# Connect Cdc Sqlserver

> Streams change data capture from Microsoft SQL Server into Redpanda or Kafka using Redpanda Connect's microsoft_sql_server_cdc input, which reads native CDC change tables in LSN order. Use when enabling SQL Server CDC capture instances, configuring microsoft_sql_server_cdc, setting up an initial snapshot alongside live streaming, tuning LSN checkpoint storage, or troubleshooting LSN gaps and missing Agent jobs. Also covers Redpanda Enterprise destination features such as Iceberg Topics, Tiered Storage, and server-side Schema ID Validation, since microsoft_sql_server_cdc itself requires a Redpanda Enterprise license.

- Skill: `redpanda-data/connect-cdc-sqlserver-2` (Agent Skill, multi-file: 6 files)
- Install (CLI): `npx skillmds@latest add redpanda-data/connect-cdc-sqlserver-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/redpanda-data/connect-cdc-sqlserver-2/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: redpanda-data (https://skillmd.com/u/redpanda-data)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/redpanda-data/connect-cdc-sqlserver-2

---


# Redpanda Connect CDC: Microsoft SQL Server

The `microsoft_sql_server_cdc` input in Redpanda Connect streams change events from Microsoft SQL Server's native CDC change tables directly into Redpanda or any Kafka-compatible output. It reads SQL Server's built-in change tables (`cdc.<schema>_<tablename>_CT`) using LSN (Log Sequence Number) ordering, optionally precedes streaming with a consistent point-in-time snapshot of existing rows, and durably checkpoints the last-delivered LSN so the pipeline can resume after a restart without re-consuming stale data.

This is an **Enterprise** component — it requires a valid Redpanda Enterprise license. The component name registered in Connect is `microsoft_sql_server_cdc`.

## Quickstart

**Step 1–3: Run the following T-SQL in SSMS or `sqlcmd` (as a sysadmin user):**

```sql
-- 1. Enable CDC on the database
USE MyDatabase;
EXEC sys.sp_cdc_enable_db;
GO

-- 2. Enable CDC on each table you want to capture
EXEC sys.sp_cdc_enable_table
  @source_schema = N'dbo',
  @source_name   = N'orders',
  @role_name     = NULL;
GO

EXEC sys.sp_cdc_enable_table
  @source_schema = N'dbo',
  @source_name   = N'customers',
  @role_name     = NULL;
GO

-- 3. Create the rpcn schema for the built-in checkpoint cache
--    and grant the Connect user rights to create objects in it
CREATE SCHEMA rpcn;
GO

GRANT CREATE TABLE TO connect_user;
GRANT CREATE PROCEDURE TO connect_user;
GRANT ALTER ON SCHEMA::rpcn TO connect_user;
GO
```

**Step 4: Run the pipeline from a shell:**

```bash
rpk connect run mssql-cdc.yaml
```

`mssql-cdc.yaml` (copy-pasteable):

```yaml
input:
  microsoft_sql_server_cdc:
    connection_string: "sqlserver://connect_user:secret@sqlserver-host/MyDatabase?database=MyDatabase"
    stream_snapshot: true          # snapshot existing rows before streaming changes
    max_parallel_snapshot_tables: 2
    snapshot_max_batch_size: 1000
    include:
      - "^dbo\\.orders$"      # anchored for exact match — unanchored also matches dbo.orders_archive
      - "^dbo\\.customers$"
    # Built-in checkpoint cache: auto-creates rpcn.CdcCheckpointCache table
    checkpoint_cache_table_name: rpcn.CdcCheckpointCache
    checkpoint_limit: 1024
    stream_backoff_interval: 5s
    batching:
      count: 100
      period: 1s

output:
  kafka_franz:
    seed_brokers:
      - localhost:9092
    topic: '${! meta("table") }'   # route each table to its own topic
    key: '${! json("id") }'        # key by primary key for correct ordering and log compaction
```

To route to a Redpanda Cloud cluster with SASL:

```yaml
output:
  kafka_franz:
    seed_brokers:
      - seed-abc123.cloud.redpanda.com:9092
    tls:
      enabled: true
    sasl:
      - mechanism: SCRAM-SHA-256
        username: my-user
        password: "${REDPANDA_PASSWORD}"
    topic: '${! "mssql." + meta("database_schema") + "." + meta("table") }'
```

## Prerequisites

Before starting the pipeline:

1. **SQL Server CDC enabled at database level** — `sys.sp_cdc_enable_db` must have been run on the source database.
2. **SQL Server Agent running** — CDC uses Agent jobs to scan the transaction log and populate change tables. Without it, change tables stay empty.
3. **Capture instance per table** — `sys.sp_cdc_enable_table` creates a change table (`cdc.<schema>_<tablename>_CT`) for each table. Only tables with a capture instance emit changes.
4. **Table has a primary key** — the snapshot phase issues keyset-pagination queries; tables without a primary key cannot be snapshotted and will error at connect time.
5. **Connect user permissions** — the user in `connection_string` needs `SELECT` on the source tables and the CDC change tables. For the built-in checkpoint cache it also needs `CREATE TABLE` and `CREATE PROCEDURE` rights on the target schema, and that schema (`rpcn` by default) must already exist.

See [setup-sqlserver.md](references/setup-sqlserver.md) for the complete T-SQL setup walkthrough.

## Config Fields

All fields are under `input.microsoft_sql_server_cdc`.

| Field | Type | Default | Required | Description |
|---|---|---|---|---|
| `connection_string` | string | — | yes | ADO.NET / go-mssqldb DSN. Format: `sqlserver://user:pass@host/instance?database=DB` |
| `stream_snapshot` | bool | `false` | no | When `true`, snapshot all existing rows before streaming changes. When `false`, streaming still starts at the beginning of each retained change table, not at the current LSN |
| `max_parallel_snapshot_tables` | int | `1` | no | Number of tables snapshotted concurrently |
| `snapshot_max_batch_size` | int | `1000` | no | Max rows per batch during snapshot |
| `include` | array of strings | — | yes | **Unanchored** regular expressions matched against `schema.tablename`. Use `^...$` to match exactly one table (e.g. `^dbo\.orders$`); without anchors, `dbo\.orders` also matches `dbo.orders_archive`. |
| `exclude` | array of strings | — | no | **Unanchored** regular expressions for tables to exclude. Applied after `include`. |
| `checkpoint_cache` | string | — | no | Name of a Connect cache resource to store the LSN checkpoint. If omitted, the built-in SQL Server cache is used |
| `checkpoint_cache_table_name` | string | `rpcn.CdcCheckpointCache` | no | Table for the built-in SQL Server checkpoint cache (schema.table format) |
| `checkpoint_cache_connection_string` | string | — | no | Optional separate DSN for the checkpoint cache table (useful when writing checkpoints to a read replica or separate DB) |
| `checkpoint_cache_key` | string | `microsoft_sql_server_cdc` | no | Cache key used only with an **external** `checkpoint_cache` resource. Ignored by the built-in SQL Server cache (which always uses the fixed key `max_lsn`). |
| `checkpoint_limit` | int | `1024` | no | Max in-flight messages. Higher values allow more output parallelism; LSN is not committed until all messages under it are acked |
| `stream_backoff_interval` | duration | `5s` | no | Wait between change-table passes. Costs throughput on high-traffic tables — consider `500ms` there |
| `auto_replay_nacks` | bool | `true` | no | Automatically replay rejected messages. Set `false` to drop nacked messages and improve memory efficiency on high-throughput pipelines |
| `batching` | object | — | no | Batching policy: `count`, `byte_size`, `period`, `check`, `processors`. If all fields are zero/unset (a no-op policy), the input defaults to `count: 1` (one message per batch). Set `count` or `period` to actually batch. |

See [config-reference.md](references/config-reference.md) for full per-field detail including validation rules for `checkpoint_cache_table_name`.

## Emitted Message Shape

Each message body is a JSON object containing the changed row's column values. System columns (`__$start_lsn`, `__$operation`, etc.) are stripped; only user-defined columns are present.

```json
{
  "id": 42,
  "customer_name": "Acme Corp",
  "amount": "199.99",
  "created_at": "2024-03-15T10:30:00Z"
}
```

Metadata fields set on change messages:

| Metadata key | Value | Present on |
|---|---|---|
| `database_schema` | SQL Server schema of the source table (e.g. `dbo`) | all messages |
| `schema` | Table schema in Benthos common schema format (compatible with `parquet_encode`) | all messages |
| `table` | Table name (e.g. `orders`) | all messages |
| `operation` | One of: `read`, `insert`, `update_before`, `update_after`, `delete` | all messages |
| `lsn` | Raw varbinary(10) LSN bytes (set via `string(m.LSN)`) — binary, not a printable hex string | streamed changes only (insert/update/delete); **absent on snapshot `read` rows** |

The `lsn` metadata is the raw binary representation of the SQL Server LSN, not the `0x…` hex form you see in log output (which uses `.String()`). To use it in a Bloblang expression or Kafka header as a readable value, hex-encode it first, for example:

```yaml
pipeline:
  processors:
    - mutation: |
        root = this
        root._lsn_hex = meta("lsn").encode("hex")
```

Snapshot (`operation: read`) rows have **no `lsn` metadata key** — they are built with `LSN: nil` in the snapshot phase and are not individually checkpointed.

### Operation Types

| Value | When emitted |
|---|---|
| `read` | Initial snapshot rows (only when `stream_snapshot: true`) |
| `insert` | A new row was inserted |
| `delete` | A row was deleted |
| `update_before` | The row state before an update |
| `update_after` | The row state after an update |

Updates produce **two** consecutive messages: `update_before` followed by `update_after` with the same LSN. Use a Bloblang processor or filter to keep only one if needed.

## Snapshot Behaviour

When `stream_snapshot: true` and no prior LSN checkpoint exists, the pipeline:

1. Captures the current max LSN via `sys.fn_cdc_get_max_lsn()`.
2. Snapshots each included table in parallel (up to `max_parallel_snapshot_tables`) using `SNAPSHOT` isolation transactions and keyset pagination.
3. Emits all existing rows with `operation: read`.
4. Stores the captured LSN as the checkpoint.
5. Begins streaming from that LSN in the change tables.

If a checkpoint already exists (restart), the snapshot phase is skipped regardless of `stream_snapshot`.

**`stream_snapshot: false` is not "start from now."** With no snapshot and no checkpoint, the first run starts at the beginning of each table's existing **change table** — every change SQL Server's CDC capture and cleanup jobs still retain (three days by default) is replayed. To truly start from the present on a table that already holds change history, disable and re-enable CDC on that table immediately before starting the pipeline so its change table starts empty.

## Performance and Throughput

This input does **not** read the transaction log directly. SQL Server's CDC capture job scans the log asynchronously and publishes rows into per-table change tables, which this input polls. Consequences worth designing around:

- **The capture job is the ceiling**, shared by every CDC consumer on that database. Tuning the pipeline cannot exceed it.
- **Delivery is inherently bursty.** Short idle periods followed by large batches are normal under heavy write load — that is the capture job's publication cadence, not a fault in the input.
- **Storage bandwidth, not CPU, is usually the limit.** CDC multiplies physical write volume several times over (base table, transaction log, change tables, checkpoints). The input itself needs very little CPU to keep pace with the capture job.
- **`stream_backoff_interval` (default `5s`) costs throughput on busy tables**, since the input idles for the full interval between passes. Lower it toward `500ms` for high-traffic tables; raise it for low-traffic ones to cut query load.

Operational rules:

- `TRUNCATE TABLE` is **rejected** on a CDC-enabled table. To clear one: disable CDC on the table, truncate, re-enable.
- On **AWS RDS**, enable CDC with `msdb.dbo.rds_cdc_enable_db` — `sys.sp_cdc_enable_db` requires sysadmin, which RDS does not grant.
- **Never stop the CDC capture job** (`cdc.<database>_capture`). While it is stopped, nothing is published to the change tables, so this input reads nothing **and reports no error** — a silent stall, not a visible failure.

## Checkpointing

By default, the pipeline auto-creates a stored procedure and then a table under the `rpcn` schema in the source database (in this order):

1. Stored procedure: `rpcn.CdcCheckpointCacheUpdate` (upsert, created first via `CREATE OR ALTER PROCEDURE`)
2. Table: `rpcn.CdcCheckpointCache` (columns: `cache_key varchar(7)`, `cache_val varchar(100)`, created if absent)

The `rpcn` schema **must already exist** before starting the pipeline — the pipeline creates the table and procedure but not the schema. Grant the Connect user `CREATE TABLE` and `CREATE PROCEDURE` on this schema.

To use an external Connect cache resource instead:

```yaml
cache_resources:
  - label: my_redis_cache
    redis:
      url: redis://localhost:6379

input:
  microsoft_sql_server_cdc:
    connection_string: "sqlserver://user:pass@host/MyDB"
    checkpoint_cache: my_redis_cache
    checkpoint_cache_key: mssql_orders_cdc
    include:
      - "dbo\\.orders"
```

## Column Type Mapping

The connector has **two distinct code paths** with different type handling: the snapshot mapper (`snapshot.go prepSnapshotScannerAndMappers`) and the stream mapper (`stream.go mapScannedValue`). The types below reflect what is emitted into the message body; the `schema` metadata uses Benthos common schema type names independent of the path.

### Snapshot path (operation: read)

| SQL Server type | Emitted body value |
|---|---|
| `DECIMAL`, `NUMERIC` | canonical decimal string with the column's precision/scale (e.g. `"199.990"`); raw text if precision is unknown |
| `MONEY`, `SMALLMONEY` | canonical decimal string |
| `DATE`, `TIME`, `DATETIME`, `DATETIME2`, `SMALLDATETIME`, `DATETIMEOFFSET` | `time.Time` (marshals as RFC3339 in JSON) |
| `TINYINT`, `SMALLINT`, `MEDIUMINT`, `INT`, `BIGINT`, `YEAR` | Go `int` (JSON number) |
| `FLOAT`, `DOUBLE` | `float64` (JSON number) |
| `BINARY`, `VARBINARY`, `VARBINARY(MAX)`, `IMAGE` | `[]byte` (base64 in JSON) |
| `JSON` | parsed JSON value (the string is `json.Unmarshal`-ed into the body) |
| All other types — including `BIT`, `REAL`, `CHAR`, `VARCHAR`, `NVARCHAR`, `UNIQUEIDENTIFIER`, `XML`, etc. | `string` (fall-through to `sql.Null[string]`) |

### Stream path (operation: insert / update_before / update_after / delete)

The stream mapper only special-cases decimal/money types; all other values come directly from the go-mssqldb driver:

| SQL Server type | Emitted body value |
|---|---|
| `DECIMAL`, `NUMERIC`, `MONEY`, `SMALLMONEY` | canonical decimal string |
| `BIT` | driver-native value (typically `bool`) |
| `TINYINT`, `SMALLINT`, `INT`, `BIGINT` | driver-native integer |
| `FLOAT`, `REAL` | driver-native float |
| `DATETIME`, `DATETIME2`, etc. | driver-native `time.Time` |
| `BINARY`, `VARBINARY`, etc. | `[]byte` (base64 in JSON) |
| `CHAR`, `VARCHAR`, `NVARCHAR`, `UNIQUEIDENTIFIER`, `XML`, etc. | `string` |

> The snapshot and stream paths use different mappers, so the emitted type for a given column may differ between an initial snapshot row and a subsequent CDC change event. Treat downstream consumers for non-decimal types defensively (accept both string and native forms).

## Per-Table Topic Routing

Use Bloblang in the output `topic` field or a `switch` processor:

```yaml
output:
  kafka_franz:
    seed_brokers:
      - localhost:9092
    topic: '${! "sqlserver." + meta("database_schema") + "." + meta("table") }'
```

Or use a `switch` output for explicit routing:

```yaml
output:
  switch:
    cases:
      - check: 'meta("table") == "orders"'
        output:
          kafka_franz:
            seed_brokers: [localhost:9092]
            topic: orders-cdc
      - check: 'meta("table") == "customers"'
        output:
          kafka_franz:
            seed_brokers: [localhost:9092]
            topic: customers-cdc
```

## License

`microsoft_sql_server_cdc` is an Enterprise-licensed Redpanda Connect connector. Attempting to run without a license produces an error at startup; after the 30-day evaluation period, enterprise connectors are blocked unless you upgrade.

Apply the Connect license in any of these ways (grounded in `connect/internal/cli/flags_redpanda.go` and `connect/internal/license/service.go`):

- `--redpanda-license <string>` flag on `rpk connect run` / `rpk connect dry-run` (takes precedence).
- `REDPANDA_LICENSE` env var (inline license string).
- `REDPANDA_LICENSE_FILEPATH` env var (path to a license file).
- Default file `/etc/redpanda/redpanda.license` (auto-applied if present and none of the above are set).

This license authorizes the **connector**. The Redpanda **cluster** you write into needs its own valid license to use destination-side enterprise features (Iceberg, Schema ID Validation, Tiered Storage, Cloud Topics).

## Destination Enterprise Features

When the CDC pipeline writes into Redpanda, these Enterprise (cluster-licensed) features pair directly with it. Full nested keys are in [enterprise-features.md](references/enterprise-features.md).

- **Iceberg Topics** (lakehouse landing of CDC events): cluster `iceberg_enabled`; per-topic `redpanda.iceberg.mode` (`key_value` / `value_schema_id_prefix` / `value_schema_latest` / `disabled`), `redpanda.iceberg.delete`, `redpanda.iceberg.partition.spec`, `redpanda.iceberg.target.lag.ms`, `redpanda.iceberg.invalid.record.action` (`drop` / `dlq_table`). Requires Tiered Storage.
- **Server-side Schema ID Validation** (reject unregistered schema IDs on destination topics): cluster `enable_schema_id_validation` (`none` / `redpanda` / `compat`); per-topic `redpanda.value.schema.id.validation`, `redpanda.value.subject.name.strategy`, and the key equivalents.
- **Tiered Storage** (long retention of CDC topics; prerequisite for Iceberg): cluster `cloud_storage_enabled`; per-topic `redpanda.remote.write`, `redpanda.remote.read`, `redpanda.remote.delete`.
- **Cloud Topics** (object-storage-native topics): per-topic `redpanda.cloud_topic.enabled` / `redpanda.storage.mode`.

## Reference Directory

- [config-reference.md](references/config-reference.md): Complete field-by-field reference for every `microsoft_sql_server_cdc` config option, types, defaults, validation rules, and the checkpoint cache internals.
- [setup-sqlserver.md](references/setup-sqlserver.md): T-SQL walkthrough for enabling SQL Server CDC — `sys.sp_cdc_enable_db`, `sys.sp_cdc_enable_table`, SQL Server Agent requirements, capture instance naming, permissions, and Azure SQL specifics.
- [pipeline-and-output.md](references/pipeline-and-output.md): Full runnable pipelines, metadata shape and routing, snapshot-then-stream lifecycle, checkpointing semantics, and restart behaviour.
- [enterprise-features.md](references/enterprise-features.md): Redpanda Enterprise features for CDC pipelines — Connect license application (flag, env vars, default file path), Iceberg Topics (`iceberg_enabled` + all `redpanda.iceberg.*` topic keys and modes), server-side Schema ID Validation (`enable_schema_id_validation` + per-topic validation/subject-name-strategy keys), Tiered Storage (`cloud_storage_enabled`, `redpanda.remote.*`), Cloud Topics (`redpanda.cloud_topic.enabled`), and license-expiration behavior. Notes which keys require an Enterprise license.