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This product has charges associated with it for seller support. DuckDB in-process SQL OLAP database pre-configured on AWS EC2 with 24/7 cloudimg support. Query Parquet, CSV, and JSON at benchmark-leading speed with zero dependencies.
Overview
This is a repackaged open source software product wherein additional charges apply for cloudimg support services.
DuckDB on AWS - Pre-Configured AMI with 24/7 Expert Support
Get a production-ready DuckDB analytical database running on EC2 in minutes.## Why This AMI Instead of DIY?
Installing DuckDB from source means configuring drivers, monitoring, networking, and security yourself. This AMI eliminates that work and adds ongoing expert support:
- Pre-hardened security - configured with restricted access, optimized for analytical workloads in production environments
- Monitoring built in - CloudWatch Agent pre-installed for resource and query performance visibility
- S3 integration ready - AWS extension pre-configured for direct Parquet/CSV querying from S3 buckets
- Expert support included - DuckDB architecture guidance, query optimization, data pipeline design, and Python integration assistance from cloudimg's team
- Multiple versions available - choose from multiple DuckDB versions spanning multiple OS variants at launch
Unlike the free open-source binary or competing AMIs without support, this listing gives you a direct line to specialists who can help with query optimization, S3 configuration, migration from other databases, and performance tuning.
Getting Started
- Subscribe and launch the AMI on your chosen instance (recommended: r5/r6i for analytics, c5/c6i for compute, m5/m6i balanced, minimum t3.medium)
- SSH into your instance using your key pair
- Verify DuckDB is installed by running: duckdb --version
- Run your first query: SELECT * FROM 'your-file.parquet'
- For S3 access, the AWS extension is pre-configured - query remote Parquet files directly
Python 3 with the DuckDB package is pre-installed, so you can immediately use DuckDB in Jupyter notebooks or Python scripts with zero-copy Arrow integration.
Real-World Use Case: Large-Scale File Analytics
Consider an analytics team processing hundreds of gigabytes of daily clickstream data stored as Parquet files on S3. Using a single r6i.2xlarge instance with this AMI, they can query that data directly without loading it into a separate data warehouse - no ETL pipeline to maintain, no cluster to manage. DuckDB's vectorized execution and metadata pushdown mean only relevant columns and row groups are read, keeping query times fast and costs low compared to always-on cluster solutions.
Performance
DuckDB ranks #1 in ClickBench and TPC-H benchmarks. Its columnar storage and vectorized execution engine deliver analytical query performance that rivals dedicated data warehouses - but running in-process on a single EC2 instance with zero network overhead. Larger-than-memory workloads are handled through intelligent spilling to disk with parallel execution.
Pre-Configured Components
- DuckDB with Python 3 DuckDB package
- CloudWatch Agent for monitoring
- Systems Manager Agent for management
- ENA drivers for enhanced networking
- NVMe drivers for optimal I/O performance
- Pre-configured S3 access via AWS extension
Key Capabilities
- File format support - Native Parquet with metadata pushdown, CSV with auto type detection, JSON/NDJSON, Excel via extension, Delta Lake and Iceberg support
- SQL dialect - Full SQL:2016 with window functions, CTEs, recursive queries, PIVOT/UNPIVOT, JSON operators, and user-defined functions
- Data science integration - Python API with zero-copy Arrow, Pandas interchange, Polars integration, NumPy support, Jupyter-ready
- Extensions - Spatial for GIS (PostGIS compatible), full-text search, HTTP/HTTPS, community ecosystem
- Processing - Parallel query execution, larger-than-memory with spilling, full ACID transactions, partitioned dataset support including Hive partitioning
Use Cases
- Interactive analytics on large Parquet/CSV datasets stored in S3
- ETL pipelines replacing heavyweight cluster solutions for single-node workloads
- Data science workflows with Python/R needing fast SQL on local or remote files
- Log analysis and aggregation without standing up Elasticsearch or similar
- Business intelligence querying S3 data lakes directly
- Geospatial data processing with the spatial extension
Support Included
24/7 cloudimg support with guaranteed 24-hour response SLA and average one-hour response for critical issues. Coverage includes DuckDB architecture guidance, query optimization, data pipeline design, Python integration assistance, performance tuning, S3 configuration, and migration from other databases.
FAQ
What instance should I use? r5/r6i for memory-intensive analytics, c5/c6i for compute-heavy workloads, m5/m6i for balanced use. Minimum t3.medium.
Can I query S3 directly? Yes. The AWS extension is pre-configured for direct Parquet/CSV querying from S3 with metadata pushdown.
Highlights
- 24/7 expert support with guaranteed 24-hour response SLA and average one-hour response for critical issues. Coverage includes DuckDB architecture guidance, query optimization, data pipeline design, Python integration, S3 configuration, and migration assistance - a direct line to specialists rather than community forums or self-support.
- Pre-configured and production-ready AMI - DuckDB, Python 3, CloudWatch monitoring, Systems Manager, ENA/NVMe drivers, and S3 access all set up so you skip hours of manual configuration. Multiple DuckDB versions available across multiple OS variants. Launch on recommended r5/r6i instances and run your first query in minutes.
- Benchmark-leading analytical performance on a single EC2 instance - DuckDB ranks #1 in ClickBench and TPC-H. Query Parquet, CSV, and JSON files directly from S3 without import steps. Handle larger-than-memory datasets with parallel execution and intelligent spilling, replacing heavyweight cluster solutions for single-node analytical workloads.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
OtherLinux 9
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Pricing
Free trial
Try this product free for 7 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
If you are an AWS Free Tier customer with a free plan, you are eligible to subscribe to this offer. You can use free credits to cover the cost of eligible AWS infrastructure. See AWS Free Tier for more details. If you created an AWS account before July 15th, 2025, and qualify for the Legacy AWS Free Tier, Amazon EC2 charges for Micro instances are free for up to 750 hours per month. See Legacy AWS Free Tier for more details.
Dimension | Description | Cost/hour |
|---|---|---|
m5.large Recommended | m5.large | $0.10 |
t3.micro | t3.micro instance type | $0.06 |
t2.micro | t2.micro instance type | $0.06 |
r7iz.8xlarge | r7iz.8xlarge instance type | $0.28 |
r6idn.12xlarge | r6idn.12xlarge instance type | $0.28 |
c6i.8xlarge | c6i.8xlarge instance type | $0.28 |
m5d.2xlarge | m5d.2xlarge instance type | $0.28 |
m6idn.metal | m6idn.metal instance type | $0.28 |
c6a.xlarge | c6a.xlarge instance type | $0.15 |
i3.metal | i3.metal instance type | $0.28 |
Vendor refund policy
Refunds available on request.
Legal
Vendor terms and conditions
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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
Security update: full dnf update to current el9 (0 advisories remaining), rpm database reconciled, kernel refreshed. No application or configuration changes; DuckDB validated on a fresh boot.
Additional details
Usage instructions
Please visit the User Guide for this product on the cloudimg website (https://www.cloudimg.co.uk ).
Resources
Vendor resources
Support
Vendor support
24/7 Support from cloudimg
This AMI includes 24/7x365 expert support from cloudimg with a guaranteed 24-hour response SLA and average one-hour response for critical issues.
Contact: support@cloudimg.co.uk
Coverage includes:
- DuckDB architecture guidance and best practices
- Query optimization and performance tuning
- Data pipeline design and implementation
- Python integration assistance
- S3 configuration and access troubleshooting
- Migration from other databases
- Instance sizing recommendations
- General troubleshooting and issue resolution
- Refund requests and billing inquiries
Recommended Instance Types:
- r5/r6i - memory-intensive analytical workloads
- c5/c6i - compute-heavy processing
- m5/m6i - balanced workloads
- Minimum: t3.medium
Getting Started After Launch:
- Launch the AMI on your chosen instance type
- SSH into the instance using your key pair
- Run "duckdb --version" to verify installation
- Execute your first query: SELECT * FROM 'your-file.parquet'
- For S3 queries, the AWS extension is pre-configured and ready to use
Python 3 with the DuckDB package is pre-installed for immediate data science workflows.
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AWS infrastructure support
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