Full-Process DataOps Practices for Large-Scale Business Data Reporting at Baidu

Introduction: Baidu's commercial data products require large‑scale data report production, prompting a full‑process DataOps practice.

Challenges include massive data volume, high engineering cost, and thousands of report metrics, demanding efficient development, stable pipelines, and rapid issue resolution.

DataOps design adopts a layered architecture (raw, warehouse, metric, report) and a unified platform (DataBoot) that provides end‑to‑end workflow, standardized tooling, and serverless deployment across control, service, and compute layers.

Development uses a web‑based IDE built on Monaco, integrated with Baidu Icode for code management and multi‑cluster job debugging, delivering one‑stop data task development.

Deployment leverages a three‑tier serverless model (control, service, compute) with elastic scaling, function‑as‑a‑service, and resource pooling to handle bursty workloads.

Risk control in the release phase employs CI/CD pipelines, mock testing, data lineage, SLA monitoring, and component‑wise gray‑release to mitigate single‑point and chain‑wide failures.

Monitoring and observability provide full‑link metrics, resource usage, latency attribution, and timeline analysis, enabling automatic fault detection and root‑cause identification.

Operations include automated data back‑tracking using cloud‑control, lineage probes, and execution engines to recover from dirty data incidents.

Optimization combines global report latency experiments, declarative dynamic tuning, and automated feedback loops to balance performance, cost, and stability.

Conclusion: DataOps has become essential for Baidu's data‑driven business, and future integration with AIOps is expected to further boost data engineering productivity.