vast variety and volume of data types and formats, with existing tools often discarding non-preprocessed data and limiting analysis flexibility.
Innovation Solution
A cloud data collector (CDC) application generates infrastructure templates to configure the ingestion of user data from service provider networks into a data intake and query system, allowing for flexible schema development and late-binding schema application, enabling the storage and analysis of minimally processed machine data for real-time querying and search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If pre-processing is applied to reduce data volume, then storage requirements are reduced, but data flexibility and analysis capability are lost
Solution Approach 1:
The system performs preliminary actions by generating infrastructure templates that define data collection configurations before data ingestion occurs. These templates establish schemas and data models in advance, enabling the system to prepare for various data types and formats without pre-processing the actual data, thus maintaining both storage efficiency and analysis flexibility.
Solution Approach 2:
The system implements dynamic schema evolution through late-binding schema application. Unlike static pre-processing approaches, the schema is applied dynamically at query time or data ingestion time, allowing the system to adapt to different data formats and structures without committing to a fixed preprocessing configuration, thereby preserving analysis flexibility while managing data volume.
2Adaptability or versatility
If minimal processing is applied to retain all data, then data flexibility is improved, but system complexity increases
Solution Approach 1:
The system segments the data processing function into distinct components: infrastructure template generation, data ingestion, schema application, and query processing. By separating these functions, the system manages complexity through modular architecture while retaining all minimally processed data for flexible analysis. Each component handles a specific aspect, reducing overall system complexity despite comprehensive data retention.
Solution Approach 2:
The infrastructure template acts as an intermediary between data sources and the data intake system. It defines the schema and configuration rules that govern how data is ingested and stored without requiring complex real-time processing decisions. This intermediary layer simplifies the system by pre-establishing data handling rules while allowing flexible data retention.
3Adaptability or versatility
If infrastructure templates are generated for data ingestion, then data flexibility is improved, but processing time is increased
Solution Approach 1:
The system performs preliminary schema definition through infrastructure template generation before data ingestion. By establishing the data model, field definitions, and configuration rules in advance, the system eliminates the need for complex real-time schema interpretation during data processing. This preliminary setup reduces processing time while maintaining schema flexibility for diverse data types.
Solution Approach 2:
The system applies schemas dynamically at ingestion or query time rather than requiring extensive pre-processing. This dynamic approach allows the infrastructure templates to define flexible data models without committing rigid preprocessing transformations, reducing processing time while preserving adaptability for different data formats and analysis requirements.
Data Source
AI summary
Techniques are described for providing a cloud data collector (CDC) application for managing the generation of infrastructure templates. The CDC application provides graphical user interfaces that enable a user to provide inputs indicating configurations of data to be ingested by the data intake and query system, each configuration including one or more user accounts, in addition to data sources and regions associated with data sources. Using the configurations provided as input to the CDC application, the CDC application generates an infrastructure template that can be used to configure the service provider network to provide the requested security data to the data intake and query system.