DuckDB SQL OLAP Database

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

1. Subscribe and launch the AMI on your chosen instance (recommended: r5/r6i for analytics, c5/c6i for compute, m5/m6i balanced, minimum t3.medium)

2. SSH into your instance using your key pair

3. Verify DuckDB is installed by running: duckdb --version

4. Run your first query: SELECT * FROM 'your-file.parquet'

5. 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.