• Amazon Bedrock Data Automation is a purpose-built service for transforming complex, unstructured content—such as invoices, contracts, forms, and research papers—into structured data. It handles the entire pipeline, from document parsing and classification to advanced information extraction using natural language and computer vision, enabling you to build scalable document workflows integrated directly with Knowledge Bases, databases, and analytics tools.

Amazon Bedrock Data Automation Features

• Multimodal Document Understanding
  • Processes a wide range of document types and formats, including scanned PDFs, digital PDFs, JPEG/PNG images, and Microsoft Word files. Extracts text, handwriting, layout, tables, and key-value pairs while maintaining structural relationships.

• Intelligent, Model-Driven Processing
  • Uses specialized Amazon Titan Multimodal Embeddings and built-in foundation models to perform tasks without manual template creation:

    • Classification: Automatically categorizes documents (e.g., invoice vs. utility bill).

    • Entity Extraction: Identifies and pulls specific data points (dates, amounts, names, terms).

    • Query-Based Extraction: Answers natural language questions about document content (e.g., “What is the total amount due?”).

    • Standardized Output: Returns clean, normalized data in consistent JSON schema.

• Serverless & Integrated Architecture

  • Fully Managed: No infrastructure to provision; scales automatically with your document volume.

  • Seamless Bedrock Integration: Output can be directly ingested into Amazon Bedrock Knowledge Bases for RAG applications or sent to other AWS services (S3, Lambda, SageMaker) via event-driven workflows.

  • High-Volume Batch Processing: Efficiently processes large document sets in asynchronous jobs via an asynchronous job API.

Amazon Bedrock Data Automation Use Cases

• Automated Financial and Legal Processing
  • Automate data entry from invoices, receipts, loan applications, and insurance claims by extracting vendor names, dates, line items, and totals into structured databases or ERP systems.
• Compliance and Contract Analysis
  • Review contracts and regulatory documents at scale to extract clauses, obligations, dates, and parties for analysis, risk assessment, and archival in contract management systems.
• Research and Content Enrichment
  • Process research papers, technical manuals, and reports to extract metadata, summaries, figures, and references, enriching content for searchable knowledge repositories or literature reviews.
• Customer Service Document Intake
  • Power customer service portals by instantly extracting relevant information from uploaded forms, identity documents, or support tickets, routing them correctly and populating case management systems.

Amazon Bedrock Data Automation Implementation 

• Core Processing Workflow
  • [Document Source (S3)] → (Invoke API) → [Data Automation Job] → (Parse & Analyze with FMs) → [Structured JSON Output] → (Deliver to S3 / Integrate with Knowledge Base)

Key APIs for Integration

• The service is accessed via its API to start asynchronous processing jobs. You provide the location of your source files in Amazon S3, specify the processing requirements, and monitor the job until completion to retrieve the structured JSON results.

Setup Checklist

1. Prepare Data Source: Upload documents to an Amazon S3 bucket.

2. Configure IAM Permissions: Set up a role granting Data Automation access to read from the input S3 bucket and write to the output bucket.

3. Select a Blueprint (Schema): Instead of a generic “task type,” select the appropriate Data Automation Project (Blueprint). You can use pre-built blueprints (e.g., “Invoices”) or create custom ones for specific data needs.

4. Start and Monitor Job: Invoke the Data Automation API to start the job. Poll the service for the job status until it is completed.

5. Integrate Output: Consume the structured JSON results—load them into a database, trigger a Lambda function, or ingest directly into a Bedrock Knowledge Base.

Amazon Bedrock Data Automation Security

• Data Encryption and Access Control
  • All data is encrypted at rest and in transit using AWS Key Management Service (KMS) keys. Access is strictly controlled via IAM policies that define which roles and users can start jobs and access S3 buckets.
• Data Privacy and Compliance
  • Your documents and extracted data are processed within your AWS environment. The service adheres to AWS compliance programs, helping you meet regulatory requirements for data handling.
• Auditing with AWS CloudTrail
  • All API calls made to Bedrock Data Automation are logged as events in AWS CloudTrail, providing an audit trail for security and compliance analysis.

Amazon Bedrock Data Automation Best Practices

• Optimize Document Quality for Accuracy
  • Ensure scanned documents are clear and legible. For forms, use machine-print text where possible for highest extraction accuracy. The service is robust but performs best with quality inputs.
• Start with a Pilot and Iterate
  • Begin by processing a small, representative sample of your documents. Analyze the JSON output to understand how the model interprets your data, then refine your questions or entity definitions as needed.
• Structure S3 Buckets for Efficiency
  • Organize your S3 buckets with clear prefixes (e.g., input/raw-pdfs/, output/structured-json/). This simplifies permission management and makes it easier to track job inputs and outputs.
• Integrate with Knowledge Bases for Full RAG Pipelines
  • For querying across a large document corpus, use Data Automation as the ingestion pre-processor. First, extract structured JSON; then, ingest the output into a Bedrock Knowledge Base to build a powerful, searchable Q&A system.

Amazon Bedrock Data Automation Pricing

Amazon Bedrock Data Automation uses a consumption-based pricing model based on the type and complexity of the processed content. The service offers two primary output types:

• Standard Output: For general analysis and integration with Amazon Bedrock Knowledge Bases.

• Custom Output: For extracting specific, user-defined fields using a custom processing blueprint. Cost increases with blueprint complexity.

Pricing (Example: US East N. Virginia Region)

Amazon Bedrock Data Automation Cheat Sheet References:

https://aws.amazon.com/bedrock/bda/

https://docs.aws.amazon.com/bedrock/latest/userguide/bda.html