1 / 2

Autonomous Data Validation in AWS S3 (3)

https://firsteigen.com/aws/

paulwalkner990
Télécharger la présentation

Autonomous Data Validation in AWS S3 (3)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Autonomous Data Validation in AWS S3 AWS S3 has several features to help you manage data quality and compliance. One of these features is autonomous data validation. It works by establishing a data fingerprint for each dataset, and validating it against this fingerprint. It automatically updates rules as the dataset changes, reducing the amount of manual work required to maintain data-quality rules. It can also verify whether a dataset is fresh, includes all relevant fields, and is unique. Rule-based approach to data validation in AWS S3 An S3 object can have one or more attributes. For instance, you might want to tag the object for a specific table or schema. You can define this by using a post-processing rule. The rule can then specify the tags to add to the object. The following example shows how a rule can be used to tag an S3 object. You can also use a rule to control the data integrity of an S3 bucket. This method can be used to detect suspicious data and avoid unnecessary rejections. First, use the includeOpForFullLoad parameter. This parameter controls whether the CDC record is inserted at the source. VISIT HERE When a data validation rule is applied to a S3 object, it will flag any instances where the data is invalid. The problem with using a rule-based approach is that it cannot scale to 100s of data assets. In addition, it is not practical to create rules for thousands of S3 objects. AWS S3 can help you keep track of the lifecycle of your data. By configuring Amazon S3 Lifecycle configuration with a simple set of rules, you can manage the lifecycle of your data and save cloud storage costs. Oracle Autonomous Database connection to Amazon S3 When you want to connect your Oracle Autonomous Database to Amazon S3, there are several ways to do it. You can either make the connection over the public Internet, with Access Control Rules enabled, or you can use a private endpoint in a Virtual Cloud Network (VCN). With a private endpoint, only traffic from the VCN is allowed. This keeps all database traffic off the public Internet.

  2. To create an Autonomous Database connection to Amazon S3, you must have an administrative account in the cloud service. You can use this account to create schemas, objects, and a repository. This repository is installed as part of the schema migration process. You can remove it after you've completed the migration, if you want. To create an Oracle Autonomous Database connection to Amazon S3, you must have an AWS account. Then, you need to create a cloud credential (a user name and password) that will give you access to the Amazon S3 bucket. You can create this credential in AWS Identity access management. Once you have your AWS account setup, you can proceed with setting up Autonomous Database connections to Amazon S3. You should configure the AWS account, roles, and policies, and update the trust relationship. In Autonomous Database, you must also configure the Oracle user ARN. Each Amazon resource name has an identity, and this is how it will authenticate to Autonomous Database. You should also specify a certificate authority, if you use mutual authentication.

More Related