Handling multiple streams of unstructured data

Design the streaming ingestion topology

• Segment Firehose delivery streams per sensor type to isolate schema evolution and throttling policies.
• Partition S3 targets by dt=YYYY-MM-DD/hour=HH to align with Glue crawlers and Athena queries.
• Attach transformation Lambda functions to normalize encodings (JSON, Avro, protobuf) before the data touches the lake.

Author resilient Glue jobs for rolling analytics

Each Glue job extracts raw partitions, applies domain-specific parsing, and computes aggregated features.

• Use Glue Studio or PySpark scripts with window functions to compute rolling averages and standard deviations per asset.
• Persist intermediate checkpoints to an S3 _tmp prefix so failed jobs can resume without replaying the entire history.
• Bundle schema registries to detect malformed payloads early and quarantine partitions to a review bucket for analysts.

Publish curated features into SageMaker Feature Store

• Materialize feature groups in offline storage (S3) and configure streaming ingestion to the online store for low-latency access.
• Tag feature groups with source partition metadata to enable traceability back to raw sensor files.
• Automate Glue job completion hooks that trigger SageMaker pipelines to consume new features for training.

Version datasets with Lake Formation and Model Registry

• Assign Lake Formation LF-Tags that encode dataset version and sensitivity; update grants atomically after Glue job success.
• Register dataset manifests as artifacts in SageMaker Model Registry so each model version cites the exact feature snapshot.
• Emit lineage events to AWS DataZone or an internal catalog for compliance teams to audit the flow from sensor to prediction.

Reference assets for a full demo

Leverage the repository resources to dry-run the entire workflow-ingestion, harmonization, and feature serving-without needing proprietary data.

• Seed the raw landing zone with the mock payloads in assets/datasets/unstructured, covering streaming telemetry, technician notes, and inspection metadata.
• Provision the AWS infrastructure by deploying unstructured-multi-dataset-demo.yaml, which wires Firehose, Glue, Lake Formation, and SageMaker Feature Store.
• Run the Glue job once to materialize parquet outputs and watch the SageMaker Feature Group populate with rolling statistics derived from all three feeds.

Map CloudFormation components to responsibilities

Highlight the most important resources from the demo template so stakeholders can see how the infrastructure enforces the playbook patterns.

• Resources:
    RawDataBucket:
      Type: AWS::S3::Bucket

  Provides the encrypted, versioned landing zone so multi-format payloads land safely before Glue crawlers scan them.

• Resources:
    SensorEventsFirehose:
      Type: AWS::KinesisFirehose::DeliveryStream

  Streams sensor JSON into partitioned S3 prefixes, letting the team observe buffering, error handling, and logging in a production-style topology.

• Resources:
    MultiSourceGlueJob:
      Type: AWS::Glue::Job

  Runs the harmonization ETL with the same arguments you will parameterize in production, including feature group names and bucket paths.

• Resources:
    FeatureGroup:
      Type: AWS::SageMaker::FeatureGroup

  Creates the governed feature store destination so teams can practice lineage, tagging, and online/offline synchronization before shipping live data.

• Resources:
    DataLakeTag:
      Type: AWS::LakeFormation::Tag

  Shows how dataset versioning is codified through Lake Formation tags, aligning governance language between the demo and your regulated environments.

Simulated production walk-through

Demonstrate the real-world lifecycle using the mock assets so stakeholders understand every control point before onboarding live data.

• Push the JSONL sensor events through the Firehose stream to observe partitioned objects landing in S3 and being cataloged automatically by the raw crawler.
• Trigger the Glue ETL script to join sensor readings with technician observations; review quarantined records and Lake Formation tag updates in the AWS Console.
• Inspect the SageMaker Feature Group to confirm that online and offline stores receive synchronized feature values linked back to the original dataset versions.