![]() ![]() Prior to creating the configuration file, you must run createKmsKey.sh, and then use the encryptValue.sh script to generate the base64 encoded encrypted configuration values. If your use case does not allow that KMS needs to be used in order to pass secrets securely.Īll passwords and access keys for reading and writing from Amazon S3 are encrypted using the Customer Master Key for the utility. ℹ️ Ideally copy statements use a Role ARN and cluster access is performed using temporary cluster credentials (see above). ![]() Sensitive configuration parameters like passwords and access keys More info on how this works is available in the AWS Redshift documentation. If no password is specified then the utility will try to use the GetClusterCredentials-API in order to get temporary credentials for the specified user.įor this the application needs access to credentials itself which would allow to get these cluster credentials. Using temporary cluster credentials (password) An example configuration to help you get started can be found in the example configuration file. To use Amazon S3, prefix the file location parameter with 's3://'. ![]() The utility is configured using a json configuration file, which can be stored on the local filesystem or on Amazon S3. Next the file names for a table will start with. A date string of format %Y-%m-%d_%H:%M:%S will be generated per execution and used as first part of the path to the object. We also add the following options on UNLOAD/COPY to ensure effective and accurate migration of data between systems:ĭata is exported to S3 to the configuration location. Data Staging Formatĭata is stored on Amazon S3 at the configured location as AES 256 encrypted CSV files, gzipped for efficiency. ![]() ℹ️ In order to have Python do this the configuration file should have "kmsGeneratedKey": "True" in the "s3Staging" section as the default will use KMS as described above. For newer versions of Python the secrets module will be used. If you have an version of Python that is older than 3.6 then you will need to install pycrypto, this can be done using pip install pycrypto. Since the key is used as a temporary resource it can be a random 256 bit sequence therefore it can be generated locally. The Unload/Copy utility then gets an AES_256 Master Symmetric Key from KMS which is used to encrypt data on S3, Let Python generate a temporary random client key In order to get this customer-managed key the utility provides 2 possibilities: Use KMS to generate a temporary client keyĪ Customer Master Key is created with the createKmsKey.sh script, and an alias to this key named 'alias/RedshiftUnloadCopyUtility' is used for all references. At no time is this key persisted to disk, it is lost when the program terminates. For this a 256-bit AES key is used for unloading the data in a secure manner to S3 and then used again by the target cluster to decrypt files from S3 and load them. The Unload/Copy Utility instructs Redshift to use client-side encryption with a customer-managed key (CSE-CMK). This utility can be used as part of an ongoing scheduled activity, for instance run as part of a Data Pipeline Shell Activity ( ) but it can also be used standalone for creating a copy of a table into another cluster. It then automatically imports the data in FULL into the configured Redshift Cluster, and will cleanup S3 if required. It exports data from a source cluster to a location on S3, and all data is encrypted with Amazon Key Management Service. Of course, this workaround assumes that no other parameters would be bound outside of the UNLOAD's query inside the ( ).The Amazon Redshift Unload/Copy Utility helps you to migrate data between Redshift Clusters or Databases. We will look at some of the frequently used options in this article. This command provides many options to format the exported data as well as specifying the schema of the data being exported. The syntax of the Unload command is as shown below. Subsequently, if the sub-query executed successfully without any errors or exceptions, we could assume that the sub-query is safe, thus allowing us to wrap the sub-query back into the UNLOAD parent statement, but this time replacing the bind parameters with actual user-supplied parameters (simply concatenating them), which have now been validated in the previously run SELECT query. The primary method natively supports by AWS Redshift is the Unload command to export data. This would let us use Redshift's prepared statement support (which is indeed supported for SELECT queries) to bind and validate the potentially risky, user-supplied parameters first. While trying to devise a workaround for this, a colleague of mine has thought up a workaround: instead of binding the parameters into the UNLOAD query itself (which is not supported by Redshift), we could simply bind them to the inner sub-query inside the UNLOAD's ( ) first (which happens to be a SELECT query - which is probably the most common subquery used within UNLOAD statements by most Redshift users, I'd say) and run this sub-query first, perhaps with a LIMIT 1 or 1=0 condition, to limit its running time. Thanks for your quick reply, and thanks for re-raising this issue with the Redshift server team. ![]()
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