Configurable Metadata Flows for Integrated Data Analytics
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Solution Overview
Problem
Existing data analytics platforms are limited by their architecture, requiring technical expertise, segregating data products, lacking scalability and security, and failing to provide integrated insights across multiple data products.
Innovation Solution
A serverless data analytics platform that automates deployment and scaling, using metadata-driven flows and flexible tenancy systems to process data across multiple clients, supporting various interfaces and ensuring security and compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing data analytics platforms use separate handling for different data products, then data segregation is maintained, but users cannot gain integrated insights across multiple data products
Solution Approach 1:
The patent merges multiple data products into a unified data lake architecture where data from different sources is ingested and stored together in a common repository. This allows users to perform integrated analytics across multiple data products while maintaining the ability to access and process individual data products separately when needed, thus achieving both integration and flexibility.
Solution Approach 2:
The data lake architecture serves multiple functions: it acts as a centralized storage repository, an integration layer for cross-product analytics, and a foundation for various analytics workloads. This universal platform handles diverse data types and sources while providing a consistent interface for users, eliminating the need for separate handling mechanisms for different data products.
2Productivity
If data analytics platforms depend on physical infrastructure like on-premises server farms, then data processing capability is provided, but scalability becomes difficult
Solution Approach 1:
The platform implements automated self-service capabilities including automatic resource provisioning, dynamic scaling, and self-managed data ingestion pipelines. The system can automatically allocate computing resources and storage capacity based on workload demands without requiring manual intervention or fixed physical infrastructure, enabling seamless scaling from small to large datasets.
Solution Approach 2:
The architecture transitions from static on-premises server farms to dynamic cloud-based infrastructure that can automatically adjust computing and storage resources in real-time based on data volume and analytics workload. This dynamic allocation allows the platform to scale elastically, providing high productivity for large-scale data processing while maintaining adaptability to changing requirements.
3Reliability
If data analytics platforms require multiple years of training for proficiency, then technical expertise is ensured, but ease of operation deteriorates
Solution Approach 1:
The patent introduces an intermediary layer consisting of pre-built data pipelines, automated ETL processes, and standardized analytics interfaces that mediate between raw data and user queries. This abstraction layer handles complex data processing, cleaning, and transformation tasks automatically, allowing users with minimal technical training to perform sophisticated analytics by simply specifying their analytical needs through intuitive interfaces.
Solution Approach 2:
The platform provides self-service analytics capabilities where the system automatically performs data ingestion, processing, and preparation based on user-defined parameters. Automated pipelines handle data extraction, transformation, and loading without requiring users to understand underlying technical processes, thereby maintaining reliability through systematic processing while dramatically reducing the learning curve for users.
4Ease of manufacture
If data analytics platforms lack automation, then technical specialists can attend to data extraction and loading details, but operational efficiency decreases
Solution Approach 1:
The system implements automated self-service data extraction, transformation, and loading pipelines that operate without manual intervention. The automated pipelines continuously ingest data from various sources, perform necessary transformations, and load processed data into the data lake according to predefined schedules and triggers, thereby maintaining data freshness and quality while significantly improving operational efficiency compared to manual specialist operations.
Solution Approach 2:
The automated data pipelines enable continuous data extraction, processing, and loading operations rather than periodic manual batch processing. The system maintains continuous data flow from sources to the data lake, ensuring that data is always up-to-date and available for analytics without interruption, thereby enhancing both the capability to handle data operations and the overall productivity of the analytics platform.
5Device complexity
If data analytics platforms lack security and data monitoring capabilities, then system simplicity is maintained, but compliance with regulations deteriorates
Solution Approach 1:
The patent segments security and compliance functions into distinct modular components including access control lists, data classification mechanisms, audit logging systems, and policy enforcement modules. These segmented security components operate independently within the unified data lake architecture, allowing comprehensive security and compliance capabilities to be implemented without fundamentally complicating the overall system structure. Each security module handles specific aspects of data protection, making the system both secure and maintainable.
Data Source
AI summary
A data analytics system is disclosed that is configured to perform operations comprising creating at least one data storage, creating a metadata store separate from the at least one data storage, creating a flow storage, and configuring a flow service using first received instructions. The flow service is configured to obtain a first flow from the flow storage, obtain metadata from the metadata storage, and execute the flow. The flow execution can include obtaining input data from the at least one data storage, generating output data at least in part by validating, transforming, and serializing the input data using the metadata, and generating additional metadata describing the output data. The flow execution can further include providing the output data for storage in the at least one data storage and providing the additional metadata for storage in the metadata storage.