hey @Rocky qq - any recommended tool to implement automatic data quality checks to proactively detect issues such as missing or stale data? i.e like anomaly detection

For implementing automatic data quality (DQ) and proactive anomaly detection in StarRocks, several ecosystem tools are commonly recommended. Since StarRocks is MySQL-compatible, it works with most tools that support the MySQL protocol or JDBC. *1. dbt + Elementary (Recommended for Anomaly Detection)* If you are already using

dbt-starrocks

for your transformations, Elementary is the most popular choice for proactive anomaly detection. * How it works: It runs as a dbt package and automatically monitors your tables for volume anomalies, freshness (staleness), and schema changes. * Proactive detection: It uses a trailing-window approach to learn the "normal" range of your data (e.g., typical daily row count) and alerts you (via Slack/Teams) if a load is significantly smaller or larger than usual. * Missing/Stale data: You can use

dbt source freshness

or Elementary's automated freshness monitors to detect if data hasn't been updated within a specific TTL. *2. Soda (Soda Core / Soda Cloud)* Soda is highly recommended for its human-readable YAML-based check language (SodaCL). * Staleness checks: It has built-in syntax for freshness, such as

freshness(last_updated_column) < 24h

. * Anomaly detection: Soda offers "Anomaly Score" checks that use machine learning to detect unexpected changes in metrics (like record counts or column averages) without requiring you to set hard thresholds. * Integration: While there isn't always a "native" StarRocks connector listed, Soda works with StarRocks via its MySQL or JDBC configurations. *3. Great Expectations (GX)* Great Expectations is a robust Python-based framework often used for complex validation. * Capabilities: It provides "Expectations" (assertions) for everything from

expect_table_row_count_to_be_between

to custom distribution-based checks. * Best for: Highly customized validation logic and generating "Data Docs" (visual reports) of your data quality status. * Integration: It connects to StarRocks using the SQLAlchemy-based

starrocks

dialect. *4. DataWorks (For Alibaba Cloud / EMR Users)* If you are running StarRocks on Alibaba Cloud (EMR Serverless StarRocks), DataWorks provides a built-in Data Quality module. * Rules: You can configure "Strong" rules (e.g., row count > 0) to block downstream ETL jobs if data is missing, or "Weak" rules to just send alerts. * Staleness: It tracks row count fluctuations daily to detect anomalies in data ingestion. Summary Comparison Tool | Best For | Anomaly Detection | Integration Elementary | Teams already using dbt | Automated ML-based anomalies | dbt package Soda | Real-time monitoring & alerts | "Anomaly Score" (ML) | MySQL/JDBC Great Expectations | Complex, Python-heavy logic | Statistical distribution checks | SQLAlchemy/JDBC DataWorks | Cloud-native StarRocks | Row count fluctuation rules | Native (Aliyun) Rocky's Tip: For a quick start on staleness, you can also set up a simple SQL-based alert in your BI tool or Grafana that checks:

SELECT MAX(load_time) FROM your_table;

If the difference between

NOW()

and

MAX(load_time)

exceeds your threshold, you have a staleness issue. References • integrations/dbt.md

Since StarRocks is MySQL-compatible, it works with most tools that support the MySQL protocol or JDBC.

many of these tools use

performance_schema

and

information_schema

databases for that; does the starrocks implementation follow the mysql standards for metadata?

Choosing between these tools depends on your existing stack and who will manage the checks. Here is a more tailored breakdown to help you decide which path to take: *1. The "Path of Least Resistance": dbt + Elementary* If you are already using (or planning to use)

dbt-starrocks

, Elementary is usually the right choice. * Why it wins: It is "set and forget." You add a few lines to your

dbt_project.yml

, and it automatically starts building a baseline for row counts and freshness. * Anomaly detection: It uses a Z-score approach (statistical deviation) to detect if a table grew by 500% or didn't grow at all today. * Visibility: It generates a report and sends Slack alerts that include a "reason" (e.g., "Table grew 3x more than the average of the last 14 days"). *2. The "Simple & Decoupled" Choice: Soda* If you have a team of analysts who know SQL but don't want to manage complex Python environments or dbt projects, use Soda. * Why it wins: Its language (SodaCL) is very readable. Writing a staleness check is as simple as:

yaml
    checks for orders:
      • freshness(created_at) < 24h
      • row_count_anomaly

* Proactive detection: Soda’s "Anomaly Score" check handles the ML part for you without you having to define hard thresholds. *3. The "Heavyweight / Regulated" Choice: Great Expectations (GX)* If you are in a highly regulated industry (FinTech, Healthcare) where you need detailed "Data Docs" for every validation run, use GX. * Why it wins: It is the most mature framework. It provides deep statistical checks (e.g., "I expect the mean of this column to be within X standard deviations"). * Trade-off: It has a steeper learning curve. It is Python-heavy, so it's better for Data Engineering teams rather than Analysts. *4. The "Native/Internal" Way: Routine Load Alerts* For staleness specifically, if you are using Routine Load (Kafka), you don't necessarily need a 3rd party tool for the first layer of defense. * Prometheus/Grafana: You can monitor

starrocks_fe_routine_load_max_lag_of_partition

. If the lag exceeds a certain number of messages or time, you have a staleness issue. * Audit Logs: You can query the

fe.audit.log

(via the AuditLoader plugin) to find queries that return 0 rows unexpectedly or monitor the frequency of load jobs. Recommendation Summary: * Go with Elementary if you want automated anomaly detection with the least amount of manual configuration (provided you use dbt). * Go with Soda if you want a standalone monitoring service that is easy for non-engineers to read and update. * Go with Custom SQL in Airflow if you have very specific business logic (e.g., "Data is stale only if Table A is updated but Table B hasn't been updated yet") and want zero extra tool overhead. Pro-tip for StarRocks: Because StarRocks is often used for real-time data, look for tools that support partition-level checks. Running a full table scan on a 10TB table for a DQ check is expensive; you want a tool that can target just the

last_3_hours

or the

latest_partition

. Soda and Elementary both support this well via "filters" or "where" clauses. References • integrations/airflow.md • integrations/IDE_integrations/Dataphin.md • knowledge/admin_and_operation/shared_data_compaction.md