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I built an open-source tool to query on-prem databases and DynamoDB in plain English

The Problem If you've ever worked in a hybrid cloud environment, you know the pain. You've got legacy data sitting on-premise — MySQL, PostgreSQL, Oracle, SQL Server — and you need it in AWS. So you write a custom sync script. Then ano…

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The Problem



If you've ever worked in a hybrid cloud environment, you know the pain.



You've got legacy data sitting on-premise — MySQL, PostgreSQL, Oracle, SQL Server — and you need it in AWS. So you write a custom sync script. Then another one. Then a cron job breaks at 2am. Then someone changes a schema and the whole pipeline falls apart.



I've been in that situation too many times. So I built IntelliHybrid.









What is IntelliHybrid?



IntelliHybrid is an open-source Python framework that creates a secure, bidirectional sync between your on-premise databases and AWS DynamoDB — with an AI layer on top that lets you query your data in plain English.



No more writing FilterExpression and KeyConditionExpression by hand. Just ask.




result = await assistant.chat("Show me all orders from customer C-001")
result = await assistant.chat("How many products have stock below 10?")
result = await assistant.chat("Find users who signed up this month")






It figures out the right DynamoDB operation — query, scan, or get_item — and returns live data.









Core Features






🔄 Bidirectional Sync



Data flows both ways — on-prem → DynamoDB and DynamoDB → on-prem — on a configurable schedule.




intellihybrid sync --mode bidirectional --interval 60









🔐 Security First




  • KMS-encrypted DynamoDB tables by default

  • TLS 1.3 for all data in transit

  • Least-privilege IAM roles auto-generated per table

  • Secrets via environment variables or AWS Secrets Manager — never in config files






⚡ Auto-Provisioning



Define your tables in a simple YAML config and IntelliHybrid creates everything in AWS for you.




dynamodb:
tables:
- name: orders-table
partition_key: { name: orderId, type: S }
sort_key: { name: customerId, type: S }
billing_mode: PAY_PER_REQUEST









🤖 AI-Generated Schema Docs



One call produces a full data dictionary — useful for onboarding, compliance, or just understanding what's in a table.




dictionary = await intel.generate_data_dictionary("orders-table")






Output:




# Data Dictionary: `orders-table`

| Attribute | Type | Description |
|-----------------|------|---------------------------------------------------|
| orderId 🔑 PK | S | Unique identifier for each order transaction |
| customerId 🔑 SK| S | References the customer — links to users-table PK |
| status | S | Fulfillment state: processing, shipped, delivered |
| total | N | Order value in USD cents |
| createdAt | N | Unix timestamp, used for date-range queries |












Getting Started






pip install intellihybrid
intellihybrid init --config config/config.yaml
intellihybrid sync --mode bidirectional






Setup takes about 5 minutes.









Try the Live Demo



If you want to see the AI query interface without setting anything up, there's a fully interactive browser demo:



👉 Live AI Demo



No backend, no AWS account needed — runs entirely in the browser.









What's Next




  • MongoDB connector (coming soon)

  • Web UI dashboard

  • CDC real-time streaming

  • Terraform module










If this solves a pain point you've had, a ⭐ on GitHub goes a long way. And I'd love to hear feedback — what database or feature would make this useful for your setup?

CTI Threat Relationship Graph2 Knoten / 1 Relationen
CVE / Incident Software MITRE ATT&CK CWE Weakness IoC
SOC Incident Playbook: Remote Code Execution (RCE) Defense
title: Detect Exploitation - I built an open-source tool to query on-prem databases and DynamoDB in plain English
id: 2c0ce053-782a-46e8-ae19-95e7381c7f37
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-24
logsource:
  category: network_connection
  product: any
detection:
  selection:
      CommandLine|contains:
        - 'exploit'
  condition: selection
falsepositives:
  - Legitime administrative Zugriffe oder Penetrationstests
level: high
tags:
  - attack.initial_access
rule CTI_Threat_Indicator {
    meta:
        author = "iShareStuff CTI Automated Detection Engine"
        date = "2026-09-24"
        description = "YARA Signature for "
    strings:
        $str = "I built an open-source tool to" ascii wide
    condition:
        any of them
}
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Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich I built an open-source tool to query on-.... Basierend auf 368k Vektor-Korrelationen werden sofortige Isolationsmaßnahmen für betroffene Endpunkte empfohlen.

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