To replicate MySQL to BigQuery with Sling, configure both connections, define the tables and replication mode in a YAML file, then run it with sling run -r. Use full-refresh for an initial load, timestamp/key-based incremental mode for recurring batches when the source has reliable keys, or transaction-log CDC when inserts, updates, and deletes must be captured.
What you need before configuring Sling
- A MySQL source that Sling can connect to and a Google Cloud project with a BigQuery dataset as the destination.
- Sling installed and available on your command line. The official guide documents installers for macOS/Linux and Windows, plus a Python package; follow its current installation instructions and verify availability with
sling --version. - A BigQuery service-account key file and the required target-project access. Sling’s guide specifies BigQuery Data Editor and BigQuery Job User roles for its example.
- A decision about which tables to copy and whether the target should be refreshed, updated in batches, or kept current from MySQL’s transaction log.
Keep credentials out of shared or checked-in YAML files. Sling’s guide covers installation, connection configuration, and the replication workflow.
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Configure and test the source and target connections
Create named Sling connections for MySQL and BigQuery using the connection configuration format in Sling’s guide. For BigQuery, configure the service-account key file and confirm that the account points to the intended Google Cloud project and has the required roles. Test both connections before starting a transfer; a successful connection test does not by itself confirm that the selected dataset, table scope, or replication behavior is correct.
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| Mode | How it works | Use it when | Important limitation |
|---|---|---|---|
full-refresh |
Loads the selected data as a full refresh. | For an initial load or a small table that is appropriate to reload. | Review the target naming and overwrite behavior for the specific mode and Sling version before using it against production data. |
incremental |
Polls for qualifying rows using an update key and uses a primary key for the intended merge behavior. | For recurring batch updates when the source has a reliable timestamp or other suitable monotonic update key, plus a primary key that identifies rows. | This key-based approach is not transaction-log CDC; do not assume it captures deletes. Sling’s guide says that without a timestamp or monotonic key, its described incremental approach is limited to full-refresh or snapshot modes. |
| CDC | Reads MySQL’s binary log after an initial load to capture ongoing row-level inserts, updates, and deletes. | When changes, including deletes, need to flow from the transaction log. | Sling documents a CLI Pro Max token or an Advanced Platform Plan as prerequisites. Verify current plan availability and MySQL binlog requirements before deployment. |
Sling describes CDC as continuously replicating inserts, updates, and deletes by reading a database transaction log; that description is from Sling’s own CDC documentation, not an independent standards definition.
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Write a replication YAML file
A replication file has source, target, optional defaults, and streams sections. Defaults apply common settings, while settings on an individual stream can override them. The example below shows the structure; replace connection names, paths, dataset names, tables, and keys with values valid for your environment.
source: mysql_source
target: bigquery_target
defaults:
mode: full-refresh
streams:
demo.*:
object: '{stream_table}'
In this illustration, demo.* selects all tables in the demo schema, while {stream_table} retains each source table’s name in the target. Confirm the wildcard scope and destination naming before running: a broad pattern can include more tables than intended. Sling’s replication reference describes streams, defaults, and per-stream settings.
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For a first full load
Set mode: full-refresh for the initial load when a full refresh is appropriate. Check the destination dataset and the mode’s overwrite behavior against the Sling version you will operate; do not infer production-safe replacement behavior from a generic example.
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For recurring incremental batches
Set the stream to mode: incremental and supply an update_key and a primary_key. The update key must reliably mark new or changed records, and the primary key must uniquely identify the rows for the intended merge behavior. Sling’s example uses a timestamp as the update key and an order ID as the primary key; those are examples, not universal choices. If source updates can occur without changing the selected update key, or if the key is not reliable, rows may not be selected as intended.
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For transaction-log CDC
Use Sling’s CDC workflow when MySQL inserts, updates, and deletes need to be captured from the binary log. CDC has an initial-load phase followed by ongoing change capture. Confirm binlog configuration, account access, and the applicable Sling plan before relying on it.
Run the replication
- Save the configuration as a YAML file, for example
replication.yaml, and ensure the named connections resolve in the environment where Sling will run. - Run
sling run -r path/to/replication.yaml, replacing the path with the actual file path. - Check the run result and confirm that the intended source tables and destination objects were processed. Verify the target data before scheduling recurring executions.
How Sling loads data into BigQuery
Sling’s guide describes loading through BigQuery bulk load jobs, rather than inserting each row individually. With a Google Cloud Storage bucket configured, Sling can stage batches there; without one, the guide says Sling stages locally and still uses bulk loading. Choose a staging path that fits your access, storage, and operational requirements.
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Sling reports an example of 80,000 rows loaded in about 23 seconds and a separate incremental run that inserted 2,500 rows in 44 seconds. These are SlingData.IO’s 2026 vendor-reported examples for its described run against MySQL 8.4 and a live BigQuery dataset, not independent benchmarks or a forecast of performance for another workload. Source
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Schedule and operate recurring runs
Sling’s guide shows cron as a basic way to schedule a working replication. A scheduler alone does not provide production monitoring or recovery. Decide how your deployment will log runs, alert on failures, retry safely, and recover from missed or partial executions. The guide describes target-based checkpoints for database targets, but that example should not be treated as a complete operational design for BigQuery.
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- Validate the Google Cloud project, dataset, service-account roles, and credentials used by the scheduled environment.
- Review the wildcard or individual stream list, destination dataset, and table-name template.
- Check that stream-specific overrides do not unintentionally replace shared defaults.
- Choose the mode according to the source’s change semantics, especially whether deletes need to propagate.
- Confirm the run can be observed and that failures have an assigned retry and recovery process.
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