How We Cut Offline Data Warehouse SLA Delay from 13 Days to Zero with DataLeap

"Xingfu Li" is a real‑estate media platform under the Douyin Group that provides diversified property information and customized house‑search services. As the business grew, the team quickly built a data warehouse but early "build‑first‑fix‑later" practices led to frequent data‑governance problems, most notably an offline warehouse SLA delay of up to 13 days, which hurt real‑time data value for agents and users.

Within Douyin, the "0987" high‑quality service evaluation system ranks data‑mid‑platform stability as the top priority, requiring zero SLA failures. For the Xingfu Li team, the high SLA latency became a core unresolved issue.

To address this, the team introduced Volcano Engine's big‑data R&D governance suite DataLeap , reducing the offline warehouse SLA from 13 days to 0 days. The project is presented through strategy formulation, task identification, standard definition, and promotion, aiming to offer SLA‑governance insights to other enterprises.

Step 1: Identify Core SLA‑Protected Tasks

The team first scoped three categories of tasks that must be SLA‑guaranteed:

Online core tasks that are directly displayed to B‑side agents or C‑side users.

Management‑dashboard data such as daily, weekly, and monthly reports.

Key business core dashboards, e.g., the 2022 Fu‑zhou priority business data.

Step 2: Formulate a Global Guarantee Plan

Using DataLeap, the team launched SLA governance for the identified core tasks. The platform supports task owners to declare tasks, initiate upstream SLA signing, and automate notifications, reducing coordination cost and accelerating SLA agreements.

Baseline monitoring was introduced to detect abnormal tasks early. Only 34% of core tasks originally had baseline monitoring, leading to frequent false alarms and operational pressure. DataLeap calculates expected baseline times based on recent task performance (30‑day percentile, variance, longest chain length, downstream count, expected output time) and provides configurable alert buffers.

Data quality monitoring was also applied to Hive tables, ensuring that missing or duplicated data would not affect critical metrics such as the "Happiness Score" for agents.

Step 3: Quantify SLA Effects and Conduct Retrospective

DataLeap’s SLA dashboard offers daily SLA statistics, delay trend analysis, SLA level distribution, task health details, and team‑level SLA achievement metrics. From June to December 2022, the offline SLA achieved zero incident days and zero delay days, meeting the "0987" evaluation target.

Key results include:

Core task baseline coverage increased to 97.4% (a 63% improvement).

Alarm volume decreased by 28.4%.

Overall data stability and operational cost were significantly improved.

The implementation also established a standardized SLA workflow: pre‑incident SLA declaration, real‑time alert refinement, and post‑incident review, creating a sustainable governance model for the platform.