Data management components include multimodal storage for structured, semi-structured, and unstructured data types, batch and streaming processing engines, integration for seamless data connectivity, and a serving layer offering APIs and interfaces for on-demand access. The core services of integration, processing, governance, and belarus rcs data operations collectively enhance platform functionality. Integration handles ETL/ELT processes, processing supports batch and real-time analytics, governance maintains data quality and compliance, and operational services optimize platform performance and ensure smooth functioning.
The data lifecycle comprises ingestion, storage, processing, serving, and archival. To achieve scalability and persistence, data is gathered from diverse sources, validated, and securely stored. into insights through cleansing and aggregation, supporting real-time and batch needs. The serving layer provides end-users access to processed data through APIs and dashboards, while archival moves less-accessed data to cost-effective storage, ensuring compliance. Together, these stages form a complete, secure data journey from ingestion to actionable insights.
Essential Non-Functional and DataOps Attributes
A modern data platform requires essential non-functional and DataOps features to operate effectively. Scalability, elasticity, reliability, and performance ensure the platform can handle growing data volumes and user demands. Security and compliance protect data access and enforce regulatory standards while monitoring, telemetry, and observability provide continuous diagnostics and insights.