snowflake-data-exchange-agent 1.12.0

Data exchange agent for migrations and validation

Snowflake Data Exchange Agent

The Data Exchange Agent is the Worker component of the Cloud Data Migration solution. It connects to source databases (SQL Server, Amazon Redshift, Teradata, Oracle), extracts data, and uploads it to Snowflake stages for ingestion by the Data Migration Orchestrator (snowflake-data-migration-orchestrator).

The same worker process also executes Cloud Data Validation tasks (data_validation) when the orchestrator schedules them. That path relies on the optional snowflake-data-validation package being installed in the worker environment (see the orchestrator documentation for creating validation workflows and JSON configuration).

Installation

pip install snowflake-data-exchange-agent

For Teradata sources using the native teradatasql driver (recommended over ODBC when you can use it), install the optional extra:

pip install snowflake-data-exchange-agent[teradata]

Python Version: 3.11 or higher

Usage

The agent provides two subcommands: run (default) and test.

# Start with a configuration file
data-exchange-agent run -c <configuration-file-path>

# Start with default configuration.toml in current directory
data-exchange-agent run

# Omitting the subcommand defaults to 'run' (backward compatible)
data-exchange-agent -c <configuration-file-path>

# Custom parallelism and port
data-exchange-agent run --max-parallel-tasks 8 --port 8080

# Task-handling only, without the HTTP server (for multi-worker setups)
data-exchange-agent run --no-server

# Custom base directory for exported files (overrides config)
data-exchange-agent run --local-results-directory /mnt/dea-exports

# Debug mode
data-exchange-agent run --debug --port 5001

# Test all configured connections (executes SELECT 1)
data-exchange-agent test -c <configuration-file-path>

Run Command Options

Flag	Short	Default	Description
--config	-c	configuration.toml	Path to the TOML configuration file.
--max-parallel-tasks	-w	from config	Maximum number of parallel tasks.
--interval	-i	from config	Interval (seconds) between task fetch attempts.
--host		0.0.0.0	Host to bind the HTTP server to.
--port	-p	5001	Port to bind the HTTP server to.
--no-server		off	Run task handling only, without starting the HTTP server.
--local-results-directory		from config	Base directory for exported files before upload.
--debug	-d	off	Enable debug mode.

Worker Configuration

The Worker configuration file uses TOML format.

Section	Property	Type	Description
Top Level	selected_task_source	String	Currently should always be set to "snowflake_stored_procedure".
[application]	max_parallel_tasks	Integer	Maximum number of tasks the worker will process in parallel (using threads).
[application]	task_fetch_interval	Integer	Interval (in seconds) between attempts to fetch new tasks from the Orchestrator.
[application]	lease_refresh_interval	Integer	Optional. Interval (in seconds) between task lease renewals. Default 120.
[application]	snowflake_database_for_metadata	String	Optional. Database where the orchestrator deployed the task queue (default SNOWCONVERT_AI). Must match the orchestrator's CUSTOM_SNOWFLAKE_DATABASE_FOR_METADATA if you override it there.
[application]	snowflake_schema_for_data_migration_metadata	String	Optional. Schema for PULL_TASKS / COMPLETE_TASK / FAIL_TASK (default DATA_MIGRATION). Must match the orchestrator's CUSTOM_SNOWFLAKE_SCHEMA_FOR_DATA_MIGRATION_METADATA if overridden.
[application]	local_results_directory	String	Optional. Base directory where each task's exported Parquet or CSV files are written before upload. Each run uses a subfolder task_<id>/<timestamp>. After a successful upload, that timestamp folder and the task_<id> parent (if empty) are removed so stale empty directories do not accumulate. When unset, files go under ~/.data_exchange_agent/result_data. Tilde (~) and relative paths are expanded at load time.
[connections.source.*]		Object	Configuration for source system connections. The Worker typically requires an ODBC driver. See examples below.
[connections.target.snowflake_connection_name]	connection_name	String	The name of the connection entry in the ~/.snowflake/config.toml file to use.

When selected_task_source is snowflake_stored_procedure, the worker issues CALL statements against the task-queue using application.snowflake_database_for_metadata and application.snowflake_schema_for_data_migration_metadata. These settings are independent of Snowflake connection session defaults (SNOWFLAKE_DATABASE, SNOWFLAKE_SCHEMA in the connection profile).

Example: SQL Server (Standard Authentication)

[connections.source.sqlserver]
username = "username"
password = "password"
database = "database_name"
host = "127.0.0.1"
port = 1433

Example: Amazon Redshift (IAM Authentication)

[connections.source.redshift]
username = "demo-user"
database = "demo_db"
auth_method = "iam-provisioned-cluster"
cluster_id = "my-aws-cluster"
region = "us-west-2"
access_key_id = "your-access-key-id"
secret_access_key = "your-secret-access-key"

Example: Amazon Redshift (Standard Authentication)

[connections.source.redshift]
username = "myuser"
password = "mypassword"
database = "mydatabase"
host = "my-cluster.abcdef123456.us-west-2.redshift.amazonaws.com"
port = 5439
auth_method = "standard"

Example: PostgreSQL (ODBC)

Use this block when the orchestrator schedules Cloud Data Validation (or migration) against a PostgreSQL source. Install a PostgreSQL ODBC driver on the worker host; see pyodbc.drivers() for the exact odbc_driver string if you need to pin one.

[connections.source.postgresql]
username = "my_user"
password = "my_password"
database = "my_database"
host = "postgres.example.com"
port = 5432
# odbc_driver = "PostgreSQL Unicode"  # optional

Example: Teradata

The agent supports two Teradata drivers and automatically selects the best one available:

1. teradatasql (preferred) -- Pure Python driver. No OS-level ODBC installation required. Install with pip install teradatasql.
2. ODBC fallback -- If teradatasql is not installed, the agent falls back to pyodbc with the Teradata ODBC driver. Set odbc_driver to the exact name returned by pyodbc.drivers().

When teradatasql is available, odbc_driver is ignored and no ODBC driver needs to be installed on the host. Use dbc_name when your Teradata COP / TDPID alias differs from host.

Older configs used driver_name for the ODBC driver label; that key still works but is deprecated in favor of odbc_driver.

[connections.source.teradata]
host = "your-teradata-host.example.com"
port = 1025
database = "tpcds"
username = "your_username"
password = "your_password"
# odbc_driver = "Teradata Database ODBC Driver 17.20"  # only needed for ODBC fallback
# dbc_name = "TDPID_ALIAS"  # optional; defaults to host
# authentication = "LDAP"  # optional ODBC AuthMech when using pyodbc (e.g. TD2, LDAP, KRB5)

# Optional: WRITE_NOS configuration for direct export to cloud object storage
# (S3, Azure Blob, or GCS). The orchestrator's `extraction.strategy` must be
# set to `"write_nos"` for the worker to use these settings.
#
# `write_nos_location_scheme` is one of `/s3/`, `/az/`, or `/gs/`.
# Provide credentials in exactly one of the three modes below.
#
# write_nos_location_scheme    = "/az/"
# write_nos_location_host      = "myaccount.blob.core.windows.net"
# write_nos_location_container = "td-nos-exports"
#
# Mode 1 - Function mapping (recommended; credentials live on Teradata):
# write_nos_function_mapping = "nos_util.WRITE_NOS_FM"
#
# Mode 2 - Named AUTHORIZATION object (credentials live on Teradata):
# write_nos_authorization_name = "nos_util.DefAuth_Write"
#
# Mode 3 - Inline credentials (embedded in SQL; redacted in logs):
# write_nos_access_id  = "your_account_or_access_key_id"
# write_nos_access_key = "your_secret"
#
# Optional output overrides (defaults shown):
# write_nos_stored_as       = "PARQUET"
# write_nos_compression     = "SNAPPY"
# write_nos_max_object_size = "16MB"
# write_nos_overwrite       = "TRUE"
#
# Optional: TPT (Teradata Parallel Transporter). Use when the orchestrator sets
# `extraction.strategy` to `"tpt"`. Requires TTU (`tbuild`) on PATH or set
# `TPT_TBUILD_EXECUTABLE` to its absolute path. Intermediate delimited files and
# job scripts use `tpt_output_directory` / `tpt_log_directory` on the worker.
#
# tpt_output_directory = "/var/dea/tpt"
# tpt_log_directory    = "/var/log/dea/tpt"
# tpt_delimiter        = "|"
# tpt_max_sessions     = 4
# tpt_charset          = "UTF8"

Example: Oracle (ODBC)

Install an Oracle Instant Client and the matching ODBC driver on the worker host. Use pyodbc.drivers() to find the exact odbc_driver string if the default label below does not match your installation.

Basic mode uses EZ Connect: service name in database, host/port for the listener.

[connections.source.oracle]
oracle_connection_mode = "basic"
username = "my_user"
password = "my_password"
database = "ORCLPDB1"
host = "oracle.example.com"
port = 1521
odbc_driver = "Oracle in instantclient_21_1"
auto_detect_driver = false

TNS alias mode sets DBQ to a name from tnsnames.ora. When tns_admin is set, TNS_ADMIN is applied only for the duration of pyodbc.connect so the driver can resolve the alias.

[connections.source.oracle]
oracle_connection_mode = "tns_alias"
tns_name = "MY_ORACLE_SERVICE"
username = "my_user"
password = "my_password"
odbc_driver = "Oracle in instantclient_21_1"
auto_detect_driver = false
tns_admin = "/path/to/tns"

Connect descriptor mode passes a full net descriptor as DBQ (for advanced routing, LDAP alternatives, or paste-in from tnsnames.ora).

[connections.source.oracle]
oracle_connection_mode = "connect_descriptor"
connect_descriptor = "(DESCRIPTION=(ADDRESS=(PROTOCOL=TCP)(HOST=oracle.example.com)(PORT=1521))(CONNECT_DATA=(SERVICE_NAME=ORCLPDB1)))"
username = "my_user"
password = "my_password"
odbc_driver = "Oracle in instantclient_21_1"
auto_detect_driver = false

Optional wallet_directory / wallet_password append common Oracle ODBC wallet attributes; TLS naming and LDAP directory options vary by driver — add driver-specific keys via extra options or extend configuration as needed.

Note: Only one source connection is needed. The Snowflake target connection should point to a valid entry in your ~/.snowflake/config.toml.

ODBC Driver Auto-Detection

The agent automatically detects the best available ODBC driver for SQL Server connections. If no odbc_driver is specified in the configuration, it will prefer the newest available driver (ODBC Driver 18 > 17 > 13 > 11). If a specific driver is requested but not found, it falls back to the best available driver with a warning.

To manually specify a driver:

[connections.source.sqlserver]
odbc_driver = "ODBC Driver 17 for SQL Server"

ODBC Encryption (SQL Server)

The encrypt and trust_server_certificate parameters are optional. By default, they are omitted from the connection string, allowing the ODBC driver to use its default behavior:

• ODBC Driver 17 and below: Encryption is disabled by default.
• ODBC Driver 18 and above: Encryption is mandatory by default.

[connections.source.sqlserver]
username = "sa"
password = "mypassword"
database = "mydb"
host = "my-server.example.com"
port = 1433
encrypt = true
trust_server_certificate = false

For development environments or SQL Servers without encryption support, either omit the encryption parameters or set encrypt = false.

Query Tagging

The Worker automatically sets Snowflake's QUERY_TAG session parameter on every query it submits. Tags are compact JSON strings containing identifiers such as the workflow ID, task ID, and worker version. You can use these tags to filter and attribute Worker queries in QUERY_HISTORY:

SELECT query_text, query_tag, start_time
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE TRY_PARSE_JSON(query_tag):DMVF_WORKFLOW_ID IS NOT NULL
ORDER BY start_time DESC;

Tag key	Present on	Description
DMVF_VERSION	Infrastructure queries	Worker package version.
DMVF_WORKFLOW_ID	Task-processing queries	Workflow that originated the task.
DMVF_TASK_ID	Task-processing queries	Individual task identifier.
DMVF_WORKER_VERSION	Task-processing queries	Worker package version.

Changelog

v1.12.0

New features

• Added view validation support to the Cloud Data Validation pipeline.
• Added Oracle ODBC source support.
• Added Teradata type mappings and Snowflake target fully-qualified-name helpers.
• Added Teradata object_type query in the shared dispatcher.
• Added Teradata to the data-migration-orchestrator workflow.
• Added Teradata ODBC source support.
• Added early-stopping support for L3 row-hashing validation.
• Added support for custom metrics and templates in Cloud Data Validation.
• Added Oracle Data Validation foundation with L1 schema validation.
• Added L3 row and cell MD5 validation for Oracle.
• Added Oracle wiring and factory registration in the SDV core.
• Added the teradata optional install extra (pip install snowflake-data-exchange-agent[teradata]).
• Added support for early stopping, hybrid L3, and Snowpipe (breaking change).
• Added Snowflake schema utilities and type-mapping updates for the orchestrator.
• Added TPT and WRITE_NOS data sources for Teradata extraction.
• Added TPT and WRITE_NOS integration in Teradata workflow tasks.
• Added a metrics skill and PostgreSQL metrics templates.
• Added PostgreSQL connector with L0 and L1 validation.
• Added Oracle as a supported Data Validation source across the orchestrator and agent.
• Added L2 and L3 row and cell validation for PostgreSQL.
• Added PostgreSQL support to Cloud Data Validation.

Improvements

• Optimized L3 row-hashing queries.
• Treated wrapped (200010) PULL_TASKS lock-wait error as transient so the worker retries instead of failing.
• Extended Teradata ODBC connection configuration.

Bug fixes

• Made BCP stdout/stderr pump threads daemon=True to prevent worker hangs on shutdown.
• Prevented out-of-memory errors in cell-by-cell and row-hashing comparisons in workers.
• Fixed L3 row-hashing producing false positives.
• Prevented unnecessary shared-cache eviction when the loaded copy already matches the workspace.
• Fixed row-hashing algorithm errors and now surfaces duplicates and missing rows distinctly (breaking change).

v1.11.1

Improvements

• Improved column metrics query performance by consolidating per-column CTEs into a single wide-row query.

Bug fixes

• Fixed aggregate overflow on STDDEV and VARIANCE during data validation by casting SUM/AVG/STDDEV inputs to FLOAT; removed the VARIANCE metric.

v1.11.0

Improvements

• Cast value columns to Utf8 before unpivot and corrected IS_VALID evaluation.
• Vertical partitioning for cell validation on wide tables.

Bug fixes

• Fixed timestamp copy handling for SQL Server BCP loads.
• Fixed duplicate tasks created when evaluating L1 results under race conditions.
• Fixed decimal partition coercion and parallelized L3 validation fixes.

v1.10.0

New features

• Added hybrid row validation mode — two-phase MD5 + cell drilldown.
• Added DEFAULT normalization templates for various data types.

Improvements

• Improved result set snapshots validation.
• Improved Data Validation performance.
• Included thread name and ID in log output for easier troubleshooting.
• Improved the task queue to support a higher number of parallel workers.

Bug fixes

• Fixed SQL compilation memory exhaustion by batching L2 metrics queries for wide tables.
• Fixed an issue with the incremental sync watermark on Redshift.
• Fixed usage of the vectorized scanner.

v1.9.2

Improvements

• Log installed dependency versions and the Python runtime version at startup.

v1.9.1

Improvements

• Cloud data validation tasks read query results in batches instead of loading full result sets into memory.