Ingestion Manager Orchestrating Analytics Data Workspaces
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Solution Overview
Problem
Information processing systems face challenges in efficiently managing data ingestion from multiple distinct data sources, particularly in selecting appropriate ingestion engines based on data type, volume, latency, and service level agreements (SLAs), while ensuring compliance and optimizing data usage across analytics workspaces.
Innovation Solution
An ingestion manager is configured to control multiple ingestion engines, selecting the appropriate engines for each analytics workspace based on data characteristics and historical usage, ensuring compliance with SLAs, and orchestrating data ingestion modes such as bulk, near-real-time, and change data capture, while managing data distribution and quality across a data lake environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple distinct ingestion engines are used to handle different data sources, then data ingestion capability and versatility are improved, but system complexity increases
Solution Approach 1:
An ingestion manager is introduced as an intermediary component that sits between multiple ingestion engines and the analytics platform. The manager receives ingestion requests, selects appropriate engines based on data characteristics and SLA requirements, and coordinates their execution. This mediator approach allows the system to leverage multiple specialized engines without exposing their complexity to users, resolving the contradiction by hiding system complexity while maintaining versatility.
Solution Approach 2:
The ingestion manager is designed as a universal component that can handle multiple types of data sources, ingestion modes (bulk, near-real-time, change data capture), and SLA requirements through a single unified interface. This multi-functional design allows one component to perform what previously required multiple separate systems, reducing overall system complexity while maintaining the ability to ingest diverse data types.
2Speed
If data ingestion is optimized for speed and latency, then data analytics performance is improved, but data quality and compliance may deteriorate
Solution Approach 1:
The system dynamically adjusts ingestion parameters based on real-time conditions and SLA requirements. For time-critical analytics workloads, the ingestion manager configures engines for faster processing with appropriate quality checks. For compliance-critical workloads, it configures engines for more thorough validation and quality assurance. This dynamic configuration allows the system to optimize for speed when appropriate while maintaining quality when required, resolving the contradiction between performance and reliability.
Solution Approach 2:
Different quality assurance measures are applied locally to different data streams based on their specific requirements. The ingestion manager evaluates each data source and target analytics workspace to determine appropriate quality checks, validation rules, and compliance verification levels. This localized approach ensures that data quality and compliance are maintained at appropriate levels for each specific use case without unnecessarily slowing down all ingestion operations.
3Productivity
If comprehensive data ingestion management is implemented, then data usage optimization is improved, but operational complexity increases
Solution Approach 1:
The ingestion manager implements self-service capabilities by automatically evaluating data sources, selecting appropriate ingestion engines and modes, and configuring parameters based on historical performance data and current SLA requirements. The system monitors ingestion performance and autonomously optimizes data flow without requiring manual intervention. This automation resolves the contradiction by handling operational complexity internally while providing users with simplified interfaces for managing data usage.
Data Source
AI summary
An apparatus in one embodiment comprises an ingestion manager, a plurality of ingestion engines associated with the ingestion manager, and an analytics platform configured to receive data from the ingestion engines under the control of the ingestion manager. The ingestion manager is configured to interact with one or more of the ingestion engines in conjunction with providing data to a given one of a plurality of analytics workspaces of the analytics platform. For example, the analytics workspaces of the analytics platform are illustratively configured to receive data from respective potentially disjoint subsets of the ingestion engines under the control of the ingestion manager. Additionally or alternatively, the ingestion manager may be configured to implement data-as-a-service functionality for one or more of the analytics workspaces of the analytics platform.