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This product has charges associated with it for seller support. DuckDB, the open source in-process analytical database, preinstalled in a ready to use JupyterLab notebook environment with a sample parquet dataset. Per instance credentials are generated on first boot. Backed by 24/7 cloudimg support.
Overview
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JupyterLab launcher landing
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Starter notebook open
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Analytical query result
This is a repackaged open source software product wherein additional charges apply for cloudimg support services.
Overview DuckDB is an open source, in-process analytical database engine designed for fast queries against large columnar datasets. This image ships DuckDB inside a complete analytics environment so you can connect, load data and run queries within minutes of launch. The current release available is DuckDB 1.5.
Application Stack DuckDB CLI installed system wide on every user's PATH. A JupyterLab notebook server pre configured with Python 3.12, the DuckDB Python client, pandas and PyArrow, fronted by nginx on port 80 with HTTP basic authentication.
Sample Dataset and Notebook 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 and runs three analytical queries so you can see the engine in action before writing any code.
Secure First Boot On the first boot of your instance 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. No shared or default credentials ship in the image.
Ready To Use The DuckDB CLI, the Python DuckDB client, the JupyterLab notebook server and the sample dataset are all configured. Browse to the instance address, sign in to JupyterLab, open the starter notebook and start querying immediately. The DuckDB CLI is also available directly over SSH for terminal driven analytics.
Dedicated Storage Tier DuckDB databases, notebooks and sample data live on a separate, independently resizable storage volume kept off the operating system disk so the analytics tier can be grown without disturbing the rest of the instance.
cloudimg Support 24/7 technical support by email and chat. Help with DuckDB deployment, notebook configuration, dataset loading, performance tuning and engine upgrades.
Use Cases Ad hoc analytics on parquet, CSV and JSON files. Local data warehouse and BI 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.
All product and company names are trademarks or registered trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them.
Highlights
- DuckDB preinstalled and ready, with the CLI on every user's PATH and a JupyterLab notebook server fronted by nginx with HTTP basic authentication and no manual setup required
- Sample one million row parquet dataset and a starter notebook bundled on a dedicated, independently resizable data volume so you can run analytical queries within minutes of launch
- 24/7 technical support from cloudimg, with expert assistance for DuckDB deployment, notebook configuration, dataset loading and performance tuning
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04
Deployed on AWS
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Pricing
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Dimension | Description | Cost/hour |
|---|---|---|
m5.large Recommended | m5.large | $0.08 |
t3.micro | t3.micro instance type | $0.04 |
t2.micro | t2.micro instance type | $0.04 |
m8azn.6xlarge | m8azn.6xlarge instance type | $0.24 |
m7a.metal-48xl | m7a.metal-48xl instance type | $0.24 |
i4i.24xlarge | i4i.24xlarge instance type | $0.24 |
c8a.4xlarge | c8a.4xlarge instance type | $0.24 |
r8id.16xlarge | r8id.16xlarge instance type | $0.24 |
m8i-flex.large | m8i-flex.large instance type | $0.08 |
c6a.2xlarge | c6a.2xlarge instance type | $0.24 |
Vendor refund policy
Refunds available on request.
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Initial release of DuckDB 1.5 in a JupyterLab notebook environment on AWS.
Additional details
Usage instructions
Connect via SSH on port 22 as the default login user for your operating system variant (the user guide lists it per variant). The DuckDB CLI is on the PATH, so 'duckdb /opt/duckdb/samples/main.duckdb' opens a session against the bundled database. JupyterLab is served on port 80 behind HTTP basic authentication; browse to http://<instance-public-ip>/ and sign in as 'duckdb'. Retrieve the generated password with: sudo cat /stage/scripts/duckdb-credentials.log. The starter notebook 01-duckdb-quickstart.ipynb opens the parquet sample dataset and runs three example queries. Restrict port 80 to trusted networks because JupyterLab can execute arbitrary Python; to enable HTTPS, follow the reverse proxy section of the user guide.
Resources
Vendor resources
Support
Vendor support
cloudimg provides 24/7 technical support for this product by email and live chat. Our engineers help with deployment, configuration, updates, performance tuning and troubleshooting; critical issues receive a one hour average response. Contact support@cloudimg.co.uk .
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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