AWS prescriptive-guidance documentation change
Summary
Updated navigation links, fixed URL formatting by removing extra slashes in AWS documentation links, and modified an example filename format (removed backticks and changed 'warehouse_number' to 'wharehouse_number').
Security assessment
Changes involve URL corrections and cosmetic updates without addressing vulnerabilities or security controls. The filename example modification doesn't introduce security content or mitigate risks.
Diff
diff --git a/prescriptive-guidance/latest/automated-pdf-analysis-solution/ingestion-phase.md b/prescriptive-guidance/latest/automated-pdf-analysis-solution/ingestion-phase.md index 5c73b2e6e..01d9a8c7c 100644 --- a//prescriptive-guidance/latest/automated-pdf-analysis-solution/ingestion-phase.md +++ b//prescriptive-guidance/latest/automated-pdf-analysis-solution/ingestion-phase.md @@ -5 +5 @@ -[Documentation](/index.html)[AWS Prescriptive Guidance](https://aws.amazon.com/prescriptive-guidance/)[Designing an automated solution to analyze PDF files on the AWS Cloud](welcome.html) +[Documentation](/index.html)[AWS Prescriptive Guidance](https://aws.amazon.com/prescriptive-guidance/)[Designing an automated solution to analyze PDF files on the AWS Cloud](introduction.html) @@ -11 +11 @@ Best practices for the ingestion phase -Your organization identifies a PDF file type that is continuously generated (for example, a daily operations report), has an identical format, and that you need to automatically and regularly extract data from. To ingest this PDF file, you need an Amazon Simple Storage Service (Amazon S3) bucket and we recommend that you create a dedicated S3 bucket. However, you can also use an existing S3 bucket. For more information about this, see [Creating a bucket](https://docs.aws.amazon.com//AmazonS3/latest/userguide/create-bucket-overview.html) in the Amazon S3 documentation. +Your organization identifies a PDF file type that is continuously generated (for example, a daily operations report), has an identical format, and that you need to automatically and regularly extract data from. To ingest this PDF file, you need an Amazon Simple Storage Service (Amazon S3) bucket and we recommend that you create a dedicated S3 bucket. However, you can also use an existing S3 bucket. For more information about this, see [Creating a bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html) in the Amazon S3 documentation. @@ -13 +13 @@ Your organization identifies a PDF file type that is continuously generated (for -The S3 bucket invokes an AWS Lambda function when the new PDF file is ingested. For more information about this, see [Using an Amazon S3 trigger to invoke a Lambda function](https://docs.aws.amazon.com//lambda/latest/dg/with-s3-example.html) in the AWS Lambda documentation. +The S3 bucket invokes an AWS Lambda function when the new PDF file is ingested. For more information about this, see [Using an Amazon S3 trigger to invoke a Lambda function](https://docs.aws.amazon.com/lambda/latest/dg/with-s3-example.html) in the AWS Lambda documentation. @@ -23 +23 @@ Use the following four best practices to ensure a successful PDF file ingestion: - * For bulk ingestion, use bulk dump (for example, uploading PDF files from a local drive). If you have more than one PDF file type, we recommend that you use different folders to hold each type of PDF file. We also recommend using a unique and descriptive naming standard for the files, such as `warehouse_<warehouse_number>_<mmddyy>_<PDF_file_type>.pdf`. + * For bulk ingestion, use bulk dump (for example, uploading PDF files from a local drive). If you have more than one PDF file type, we recommend that you use different folders to hold each type of PDF file. We also recommend using a unique and descriptive naming standard for the files, such as warehouse_<wharehouse_number>_<mmddyy>_<PDF_file_type>.pdf. @@ -27 +27 @@ Use the following four best practices to ensure a successful PDF file ingestion: - * Make sure that your PDF files are of good quality and clearly readable. We recommend using native PDF files, but you can also use scanned documents that are converted to a PDF format if the individual words are clear. For more information about this, see [PDF file preprocessing with Amazon Textract: Visuals detection and removal](https://aws.amazon.com//blogs/machine-learning/process-text-and-images-in-pdf-documents-with-amazon-textract/) on the AWS Machine Learning Blog. + * Make sure that your PDF files are of good quality and clearly readable. We recommend using native PDF files, but you can also use scanned documents that are converted to a PDF format if the individual words are clear. For more information about this, see [PDF file preprocessing with Amazon Textract: Visuals detection and removal](https://aws.amazon.com/blogs/machine-learning/process-text-and-images-in-pdf-documents-with-amazon-textract/) on the AWS Machine Learning Blog.