> ## Documentation Index
> Fetch the complete documentation index at: https://www.integrate.io/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# ELT/CDC: Available Metrics Types
> Explore the available metric types in Integrate.io ELT & CDC monitoring. Track replication delay, sync lag, and pipeline health indicators.
| | **Metric Level** | **Data Type** | **Description** |
| :---------------------- | :--------------- | :-------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Nulls** | Table | All Types | The count of rows with a null value in the column e.g.
Sample dataset:
Null
Null
23
Metric value:
**2** |
| **Count** | Table | All Types | The total number of rows in a table e.g.
Sample dataset:
78
6
89
23
45
Metric value:
**5** |
| **Cardinality** | Table | All Types | The count of distinct elements in the column. This metric should be used when you expect a fixed number of value options e.g.
Sample dataset:
Cat
Dog
Cat
Metric value:
**2** |
| **Duplicate** | Table | All Types | The count of rows with a duplicate value in the column (excluding the unique value). eg
Sample dataset:
Apple
Apple
Pear
Pear
Banana
Pear
Metric value:
**3**
*Apple has 1 duplicate, Pear has 2 duplicates, result is 3* |
| **Min** | Table | Numeric | The minimum value of the column e.g.
Sample dataset:
78
6
89
23
45
Metric value:
**6** |
| **Max** | Table | Numeric | The maximum value of the column e.g.
Sample dataset:
78
6
89
23
45
Metric value:
**89** |
| **Median** | Table | Numeric | The [median](https://en.wikipedia.org/wiki/Median) of the column. The median is computed as the 50th percentile, and will only return a value that is in the dataset e.g.
Sample dataset:
78
6
89
23
45
Metric value:
**45** |
| **Skewness** | Table | Numeric | The [statistical skew](https://en.wikipedia.org/wiki/Skewness) of the column. The skew is used to determine how evenly the values are distributed about the mean e.g.
Sample dataset:
78
6
89
23
45
Metric value:
**0.0303028253** |
| **Variance Sample** | Table | Numeric | The [variance](https://en.wikipedia.org/wiki/Variance) of a column from its [sample mean](https://en.wikipedia.org/wiki/Sample_mean_and_covariance). This should be used to calculate the variance when the data represents a sample taken from a larger data set e.g.
Sample dataset:
78
6
89
23
45
Metric value:
**1244.7** |
| **Variance Population** | Table | Numeric | The [variance](https://en.wikipedia.org/wiki/Variance) of a column from its [population mean](https://en.wikipedia.org/wiki/Statistical_population#Mean). This should be used to calculate the variance when the data represents the entire data set e.g.
Sample dataset:
78
6
89
23
45
Metric value:
**995.76** |
| **Geometric Mean** | Table | Numeric | The [geometric mean](https://en.wikipedia.org/wiki/Geometric_mean) of a column e.g.
Sample dataset:
78
6
89
23
45
Metric value:
**33.644590126644** |
| **Freshness (hrs)** | Table | Date
Timestamp | The difference in hours of the maximum value of the column and the metric’s collection time.
Sample dataset in UTC (Metric collected at 2022-12-07 03:48:00 UTC):
2022-12-06 05:24:26
2022-12-07 01:33:36
2022-12-07 00:33:44
Metric value:
**2.24** |
| **Sync Lag** | Pipeline | NA | The Sync Lag metric measures the difference between the source data and the pipeline in megabytes (MB), providing a real-time indicator of pipeline performance. A high sync lag value (typically above 100MB, depending on pipeline load) may indicate abnormal pipeline behavior. |
| **Replication Delay** | Pipeline | NA | The **Replication Delay** metric measures the time, in minutes, since the pipeline last copied data from the intermediary storage (S3) to the destination. It provides a clear indicator of how timely data replication is occurring. |