Change Data Capture (CDC) Configuration

The cdcSettings and cdcSnapshotSettings enable and pass configuration info to the CDC API’s.

Field	Type	Description
cdcSettings	`object`	See cdcSettings for more information.
cdcSnapshotSettings	`object`	See cdcSnapshotSettings for more information.

cdcSettings

The cdcSettings object contains the following properties:

Parameter	Type	Description
keys	`list`	The column or combination of columns that uniquely identify a row in the source data. This is used to identify which CDC events apply to specific records in the target table.
sequence_by	str	The column name specifying the logical order of CDC events in the source data. Delta Live Tables uses this sequencing to handle change events that arrive out of order.
scd_type	`string`	Whether to store records as SCD type 1 or SCD type 2. Set to `1` for SCD type 1 or 2 for SCD type `2`.
apply_as_deletes	`string`	(optional) Specifies when a CDC event should be treated as a DELETE rather than an upsert.
where	`string`	(optional) Filter the rows by a condition.
ignore_null_updates	`boolean`	(optional) Allow ingesting updates containing a subset of the target columns. When a CDC event matches an existing row and ignore_null_updates is True, columns with a null retain their existing values in the target. This also applies to nested columns with a value of null. When ignore_null_updates is False, existing values are overwritten with null values.
except_column_list	`list`	(optional) A list of columns to exclude from the upsert into the target table.
track_history_column_list track_history_except_column_list	`list`	A subset of output columns to be tracked for history in the target table. Use track_history_column_list to specify the complete list of columns to be tracked. Use track_history_except_column_list to specify the columns to be excluded from tracking.

cdcSnapshotSettings

The cdcSnapshotSettings object contains the following properties:

CDC Historical Snapshot Source Configuration

The source object contains the following properties for file based sources:

Parameter

Type

Description

format

string

The format of the source data. E.g. supported formats are table, parquet, csv, json. All formats supported by spark see PySpark Data Sources API.

path

string

The location to load the source data from. This can be a table name or a path to a file or directory with multiple snapshots. Supports three path pattern styles for version extraction: the {version} placeholder (simple single-segment match), the {fragment} placeholder (for multi-file snapshots), and regex named capture groups (for complex partitioning). See File Path Patterns for details and examples.

versionType

string

The type of versioning to use. Can be either int or datetime.

datetimeFormat

string

(conditional) Required if versionType is datetime. The format of startingVersion datetime value.

microSecondMaskLength

integer

(optional) WARNING: Edge Cases Only! - Specify this if your versionType is datetime and your filename includes microsends, but not the full 6 digits. The number of microsecond digits to included at the end of the datetime value. - The default value is 6.

startingVersion

string or integer

(optional) The version to start processing from.

readerOptions

object

(optional) Additional options to pass to the reader.

schemaPath

string

(optional) The schema path to use for the source data.

selectExp

list

(optional) A list of select expressions to apply to the source data.

filter

string

(optional) A filter expression to apply to the source data. This filter is applied to the dataframe as a WHERE clause when the source is read. The placeholder {version} can be used in this filter expression and will be substituted with the version value at run time (e.g. "year = '{version}'"). Not applicable when using regex named capture groups in path.

recursiveFileLookup

boolean

(optional) When set to true, enables recursive directory traversal to find snapshot files. This should be used when snapshots are stored in a nested directory structure such as Hive-style partitioning (e.g., /data/{version}/file.parquet). When set to false (default), only files in the immediate directory are searched. Default: false.

Note

If recursiveFileLookup is set to true, ensure that the path parameter is compatible with recursive directory traversal. When using the {version} placeholder, place it in the directory portion of the path rather than the filename (e.g. /data/{version}/file.parquet). When using regex named capture groups, the pattern spans the full relative path from the first dynamic segment, so recursiveFileLookup must be true if the version spans multiple directory levels.

File Path Patterns

The path field supports three styles for expressing where the version (and optional fragment) appears in the file path. All styles can be combined with a static base path prefix that is resolved at run time (e.g. {sample_file_location}).

Style

Syntax

When to Use

{version} placeholder

{version}

Version is contained in a single path segment or filename component. Simple and readable for flat or single-level partitioned layouts.

{fragment} placeholder

{fragment}

Snapshot data for a single version is split across multiple files. Use alongside {version} to group files sharing the same version together.

Regex named capture groups

(?P<version_<name>>.+)

Version is spread across multiple path segments or interleaved with other text. Supports complex partitioning schemes (e.g. Hive-style YEAR=.../MONTH=.../DAY=...) where the version cannot be expressed as a single placeholder.

``{version}`` — single-segment version

The {version} placeholder matches one path segment or filename component. It is internally converted to a regex named capture group (?P<version_main>.+).
{
  "path": "/mnt/data/snapshots/customer_{version}.csv",
  "versionType": "timestamp",
  "datetimeFormat": "%Y_%m_%d"
}
Files matched: customer_2024_01_01.csv, customer_2024_01_02.csv, …

For directory-partitioned layouts, place {version} in the directory portion and set recursiveFileLookup to true:
{
  "path": "/mnt/data/snapshots/{version}/customer.csv",
  "versionType": "timestamp",
  "datetimeFormat": "YEAR=%Y/MONTH=%m/DAY=%d",
  "recursiveFileLookup": true
}
Files matched: YEAR=2024/MONTH=01/DAY=01/customer.csv, …

``{fragment}`` — multi-file snapshots

Use {fragment} alongside {version} when a single snapshot version is split across multiple files. All files sharing the same version are read and unioned together before CDC processing.
{
  "path": "/mnt/data/snapshots/customer_{version}_split_{fragment}.csv",
  "versionType": "timestamp",
  "datetimeFormat": "%Y_%m_%d"
}
Files matched and grouped by version: customer_2024_01_01_split_1.csv, customer_2024_01_01_split_2.csv → both ingested as version 2024-01-01.

Regex named capture groups — multi-segment versions

For cases where the version is distributed across multiple directory levels or interleaved with fixed text, use Python regex named capture groups with the prefix version_. All groups whose names start with version_ are extracted and concatenated in the order they appear in the pattern (left to right) to form the final version string, which is then parsed according to datetimeFormat or treated as an integer.

Group naming convention: (?P<version_<name>>.+). The <name> suffix is arbitrary but must be unique within the pattern. The concatenation order is determined by the position of each group in the path expression, not the name.
{
  "path": "/mnt/data/snapshots/(?P<version_year>.+)/(?P<version_month>.+)/data/customer_(?P<version_day>.+).csv",
  "versionType": "timestamp",
  "datetimeFormat": "%Y%m%d",
  "recursiveFileLookup": true
}
For the file 2024/01/data/customer_15.csv, the groups are captured left-to-right: version_year=2024, version_month=01, version_day=15. These are concatenated in pattern order to produce "20240115", which is then parsed with datetimeFormat: "%Y%m%d".

Tip

Arrange your (?P<version_...>) groups in the path from left to right in the same order that their values should be concatenated to match your datetimeFormat. The group names themselves only need to be unique — their order in the pattern controls concatenation.

See samples/bronze_sample/src/dataflows/feature_samples/dataflowspec/historical_snapshot_files_datetime_recursive_and_partitioned_regex_main.json for a complete working example.

The source object contains the following properties for table based sources:

Parameter

Type

Description

table

string

The table name to load the source data from.

versionColumn

string

The column name to use for versioning.

startingVersion

string or integer

(optional) The version to start processing from.

selectExp

list

(optional) A list of select expressions to apply to the source data.