Draft data ingestion strategies

1. Data Extraction

Process:

• Use an HTTP client (e.g., Python’s requests library) to fetch data from the API endpoint.
• Handle pagination, rate limits, and authentication (e.g., API keys, OAuth).
  Best Practices:
• Implement retry logic for transient failures.
• Log all requests and responses for troubleshooting.

Example Code Snippet:

2. Data Transformation

Process:

• Convert the raw JSON response to a tabular format (e.g., CSV or Parquet) using libraries like pandas.
• Perform data cleaning, such as:
  • Flattening nested structures.
  • Standardizing date formats.
  • Handling missing or invalid values. Best Practices:
• Validate the transformed data schema matches Redshift table structures.
• Write transformation logic as reusable functions.

Example Code Snippet:

3. Data Loading

Process:

• Use Amazon S3 as an intermediate storage layer:
  • Upload transformed files to an S3 bucket using AWS SDKs or CLI.
  • Use Redshift’s COPY command to load data from S3 into Redshift tables.
    Best Practices:
• Compress files (e.g., GZIP) before uploading to S3 for cost and speed optimization.
• Enable encryption for S3 objects to secure data in transit and at rest.

Example Code Snippet (S3 Upload):

Example Code Snippet (Redshift COPY Command):

4. Monitoring and Error Handling

Process:

• Monitor the pipeline’s health using CloudWatch or a custom logging system.
• Capture errors in extraction, transformation, or loading and implement notification mechanisms (e.g., email, Slack alerts).
  Best Practices:
• Use idempotent operations to handle retries without duplicating data.
• Log detailed error messages for debugging.