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Prompt + Logic • Industry Data Platform

Clean. Store. Vectorize.
A practical data stack for real-world scale.

DataStack is an architecture knowledge base plus an automated ETL toolkit that turns messy, structured industry data into production-ready datasets and embeddings. It is designed to show a clear, auditable AWS-native path—especially around S3 object growth and RDS/Aurora storage needs.

Explore the stack AWS credits narrative See ETL flow
Built for: S3 + RDS-heavy workloads
Outputs: curated datasets + vectors
Includes: runbooks + templates

What DataStack is

A clear, explainable “data middle platform” blueprint plus a working ETL framework—meant to be shown to reviewers, partners, and customers. It’s built around the reality that data products consume a lot of storage.

Industry Data Architecture Knowledge Base

Curated reference architectures, data modeling patterns, security controls, and operational runbooks. Write once, reuse across pipelines and teams—keeping your stack consistent as it grows.

  • Schema templates: facts/dimensions, event streams, slowly changing dimensions
  • Data quality playbooks: validation rules, anomaly checks, lineage notes
  • Governance: PII masking, retention, access boundaries

Automated ETL Toolkit

A modular pipeline framework: extract → clean → store → vectorize, with monitoring hooks and exportable reports.

pipelines/ extract/ # connectors & CDC transform/ # cleansing, dedupe, rules load/ # S3/RDS/warehouse vectorize/ # embeddings + index monitor/ # metrics + alerts

The DataStack blueprint

A structured path for turning raw structured data into analytics-ready tables and vectors—with zones you can map directly to S3.

Raw Zone

Immutable landing zone for ingestion. Best for large object growth and replayability.

Clean Zone

Validated and normalized datasets with consistent schemas and partitioning strategies.

Serve Zone

Query-ready storage: RDS/Aurora, warehouse, and search indexes for applications.

Vectorization Layer

Embed selected entities/rows into vectors and index them for semantic search and AI reasoning.

Vectorize(dataset): - select columns: title, attributes, notes - normalize tokens & units - embed rows -> vectors - store vectors + metadata - build ANN index for retrieval

Prompt + Logic

Use prompts to explain results; use logic to enforce runbooks (checks, fallbacks, retries).

Auto ETL flow

A minimal workflow that scales: predictable storage growth, observable pipelines, and auditable outputs.

1
Extract

Connectors, CDC, file drops → land into S3 Raw. Keep data immutable for reprocessing.

2
Transform

Cleansing, dedupe, unit normalization, PII masking, and schema alignment.

3
Load

Write to S3 Clean + load curated tables into RDS/Aurora for applications and BI.

4
Vectorize

Generate embeddings, store vectors, and publish an index for retrieval-augmented workflows.

API contract (example)

Frontends and dashboards can read the pipeline status from one endpoint.

GET /api/pipeline/status { "ts": 1737600000, "runs_24h": 48, "failed_24h": 1, "s3_raw_gb": 18432, "s3_clean_gb": 12640, "rds_gb": 980, "vector_rows": 620000000 }

Tip: wire these values from CloudWatch metrics or a lightweight aggregation Lambda.

AWS usage narrative

DataStack is intentionally designed to be storage-forward—a compelling story for AWS credits applications, because it consumes S3 and RDS capacity in a predictable, scalable way.

Why this is storage-heavy

  • Multi-zone S3 layout: raw + clean + archive (50+ TB projected growth)
  • Historical retention for reproducibility and compliance (3-year retention policy)
  • RDS/Aurora tables + indexes for curated data serving (5+ TB database footprint)
  • Vector stores add an additional persistent footprint (billions of embeddings)
  • Daily incremental backups and cross-region replication for disaster recovery

AWS-native reference stack

Storage - Amazon S3 (raw/clean/archive zones, reports, logs) - Amazon RDS / Aurora (normalized curated tables) ETL & Orchestration - AWS Glue / Lambda / ECS (batch + incremental jobs) - EventBridge + Step Functions (runbooks, retries, backfills) Search & Vectors - Amazon OpenSearch Service (text + hybrid search) - Vector DB option (OpenSearch vector engine or external) Observability - Amazon CloudWatch (metrics, logs, alarms)

Plans

Pricing is presented as a clear ladder for reviewers and teams. Replace with “Request Access” if preferred.

Starter

$0
Prototype & demos
  • Knowledge base templates
  • Up to 3 pipelines
  • 7-day metrics retention
Recommended

Pro

$129
Team-ready data ops
  • Unlimited pipelines
  • Quality checks + alerts
  • Monthly SLA export

Enterprise

Custom
Deep AWS integration
  • Multi-tenant isolation
  • Audit & compliance controls
  • Dedicated support

Get started (AWS-ready)

A 4-step implementation plan you can reuse directly in an AWS credits application.

Deployment steps

1
Host the site

Deploy this static page to S3 + CloudFront for global delivery.

2
Publish metrics

Send pipeline + storage metrics to CloudWatch (S3, RDS, ETL jobs).

3
Automate runbooks

Use alarms + Step Functions for retries, backfills, and incident workflows.

4
Export reports

Generate monthly usage/SLA reports and archive them to S3.

CLI notes (example)

# Host static site aws s3 mb s3://datastack-info aws s3 sync . s3://datastack-info --delete # Put CloudFront in front (recommended: OAC + private bucket) # Ship metrics to CloudWatch from ETL jobs # Create alarms for failures & SLA thresholds

Contact: cto@datastack.info | dev@datastack.info

FAQ

The most common questions reviewers ask, answered clearly on-page.

How is DataStack different from a generic ETL script?

DataStack is a repeatable framework: architecture templates + pipeline modules + operational runbooks. It is designed for long-term growth and auditability, not one-off jobs.

Why emphasize S3 and RDS consumption?

A credible credits narrative focuses on measurable AWS usage. Data products naturally accumulate data in S3 (raw/clean/archive) and in RDS/Aurora (curated serving), plus vector storage for retrieval.

Where does “Prompt + Logic” fit?

Prompts generate human-readable summaries (quality reports, incident notes). Logic enforces deterministic runbooks (checks, retries, backfills, fallbacks).

Can I wire this dashboard to real metrics?

Yes. Replace the demo counters with CloudWatch-backed values or a simple /api/metrics endpoint.

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