Introduction to Microsoft SQL Server data transfers

You can load data from Microsoft SQL Server to BigQuery by using the BigQuery Data Transfer Service for Microsoft SQL Server connector. The connector supports Microsoft SQL Server instances hosted in your on-premises environment, Cloud SQL, and other public cloud providers such as Amazon Web Services (AWS) and Microsoft Azure. This document provides an overview of the data ingestion options for your Microsoft SQL Server transfer and information about data type mapping and pricing.

To learn how to schedule a Microsoft SQL Server transfer, see Load Microsoft SQL Server data into BigQuery.

Data ingestion options

The following sections provide information about the data ingestion options when you set up a Microsoft SQL Server data transfer.

TLS configuration

The Microsoft SQL Server connector supports the configuration for transport level security (TLS) to encrypt your data transfers into BigQuery. The Microsoft SQL Server connector supports the following TLS configurations:

  • The Encrypt data, and verify CA and hostname mode. This mode performs a full validation of the server using TLS over the TCPS protocol. It encrypts all data in transit and verifies that the database server's certificate is signed by a trusted certificate authority (CA). This mode also checks that the hostname you're connecting to exactly matches the Common Name (CN) or a Subject Alternative Name (SAN) on the server's certificate. This mode prevents attackers from using a valid certificate for a different domain to impersonate your database server.

    If your hostname does not match the certificate CN or SAN, the connection fails. You must configure a DNS resolution to match the certificate or use a different security mode. Use this mode for the most secure option to prevent person-in-the-middle (PITM) attacks.

  • The Encrypt data, and verify CA only mode. This mode encrypts all data using TLS over the TCPS protocol and verifies that the server's certificate is signed by a CA that the client trusts. However, this mode does not verify the server's hostname. This mode successfully connects as long as the certificate is valid and issued by a trusted CA, regardless of whether the hostname in the certificate matches the hostname you are connecting to.

    Use this mode if you want to ensure that you are connecting to a server whose certificate is signed by a trusted CA, but the hostname is not verifiable or you don't have control over the hostname configuration.

  • The Encryption only mode. This mode encrypts all data transferred between the client and the server. It does not perform any certificate or hostname validation.

    This mode provides some level of security by protecting data in transit, but it can be vulnerable to PITM attacks.

    Use this mode if you need to ensure all data is encrypted but can't or don't want to verify the server's identity. We recommend using this mode when working with private VPCs.

  • The No encryption or verification mode. This mode does not encrypt any data and does not perform any certificate or hostname verification. All data is sent as plain text.

    We don't recommend using this mode in an environment where sensitive data is handled. We only recommend using this mode for testing purposes on an isolated network where security is not a concern.

Trusted Server Certificate (PEM)

If you are using either the Encrypt data, and verify CA and hostname mode or the Encrypt data, and verify CA mode, then you can also provide one or more PEM-encoded certificates. These certificates are required in some scenarios where the BigQuery Data Transfer Service needs to verify the identity of your database server during the TLS connection:

  • If you are using a certificate signed by a private CA within your organization or a self-signed certificate, you must provide the full certificate chain or the single self-signed certificate. This is required for certificates issued by internal CAs of managed cloud provider services, such as the Amazon Relational Database Service (RDS).
  • If your database server certificate is signed by a public CA (for example, Let's Encrypt, DigiCert, or GlobalSign), you don't need to provide a certificate. The root certificates for these public CAs are pre-installed and trusted by the BigQuery Data Transfer Service.

You can specify PEM-encoded certificates in the Trusted PEM Certificate field in the transfer configuration, with the following requirements:

  • The certificate must be a valid PEM-encoded certificate chain.
  • The certificate must be entirely correct. Any missing certificates in the chain or incorrect content causes the TLS connection to fail.
  • For a single certificate, you can provide a single, self-signed certificate from the database server.
  • For a full certificate chain issued by a private CA, you must provide the full chain of trust. This includes the certificate from the database server and any intermediate and root CA certificates.

Full or incremental transfers

You can specify how data is loaded into BigQuery by selecting either the Full or Incremental write preference in the transfer configuration when you set up a Microsoft SQL Server transfer. Incremental transfers are supported in preview.

You can configure a full data transfer to transfer all data from your Microsoft SQL Server datasets with each data transfer.

Alternatively, you can configure an incremental data transfer (Preview) to only transfer data that was changed since the last data transfer, instead of loading the entire dataset with each data transfer. If you select Incremental for your data transfer, you must specify either the Append or Upsert write modes to define how data is written to BigQuery during an incremental data transfer. The following sections describe the available write modes.

Append write mode

The append write mode only inserts new rows to your destination table. This option strictly appends transferred data without checking for existing records, so this mode can potentially cause data duplication in the destination table.

When you select the append mode, you must select a watermark column. A watermark column is required for the Microsoft SQL Server connector to track changes in the source table.

For Microsoft SQL Server transfers, we recommend selecting a column that is only updated when the record was created, and won't change with subsequent updates—for example, the CREATED_AT column.

Upsert write mode

The upsert write mode either updates a row or inserts a new row in your destination table by checking for a primary key. You can specify a primary key to let the Microsoft SQL Server connector determine what changes are needed to keep your destination table up to date with your source table. If the specified primary key is present in the destination BigQuery table during a data transfer, then the Microsoft SQL Server connector updates that row with new data from the source table. If a primary key is not present during a data transfer, then the Microsoft SQL Server connector inserts a new row.

When you select the upsert mode, you must select a watermark column and a primary key:

  • A watermark column is required for the Microsoft SQL Server connector to track changes in the source table.
    • Select a watermark column that updates every time a row is modified. We recommend columns similar to the UPDATED_AT or LAST_MODIFIED column.

  • The primary key can be one or more columns on your table that are required for the Microsoft SQL Server connector to determine if it needs to insert or update a row.

    Select columns that contain non-null values that are unique across all rows of the table. We recommend columns that include system-generated identifiers, unique reference codes (for example, auto-incrementing IDs), or immutable time-based sequence IDs.

    To prevent potential data loss or data corruption, the primary key columns that you select must have unique values. If you have doubts about the uniqueness of your chosen primary key column, then we recommend that you use the append write mode instead.

Incremental ingestion behavior

When you make changes to the table schema in your data source, incremental data transfers from those tables are reflected in BigQuery in the following ways:

Changes to data source Incremental ingestion behavior
Adding a new column A new column is added to the destination BigQuery table. Any previous records for this column will have null values.
Deleting a column The deleted column remains in the destination BigQuery table. New entries to this deleted column are populated with null values.
Changing the data type in a column The connector only supports data type conversions that are supported by the ALTER COLUMN DDL statement. Any other data type conversion causes the data transfer to fail.

If you encounter any issues, we recommend creating a new transfer configuration.
Renaming a column The original column remains in the destination BigQuery table as is, while a new column is added to the destination table with the updated name.

Data type mapping

The following table maps Microsoft SQL Server data types to the corresponding BigQuery data types:

Microsoft SQL Server data type BigQuery data type Updated BigQuery data type
tinyint INTEGER
smallint INTEGER
int INTEGER
bigint BIGNUMERIC
bit BOOLEAN
decimal BIGNUMERIC
numeric NUMERIC
money BIGNUMERIC
smallmoney BIGNUMERIC
float FLOAT
real FLOAT
date DATE
time TIME
datetime2 TIMESTAMP DATETIME
datetimeoffset TIMESTAMP
datetime TIMESTAMP DATETIME
smalldatetime TIMESTAMP DATETIME
char STRING
varchar STRING
text STRING
nchar STRING
nvarchar STRING
ntext STRING
binary BYTES
varbinary BYTES
image BYTES
geography STRING
geometry STRING
hierarchyid BYTES
rowversion BYTES
sql_variant BYTES
uniqueidentifier STRING
xml STRING
json STRING
vector STRING

The json and vector data types are only supported in Azure.

The JSON data type is supported in Azure SQL databases and Azure SQL managed instances configured with the always-up-to-date update policy. The JSON data type is not supported in Azure SQL managed instances configured with the Microsoft SQL Server 2022 update policy.

Microsoft SQL Server stores JSON as NVARCHAR(MAX), and not as a JSON type. We recommend that you use CHECK (ISJSON(json_col) = 1) for validation, and JSON_VALUE() for querying.

The Microsoft SQL Server doesn't have vector support for the vector data type. We recommend that you store vectors as JSON arrays in NVARCHAR(MAX) and use JSON_VALUE() for extraction, with manual FLOAT calculations for similarity.

Pricing

There is no cost to transfer Microsoft SQL Server data into BigQuery while this feature is in Preview.

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