The Scheduling module in the Quantela Platform automates data ingestion and processing, ensuring that cleansed and transformed datasets are delivered at the right intervals for analysis, reporting, and operational workflows. It provides a structured execution framework for coordinating data flow between sources, transformation pipelines, and external systems.
Using CRON-based scheduling, real-time triggers, and event-driven workflows, the module enables seamless data exchanges while reducing manual intervention. This ensures consistent and timely data availability across integrated environments, enhancing operational efficiency and decision-making.
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Key Features:
After data is ingested through connectors and processed for cleansing and transformation, the platform’s Data Ingestion Function maps it to a predefined schema and business rules. The scheduler then automates data ingestion, storage, and availability for analytics, reporting, and automation. With built-in error handling, retry policies, and dependency resolution, the system ensures data consistency across scheduled runs.
The scheduling engine leverages adapters to manage inbound and outbound data flows. Inbound adapters handle both scheduled and on-demand data pulls, using CRON expressions to regulate execution frequency. They also support asynchronous data pushes, storing time-sensitive information incrementally for historical analysis and anomaly detection. Outbound adapters automate system actions, such as triggering alerts for environmental violations or controlling IoT devices like smart streetlights based on real-time data.
With CRON-based scheduling, users can define precise execution windows for data ingestion, transformation, and export. Schedulers can be dynamically enabled or disabled to optimize system workloads. The platform also supports broadcasting, receivers, and streaming mechanisms, allowing multi-threaded scheduling workflows to efficiently process high-volume data.
The module offers advanced processing capabilities, enabling filtering, merging, and transformation before data ingestion. By eliminating redundant or incomplete data at the scheduling level, it optimizes storage and computational efficiency. Persistent incremental storage ensures historical data remains available for analysis, machine learning, and anomaly detection. With event-driven scheduling, businesses can automate real-time responses, keeping systems synchronized and operational with precision and reliability.
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