DuckDB Analytics Database | JupyterLab AMI by cloudimg

This is a repackaged open source software product wherein additional charges apply for cloudimg support services.

Installing DuckDB, configuring JupyterLab, setting up nginx authentication, and preparing sample data typically takes hours of manual work. This AMI eliminates that effort entirely - launch an instance and run your first analytical query within minutes, not hours. Every component is pre-integrated and secured with per-instance credentials generated automatically on first boot, so there are no shared or default passwords to worry about.

DuckDB is an open source, in-process analytical database engine designed for fast queries against large columnar datasets. With over 6 million monthly downloads across its community, DuckDB has become a leading choice for OLAP workloads. This image ships DuckDB 1.5 inside a complete analytics environment so you can connect, load data, and run queries within minutes of launch.

• DuckDB CLI installed system-wide on every user's PATH
• JupyterLab notebook server pre-configured with Python 3.12, the DuckDB Python client, pandas, and PyArrow
• nginx on port 80 with HTTP basic authentication fronting JupyterLab
• SSH access for terminal-driven analytics via the DuckDB CLI

A one-million-row New York City yellow taxi trips parquet file is bundled on a dedicated data disk. A starter notebook opens a persistent DuckDB database against the parquet file and runs three analytical queries so you can see the engine in action before writing any code.

On first boot, a one-shot service generates a fresh JupyterLab administrator password unique to that instance, writes it into the nginx HTTP basic authentication store, and stores the plain-text value in a root-only file accessible via SSH. No shared or default credentials ship in the image. The dedicated storage volume can leverage EBS encryption for data at rest. Buyers requiring HTTPS should configure a TLS certificate on the nginx frontend or place the instance behind an AWS Application Load Balancer with TLS termination.

DuckDB databases, notebooks, and sample data live on a separate, independently resizable storage volume kept off the operating system disk. This means your analytics tier can grow without disturbing the rest of the instance - scale storage as your datasets expand without reprovisioning.

• Ad hoc analytics on parquet, CSV, and JSON files
• Local data warehouse andBI prototyping
• Querying data on Amazon S3 directly with DuckDB's httpfs extension
• Embedded analytics inside notebooks and Python applications
• Single-node OLAP for departmental reporting and finance teams

1. Launch the AMI on your preferred EC2 instance type
2. Ensure your security group allows inbound traffic on port 80 (HTTP) and port 22 (SSH)
3. SSH into the instance and retrieve the generated password from /root/.jupyter_password
4. Browse to the instance's public IP address and sign in with username "admin" and the retrieved password
5. Open the starter notebook and run the bundled analytical queries
6. Use the DuckDB CLI over SSH for terminal-driven workflows

24/7 technical support by email and live chat. Our engineers provide expert assistance with DuckDB deployment, notebook configuration, dataset loading, performance tuning, and engine upgrades. Critical issues receive a one-hour average response time.

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