Real-time Automated Data Analytics via Lambda Architecture
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Solution Overview
Problem
Conventional data analytics techniques often produce stale insights due to the need for extensive processing steps, failing to provide real-time, actionable information for market conditions and lacking capabilities for margin and risk data analytics, automated detection, and user trend analysis.
Innovation Solution
A method utilizing artificial intelligence and machine learning to aggregate raw data in real-time, transform it using lambda functions, generate structured datasets, and create predictive outputs, which are then used to generate dashboards and alert clients via a graphical user interface, compatible with mobile devices, while ensuring data validation and entitlement rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional data analytic techniques are implemented, then data processing can be performed, but the output becomes stale due to multiple processing steps and time delays
Solution Approach 1:
The patent implements a lambda architecture that performs preliminary processing by maintaining incremental state updates and pre-computed aggregates in the speed layer. This allows real-time query responses without executing full processing pipelines, as the system has already prepared intermediate results in advance through continuous data streaming and state maintenance.
Solution Approach 2:
The patent divides the data processing system into three distinct layers: the speed layer for real-time incremental processing, the batch layer for comprehensive periodic processing, and the serving layer for query fulfillment. This segmentation allows different processing modes to operate independently and concurrently, eliminating the need to choose between real-time performance and thorough processing.
2Adaptability or versatility
If conventional data analytic techniques are used, then basic data processing is achievable, but advanced capabilities such as margin and risk analytics, automated detection, and trend analysis are not facilitated
Solution Approach 1:
The patent creates a universal data processing platform that handles multiple analytics operations through a common architecture. The same lambda framework supports real-time monitoring, margin calculations, risk analytics, automated anomaly detection, and trend analysis by applying different analytical models and configurations to the underlying data stream, rather than requiring separate systems for each function.
Solution Approach 2:
The patent introduces a standardized data functionalization layer that transforms raw data into structured, queryable formats with consistent schemas and metadata. This intermediary representation enables diverse analytics operations to be performed on uniformly processed data, simplifying the complexity by providing a common interface between data ingestion and various analytical workloads.
3Productivity
If real-time automated data analytics are implemented using AI and machine learning, then timely insights and automated alerts are achieved, but data processing complexity increases
Solution Approach 1:
The patent implements automated machine learning model training and deployment within the batch layer, where models are continuously retrained on accumulating data and automatically updated in the serving layer without manual intervention. The system self-manages model versioning, performance monitoring, and retraining schedules, reducing the operational complexity of maintaining AI capabilities while achieving real-time insights through automated model inference.
Data Source
AI summary
A method for facilitating automated data analytics in real-time via artificial intelligence is disclosed. The method includes automatically aggregating, in real-time, raw data from various sources, the sources including a source application that persists the raw data in a data storage container; triggering, by using a lambda function, a transformation process for the raw data; generating, based on an output of the transformation process, structured data sets from the aggregated raw data; persisting the structured data sets in a repository, the repository including a distributed database; determining, by using a machine learning model, predictive outputs based on the persisted structured data sets; and generating, in real-time, a dashboard by using the predictive outputs and the structured data sets.