Event-Driven Architecture for Real-Time Financial News Processing and Bilingual Publishing

Financial news has characteristics such as strong time sensitivity, dispersed sources, diverse formats, and frequent bilingual publishing. For high-frequency information such as macroeconomic events, protocol status changes, and shifts in market expectations, a platform must ensure speed while also ensuring accuracy and traceability. Simple scheduled polling and manual assembly can easily lead to duplicate publishing, inconsistent bilingual versions, missed state-change releases, and error content that is difficult to roll back. Therefore, an event-driven core is needed to build a full pipeline covering collection, normalization, extraction, alignment, review, and publishing.

Overall Layered Architecture

The system should be divided into an access layer, a processing layer, a storage layer, a publishing layer, and a governance layer. The access layer handles real-time ingestion and authentication of multi-source information. The processing layer performs cleaning, deduplication, structured extraction, bilingual alignment, and risk review. The storage layer stores raw drafts, normalized drafts, event snapshots, translation versions, and publishing records. The publishing layer provides aggregated queries and push delivery for different terminals. The governance layer covers logs, metrics, tracing, alerting, and permission auditing. The key to this layering is to decouple raw content from publishable content, so that every publish can be traced back to the input, processing rules, reviewer, and version number.

Key Design of the Real-Time Pipeline

1. Collection and Access

Multi-source data should enter a common message queue component in a unified way, tagged by source, language, and timestamp. During access, perform signature validation, rate limiting, and structured validation to prevent invalid traffic from impacting downstream systems. For batch historical data, generic object storage components can be used for archiving, followed by batch processing for backfill.

2. Normalization and Event Extraction

Normalization includes unified encoding, unified time formats, body paragraph segmentation, entity placeholders, and sensitive field tagging. Event extraction should output fields such as event type, subject, status, time, impact scope, and confidence, rather than directly generating final copy. State-change events need to retain the before and after states so that semantics such as “effective,” “paused,” and “pending negotiation” can be determined.

3. Bilingual Alignment and Version Management

Bilingual drafts should not be produced through literal sentence-by-sentence translation. Instead, alignment relationships should be established by paragraph, event, and timestamp. Glossaries and style guides should be versioned, and translation results should carry version numbers and confidence levels. Before publishing, validate consistency of key fields such as time, subject, status, and numbers. Any modification on either side should trigger a difference check and re-review.

4. Review and Publishing

Review is divided into rule-based review and manual review. Rule-based review covers sensitive words, numeric anomalies, state conflicts, and duplicate publishing. Manual review focuses on high-risk events. The publishing layer uses a publish ticket mechanism that records the content version, review records, effective time, and rollback point. For terminal-facing queries, caching and content distribution can be added to reduce latency for hot access.

Consistency and Failure Compensation

A real-time pipeline must assume that failures will occur. It is recommended to use “at-least-once delivery + idempotent consumption” to ensure no loss and no duplication. Generate a globally unique ID for each event and perform an idempotency check before publishing. Translation and review are long-running workflows and can be managed with a state machine: pending processing, processing, pending review, published, and rolled back. On failure, events enter a retry queue; when a threshold is exceeded, they are escalated to manual handling. If an error is found after publishing, version-based rollback should be supported, and change audits should be retained.

Observability and Security

Observability should cover metrics, logs, and tracing. Metrics include ingestion volume, processing latency, extraction failure rate, translation consistency alert count, review backlog, publishing success rate, and rollback count. Logs must be desensitized, and tracing must be able to locate the raw input and processing nodes corresponding to a publish ticket. For security, permissions for collection, review, and publishing should be divided by role, critical operations should require dual-person review, and external queries should be protected by rate limiting and caching.

Illustrative Pseudocode

function process(raw):
    validated = validate(raw)
    normalized = normalize(validated)
    event = extract(normalized)
    if not consistent(event):
        enqueue_review(event)
        return
    version = align_bilingual(event)
    if audit(event, version) == PASS:
        publish_id = idempotent_publish(version)
        record(publish_id, event, version)
    else:
        enqueue_review(event)

The pseudocode above reflects three principles: validate before processing, review after bilingual alignment, and ensure publishing is idempotent and recordable.

Deployment and Implementation Recommendations

On Baidu AI Cloud, generic categories such as object storage, message queues, container platforms, log services, and monitoring and alerting can be combined to support archiving, buffering, elastic compute, auditing, and alerting. For implementation, start with a small-traffic canary release and onboard by source and language in batches. Establish a glossary and an event type dictionary. Set up manual fallback for high-risk events. Review publishing discrepancies and rollback reasons weekly. Through layered decoupling, event-driven processing, versioning, and observable governance, the platform can reduce publishing risk while ensuring timeliness and provide a stable foundation for later expansion to more languages and terminals.