Add snapshots to your DAG

Related documentation

• Snapshot configurations
• Snapshot properties
• snapshot command

Learn by video!

For video tutorials on Snapshots, go to dbt Learn and check out the Snapshots course.

What are snapshots?

Analysts often need to "look back in time" at previous data states in their mutable tables. While some source data systems are built in a way that makes accessing historical data possible, this is not always the case. dbt provides a mechanism, snapshots, which records changes to a mutable table over time.

Snapshots implement type-2 Slowly Changing Dimensions over mutable source tables. These Slowly Changing Dimensions (or SCDs) identify how a row in a table changes over time. Imagine you have an orders table where the status field can be overwritten as the order is processed.

id	status	updated_at
1	pending	2024-01-01

Now, imagine that the order goes from "pending" to "shipped". That same record will now look like:

id	status	updated_at
1	shipped	2024-01-02

This order is now in the "shipped" state, but we've lost the information about when the order was last in the "pending" state. This makes it difficult (or impossible) to analyze how long it took for an order to ship. dbt can "snapshot" these changes to help you understand how values in a row change over time. Here's an example of a snapshot table for the previous example:

id	status	updated_at	dbt_valid_from	dbt_valid_to
1	pending	2024-01-01	2024-01-01	2024-01-02
1	shipped	2024-01-02	2024-01-02	null

Configuring snapshots

(Applies to dbt v1.9 and later)

Configure your snapshots in YAML files to tell dbt how to detect record changes. Define snapshots configurations in YAML files, alongside your models, for a cleaner, faster, and more consistent set up. Place snapshot YAML files in the models directory or in a snapshots directory.

snapshots/orders_snapshot.yml

snapshots:
  - name: string
    relation: relation # source('my_source', 'my_table') or ref('my_model')
    description:  markdown_string
    config:
      database: string
      schema: string
      alias: string
      strategy: timestamp | check
      unique_key: column_name_or_expression
      check_cols: [column_name] | all
      updated_at: column_name
      snapshot_meta_column_names: dictionary
      dbt_valid_to_current: string
      hard_deletes: ignore | invalidate | new_record 

The following table outlines the configurations available for snapshots:

Config	Description	Required?	Example
database	Specify a custom database for the snapshot	No	analytics
schema	Specify a custom schema for the snapshot	No	snapshots
alias	Specify an alias for the snapshot	No	your_custom_snapshot
strategy	The snapshot strategy to use. Valid values: timestamp or check	Yes	timestamp
unique_key	A primary key column(s) (string or array) or expression for the record	Yes	id or [order_id, product_id]
check_cols	If using the check strategy, then the columns to check	Only if using the check strategy	["status"]
updated_at	A column in your snapshot query results that indicates when each record was last updated, used in the timestamp strategy. May support ISO date strings and unix epoch integers, depending on the data platform you use.	Only if using the timestamp strategy	updated_at
dbt_valid_to_current	Set a custom indicator for the value of dbt_valid_to in current snapshot records (like a future date). By default, this value is NULL. When configured, dbt will use the specified value instead of NULL for dbt_valid_to for current records in the snapshot table.	No	string
snapshot_meta_column_names	Customize the names of the snapshot meta fields	No	dictionary
hard_deletes	Specify how to handle deleted rows from the source. Supported options are ignore (default), invalidate (replaces the legacy invalidate_hard_deletes=true), and new_record.	No	string

• In v1.9, target_schema became optional, allowing snapshots to be environment-aware. By default, without target_schema or target_database defined, snapshots now use the generate_schema_name or generate_database_name macros to determine where to build.
• Developers can still set a custom location with schema and database configs, consistent with other resource types.
• A number of other configurations are also supported (for example, tags and post-hook). For the complete list, refer to Snapshot configurations.
• You can configure snapshots from both the dbt_project.yml file and a config block. For more information, refer to the configuration docs.
• Starting dbt Core v1.12, you can inspect the SQL generated for snapshots by running dbt compile. You can find compiled SQL files in the target/compiled/ directory of your dbt project.

Add a snapshot to your project

To add a snapshot to your project follow these steps. For users on versions 1.8 and earlier, refer to Legacy snapshot configurations.

1. Create a properties YAML file in your snapshots directory: snapshots/orders_snapshot.yml and add your configuration details. You can also configure your snapshot from your project YAML file (dbt_project.yml) (docs).

   snapshots/orders_snapshot.yml

   snapshots:
     - name: orders_snapshot
       relation: source('jaffle_shop', 'orders')
       config:
         schema: snapshots
         database: analytics
         unique_key: id
         strategy: timestamp
         updated_at: updated_at
         dbt_valid_to_current: "to_date('9999-12-31')" # Specifies that current records should have `dbt_valid_to` set to `'9999-12-31'` instead of `NULL`.
   

2. (Optional) Apply transformations using an ephemeral model. By default, snapshots reference a source directly (as shown in the YAML in the previous step). If you need to apply transformations (such as filtering or deduplication), define an ephemeral model first to apply those transformations, and reference it in the snapshot relation field instead of calling source() directly.

   For example, here's an ephemeral model:

   models/ephemeral_orders.sql

   {{ config(materialized='ephemeral') }}
   
   select * from {{ source('jaffle_shop', 'orders') }}

   This is how to reference the ephemeral model in the relation field:

   snapshots/orders_snapshot.yml

     snapshots:
       - name: orders_snapshot
         relation: ref('ephemeral_orders')
         ... rest of config...

3. Check whether the result set of your query includes a reliable timestamp column that indicates when a record was last updated. For our example, the updated_at column reliably indicates record changes, so we can use the timestamp strategy. If your query result set does not have a reliable timestamp, you'll need to instead use the check strategy — more details on this below.

4. Run the dbt snapshot command — for our example, a new table will be created at analytics.snapshots.orders_snapshot. The schema config will utilize the generate_schema_name macro.

   $ dbt snapshot
   Running with dbt=1.9.0
   
   15:07:36 | Concurrency: 8 threads (target='dev')
   15:07:36 |
   15:07:36 | 1 of 1 START snapshot snapshots.orders_snapshot...... [RUN]
   15:07:36 | 1 of 1 OK snapshot snapshots.orders_snapshot..........[SELECT 3 in 1.82s]
   15:07:36 |
   15:07:36 | Finished running 1 snapshots in 0.68s.
   
   Completed successfully
   
   Done. PASS=2 ERROR=0 SKIP=0 TOTAL=1
   Compiled SQL for snapshots

   Starting dbt Core v1.12, you can inspect the SQL generated for this snapshot by running dbt compile or dbt compile --select orders_snapshot.

   Open the compiled SQL in target/compiled/ to inspect or debug the generated queries. Each snapshot is compiled into its own SQL file, even if multiple snapshots are defined in the same source file.

5. Inspect the results by selecting from the table dbt created (analytics.snapshots.orders_snapshot). After the first run, you should see the results of your query, plus the snapshot meta fields as described later on.

6. Run the dbt snapshot command again and inspect the results. If any records have been updated, the snapshot should reflect this.

7. Select from the snapshot in downstream models using the ref function.

   models/changed_orders.sql

   select * from {{ ref('orders_snapshot') }}

8. Snapshots are only useful if you run them frequently — schedule the dbt snapshot command to run regularly.

Configuration best practices

 Use the timestamp strategy where possible

The timestamp strategy is recommended because it handles column additions and deletions more efficiently than the check strategy. This is because it's more robust to schema changes, especially when columns are added or removed over time.

The timestamp strategy relies on a single updated_at field, which means it avoids the need to constantly update your snapshot configuration as your source table evolves.

Why timestamp is the preferred strategy:

• Requires tracking only one column (updated_at)
• Automatically handles new or removed columns in the source table
• Less prone to errors when the table schema evolves over time (for example, if using the check strategy, you might need to update the check_cols configuration)

 Use dbt_valid_to_current for easier date range queries

By default, dbt_valid_to is NULL for current records. However, if you set the dbt_valid_to_current configuration (available in dbt Core v1.9+), dbt_valid_to will be set to your specified value (such as 9999-12-31) for current records.

This allows for straightforward date range filtering.

 Ensure your unique key is really unique

The unique key is used by dbt to match rows up, so it's extremely important to make sure this key is actually unique! If you're snapshotting a source, I'd recommend adding a uniqueness test to your source (example).

(Applies to dbt v1.9 and later)

 Use a schema that is separate to your models' schema

Snapshots can't be rebuilt. Because of this, it's a good idea to put snapshots in a separate schema so end users know they're special. From there, you may want to set different privileges on your snapshots compared to your models, and even run them as a different user (or role, depending on your warehouse) to make it very difficult to drop a snapshot unless you really want to.

 Use ephemeral model to clean or transform data before snapshotting

If you need to clean or transform your data before snapshotting, create an ephemeral model or a staging model that applies the necessary transformations. Then, reference this model in your snapshot configuration. This approach keeps your snapshot definitions clean and allows you to test and run transformations separately.

How snapshots work

When you run the dbt snapshot command:

• On the first run: dbt will create the initial snapshot table — this will be the result set of your select statement, with additional columns including dbt_valid_from and dbt_valid_to. All records will have a dbt_valid_to = null or the value specified in dbt_valid_to_current (available in dbt Core 1.9+) if configured.
• On subsequent runs: dbt will check which records have changed or if any new records have been created:
  • The dbt_valid_to column will be updated for any existing records that have changed.
  • The updated record and any new records will be inserted into the snapshot table. These records will now have dbt_valid_to = null or the value configured in dbt_valid_to_current (available in dbt Core v1.9+).

Snapshots ignore full refresh

Snapshots ignore both the full_refresh config and the --full-refresh flag. A command such as dbt build --full-refresh or dbt snapshot --full-refresh that includes a snapshot node runs the snapshot as normal — it won't drop or recreate the snapshot table, so existing snapshot history is preserved.

(Applies to dbt v1.9 and later)

Note

• These column names can be customized to your team or organizational conventions using the snapshot_meta_column_names config.
• Use the dbt_valid_to_current config to set a custom indicator for the value of dbt_valid_to in current snapshot records (like a future date such as 9999-12-31). By default, this value is NULL. When set, dbt will use this specified value instead of NULL for dbt_valid_to for current records in the snapshot table.
• Use the hard_deletes config to track hard deletes by adding a new record when row become "deleted" in source. Supported options are ignore, invalidate, and new_record.

Snapshots can be referenced in downstream models the same way as referencing models — by using the ref function.

Detecting row changes

Snapshot "strategies" define how dbt knows if a row has changed. There are two strategies built-in to dbt:

• Timestamp — Uses an updated_at column to determine if a row has changed.
• Check — Compares a list of columns between their current and historical values to determine if a row has changed.

Timestamp strategy (recommended)

The timestamp strategy uses an updated_at field to determine if a row has changed. If the configured updated_at column for a row is more recent than the last time the snapshot ran, then dbt will invalidate the old record and record the new one. If the timestamps are unchanged, then dbt will not take any action.

Why timestamp is recommended?

• Requires tracking only one column (updated_at)
• Automatically handles new or removed columns in the source table
• Less prone to errors when the table schema evolves over time (for example, if using the check strategy, you might need to update the check_cols configuration)

The timestamp strategy requires the following configurations:

Config	Description	Example
updated_at	A column which represents when the source row was last updated. May support ISO date strings and unix epoch integers, depending on the data platform you use.	updated_at

Example usage:

(Applies to dbt v1.9 and later)

snapshots/orders_snapshot.yml

snapshots:
  - name: orders_snapshot_timestamp
    relation: source('jaffle_shop', 'orders')
    config:
      schema: snapshots
      unique_key: id
      strategy: timestamp
      updated_at: updated_at

Check strategy

The check strategy is useful for tables which do not have a reliable updated_at column. This strategy works by comparing a list of columns between their current and historical values. If any of these columns have changed, then dbt will invalidate the old record and record the new one. If the column values are identical, then dbt will not take any action.

The check strategy requires the following configurations:

Config	Description	Example
check_cols	A list of columns to check for changes, or all to check all columns	["name", "email"]

check_cols = 'all'

The check snapshot strategy can be configured to track changes to all columns by supplying check_cols = 'all'. It is better to explicitly enumerate the columns that you want to check. Consider using a surrogate key to condense many columns into a single column.

Example usage

(Applies to dbt v1.9 and later)

snapshots/orders_snapshot.yml

snapshots:
  - name: orders_snapshot_check
    relation: source('jaffle_shop', 'orders')
    config:
      schema: snapshots
      unique_key: id
      strategy: check
      check_cols:
        - status
        - is_cancelled

Example usage with updated_at

When using the check strategy, dbt tracks changes by comparing values in check_cols. By default, dbt uses the timestamp to update dbt_updated_at, dbt_valid_from and dbt_valid_to fields. Optionally you can set an updated_at column:

• If updated_at is configured, the check strategy uses this column instead, as with the timestamp strategy.
• If updated_at value is null, dbt defaults to using the current timestamp.

Check out the following example, which shows how to use the check strategy with updated_at:

snapshots:
  - name: orders_snapshot
    relation: ref('stg_orders')
    config:
      schema: snapshots
      unique_key: order_id
      strategy: check
      check_cols:
        - status
        - is_cancelled
      updated_at: updated_at

In this example:

• If at least one of the specified check_cols changes, the snapshot creates a new row. If the updated_at column has a value (is not null), the snapshot uses it; otherwise, it defaults to the timestamp.
• If updated_at isn’t set, then dbt automatically falls back to using the current timestamp to track changes.
• Use this approach when your updated_at column isn't reliable for tracking record updates, but you still want to use it — rather than the snapshot's execution time — whenever row changes are detected.

Hard deletes (opt-in)

(Applies to dbt v1.9 and later)

In dbt v1.9 and higher, the hard_deletes config replaces the invalidate_hard_deletes config to give you more control on how to handle deleted rows from the source. The hard_deletes config is not a separate strategy but an additional opt-in feature that can be used with any snapshot strategy.

The hard_deletes config has three options/fields:

Field	Description
ignore (default)	No action for deleted records.
invalidate	Behaves the same as the existing invalidate_hard_deletes=true, where deleted records are invalidated by setting dbt_valid_to.
new_record	Tracks deleted records as new rows using the dbt_is_deleted meta field when records are deleted.

 When to use the hard_deletes and invalidate_hard_deletes config?

Use invalidate_hard_deletes (v1.8 and earlier) if:

• Gaps in the snapshot history (missing records for deleted rows) are acceptable.
• You want to invalidate deleted rows by setting their dbt_valid_to timestamp to the current time (implicit delete).
• You are working with smaller datasets where tracking deletions as a separate state is unnecessary.

Use hard_deletes: new_record (v1.9 and higher) if:

• You want to maintain continuous snapshot history without gaps.
• You want to explicitly track deletions by adding new rows with a dbt_is_deleted column (explicit delete).
• You are working with larger datasets where explicitly tracking deleted records improves data lineage clarity.

Example usage

snapshots/orders_snapshot.yml

snapshots:
  - name: orders_snapshot_hard_delete
    relation: source('jaffle_shop', 'orders')
    config:
      schema: snapshots
      unique_key: id
      strategy: timestamp
      updated_at: updated_at
      hard_deletes: new_record  # options are: 'ignore', 'invalidate', or 'new_record'

In this example, the hard_deletes: new_record config will add a new row for deleted records with the dbt_is_deleted column set to True. Any restored records are added as new rows with the dbt_is_deleted field set to False.

The resulting table will look like this:

id	status	updated_at	dbt_valid_from	dbt_valid_to	dbt_is_deleted
1	pending	2024-01-01 10:47	2024-01-01 10:47	2024-01-01 11:05	False
1	shipped	2024-01-01 11:05	2024-01-01 11:05	2024-01-01 11:20	False
1	deleted	2024-01-01 11:20	2024-01-01 11:20	2024-01-01 12:00	True
1	restored	2024-01-01 12:00	2024-01-01 12:00		False

Snapshot meta-fields

Snapshot tables will be created as a clone of your source dataset, plus some additional meta-fields.

In dbt Core v1.9+ (or available sooner in the Latest release track in dbt):

• These column names can be customized to your team or organizational conventions using the snapshot_meta_column_names config.
• Use the dbt_valid_to_current config to set a custom indicator for the value of dbt_valid_to in current snapshot records (like a future date such as 9999-12-31). By default, this value is NULL. When set, dbt will use this specified value instead of NULL for dbt_valid_to for current records in the snapshot table.
• Use the hard_deletes config to track deleted records as new rows with the dbt_is_deleted meta field when using the hard_deletes='new_record' field.

Field	Meaning	Notes	Example
dbt_valid_from	The timestamp when this snapshot row became valid.	Use this to order versions of a record. For a given row, this often matches dbt_updated_at, but they mean different things. Refer to Strategy and meta-field timestamps for examples by strategy.	snapshot_meta_column_names: {dbt_valid_from: start_date}
dbt_valid_to	The timestamp when this row became invalidated. For current records, this is NULL by default or the value specified in dbt_valid_to_current.	The most recent snapshot record will have dbt_valid_to set to NULL or the specified value.	snapshot_meta_column_names: {dbt_valid_to: end_date}
dbt_scd_id	A unique key generated for each snapshot row.	Used internally by dbt to identify each SCD version. Refer to How dbt_scd_id is calculated to learn how dbt builds this key.	snapshot_meta_column_names: {dbt_scd_id: scd_id}
dbt_updated_at	The source record's change timestamp when this snapshot row was inserted.	Used internally by dbt. Strategy and meta-field timestamps describes which value populates this column for each strategy.	snapshot_meta_column_names: {dbt_updated_at: modified_date}
dbt_is_deleted	A string value indicating if the record has been deleted. (True if deleted, False if not deleted).	Added when hard_deletes='new_record' is configured.	snapshot_meta_column_names: {dbt_is_deleted: is_deleted}

All of these column names can be customized using the snapshot_meta_column_names config. Refer to this example for more details.

On insert, dbt_valid_from and dbt_updated_at are set from the same value. They represent validity start and recorded change time, respectively.

Strategy and meta-field timestamps

The timestamps used for each column depend on the strategy you use:

• For the timestamp strategy, the configured updated_at column is used to populate the dbt_valid_from, dbt_valid_to and dbt_updated_at columns.

   Sample results for the timestamp strategy

  Snapshot query results at 2024-01-01 11:00

  id	status	updated_at
  1	pending	2024-01-01 10:47

  Snapshot results (note that 11:00 is not used anywhere):

  id	status	updated_at	dbt_valid_from	dbt_valid_to	dbt_updated_at
  1	pending	2024-01-01 10:47	2024-01-01 10:47		2024-01-01 10:47

  Query results at 2024-01-01 11:30:

  id	status	updated_at
  1	shipped	2024-01-01 11:05

  Snapshot results (note that 11:30 is not used anywhere):

  id	status	updated_at	dbt_valid_from	dbt_valid_to	dbt_updated_at
  1	pending	2024-01-01 10:47	2024-01-01 10:47	2024-01-01 11:05	2024-01-01 10:47
  1	shipped	2024-01-01 11:05	2024-01-01 11:05		2024-01-01 11:05

  Snapshot results with hard_deletes='new_record':

  id	status	updated_at	dbt_valid_from	dbt_valid_to	dbt_updated_at	dbt_is_deleted
  1	pending	2024-01-01 10:47	2024-01-01 10:47	2024-01-01 11:05	2024-01-01 10:47	False
  1	shipped	2024-01-01 11:05	2024-01-01 11:05	2024-01-01 11:20	2024-01-01 11:05	False
  1	deleted	2024-01-01 11:20	2024-01-01 11:20		2024-01-01 11:20	True

• For the check strategy, the current timestamp is used to populate each column. If configured, the check strategy uses the updated_at column instead, as with the timestamp strategy.

   Sample results for the check strategy

  Snapshot query results at 2024-01-01 11:00

  id	status
  1	pending

  Snapshot results:

  id	status	dbt_valid_from	dbt_valid_to	dbt_updated_at
  1	pending	2024-01-01 11:00		2024-01-01 11:00

  Query results at 2024-01-01 11:30:

  id	status
  1	shipped

  Snapshot results:

  id	status	dbt_valid_from	dbt_valid_to	dbt_updated_at
  1	pending	2024-01-01 11:00	2024-01-01 11:30	2024-01-01 11:00
  1	shipped	2024-01-01 11:30		2024-01-01 11:30

  Snapshot results with hard_deletes='new_record':

  id	status	dbt_valid_from	dbt_valid_to	dbt_updated_at	dbt_is_deleted
  1	pending	2024-01-01 11:00	2024-01-01 11:30	2024-01-01 11:00	False
  1	shipped	2024-01-01 11:30	2024-01-01 11:40	2024-01-01 11:30	False
  1	deleted	2024-01-01 11:40		2024-01-01 11:40	True

FAQs

How do I run one snapshot at a time?

To run one snapshot, use the --select flag, followed by the name of the snapshot:

$ dbt snapshot --select order_snapshot

Check out the model selection syntax documentation for more operators and examples.

How often should I run the snapshot command?

Snapshots are a batch-based approach to change data capture. The dbt snapshot command must be run on a schedule to ensure that changes to tables are actually recorded! While individual use-cases may vary, snapshots are intended to be run between hourly and daily. If you find yourself snapshotting more frequently than that, consider if there isn't a more appropriate way to capture changes in your source data tables.

What happens if I add new columns to my snapshot query?

When the columns of your source query changes, dbt will attempt to reconcile this change in the destination snapshot table. dbt does this by:

1. Creating new columns from the source query in the destination table
2. Expanding the size of string types where necessary (eg. varchars on Redshift)

dbt will not delete columns in the destination snapshot table if they are removed from the source query. It will also not change the type of a column beyond expanding the size of varchar columns. That is, if a string column is changed to a date column in the snapshot source query, dbt will not attempt to change the type of the column in the destination table.

Do hooks run with snapshots?

Yes! The following hooks are available for snapshots:

• pre-hooks
• post-hooks
• on-run-start
• on-run-end

Can I store my snapshots in a directory other than the `snapshot` directory in my project?

By default, dbt expects your snapshot files to be located in the snapshots subdirectory of your project.

To change this, update the snapshot-paths configuration in your dbt_project.yml file, like so:

dbt_project.yml

snapshot-paths: ["snapshots"]

Note that you cannot co-locate snapshots and models in the same directory.

Debug Snapshot target is not a snapshot table errors

If you see the following error when you try executing the snapshot command:

Snapshot target is not a snapshot table (missing dbt_scd_id, dbt_valid_from, dbt_valid_to)

Double check that you haven't inadvertently caused your snapshot to behave like table materializations by setting its materialized config to be table. Prior to dbt version 1.4, it was possible to have a snapshot like this:

{% snapshot snappy %}
  {{ config(materialized = 'table', ...) }}
  ...
{% endsnapshot %}

dbt is treating snapshots like tables (issuing create or replace table ... statements) silently instead of actually snapshotting data (SCD2 via insert / merge statements). When upgrading to dbt versions 1.4 and higher, dbt now raises a Parsing Error (instead of silently treating snapshots like tables) that reads:

A snapshot must have a materialized value of 'snapshot'

This tells you to change your materialized config to snapshot. But when you make that change, you might encounter an error message saying that certain fields like dbt_scd_id are missing. This error happens because, previously, when dbt treated snapshots as tables, it didn't include the necessary snapshot meta-fields in your target table. Since those meta-fields don't exist, dbt correctly identifies that you're trying to create a snapshot in a table that isn't actually a snapshot.

When this happens, you have to start from scratch — re-snapshotting your source data as if it was the first time by dropping your "snapshot" which isn't a real snapshot table. Then dbt snapshot will create a new snapshot and insert the snapshot meta-fields as expected.

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