Monitors are a core concept of SYNQ and help you proactively detect issues through self-learning and powerful anomaly monitors
Monitors help you proactively detect issues such as missing or stale data, a change in a segment within your data, or a drop in a metric. SYNQ supports multiple monitors, but it’s helpful to distinguish them by
Automatic monitor queries your metadata such as the Snowflake Information Schema. This means they’re easy to configure as you don’t have to define a custom time dimension. As automatic monitors only query metadata, they incur minimal additional processing costs.
The table stats monitor is an all-in-one monitor for row count, freshness, and delay. It’s out recommended automatic monitor.
The freshness monitor looks at a table’s timestamp and alerts you if it’s been too long since the last update.
The volume monitor looks at the number of rows in a table to detect how much data is added or removed and alerts you of abnormal increases or decreases.
The schema monitor detects changes in your environment and alerts you of added, removed, or updated fields.
Custom monitors can help detect issues more nuanced to your business, such as a custom monitor detecting a change in business metrics or a drop in one segment. Custom monitors query your raw data and should be used selectively to avoid incurring unnecessary costs. Custom monitors have to be configured individually by, e.g., specifying a time dimension, a field to group the monitor by, and custom SQL for an aggregate metric (e.g., avg(revenue)
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The custom volume monitor detects issues in the volume of data in a time series and is highly configurable.
The custom monitor detects issues in a time series, as represented by your specific SQL statement.
The field stats monitor detects issues in the health of a specific field (e.g., %, not null or % empty values).
Most SYNQ monitors are based on a time series model to predict the expected value of, e.g., the number of rows or a business metric you specified as an SQL statement. If a new data point falls significantly outside our prediction confidence interval, we alert you of an anomaly. Our model uses seasonal decomposition to build the most robust anomaly detection.
Each monitor has a severity that helps you understand the current state.
Selecting a monitor shows you the current severity
See all monitors by type and their severity in the Health overview