DuckDB

DuckDB 1.4.3 on Ubuntu 24.04 with 24/7 Support by bCloud

DuckDB 1.4.3 on Ubuntu 24.04 is a repackaged open source software offering with optional paid support, optimised for deployment on the AWS Marketplace. DuckDB is a lightweight, high performance, in process SQL OLAP database built specifically for analytical workloads. It delivers blazing fast query performance without requiring any outside services, complex configuration, or client-server setup.

Designed for efficiency and simplicity, DuckDB runs as a single binary inside your application, eliminating network overhead while delivering enterprise-grade analytical capabilities. It is production ready for modern data analytics, ETL pipelines, and cloud-native data science workflows on AWS.

• Blazing fast analytical queries with vectorized execution
• Zero external dependencies runs as a single binary
• In-process database eliminates client-server latency
• Columnar storage optimized for OLAP workloads
• Direct querying of Parquet, CSV, and JSON without data import
• Larger-than-memory processing with automatic disk spilling
• Full ACID transaction support
• Rich SQL dialect with window functions and CTEs
• MIT open-source license
• Runs everywhere from laptops to large AWS EC2 instances

Optimised for AWS Marketplace

This AMI is fully optimized for AWS Cloud environments and designed for fast, secure deployment

• Pre-installed DuckDB on Ubuntu 24.04 LTS
• Python 3 with DuckDB package preconfigured
• CloudWatch Agent for monitoring
• AWS Systems Manager SSM Agent enabled
• ENA drivers for enhanced networking performance
• NVMe drivers for high-speed I/O
• Security hardened OS configuration
• Pre-configured for S3 access and cloud storage
• Ready for data science, analytics, and ETL workloads

Supporting Software used python - 3.12

Key Features

• In-process SQL OLAP database
• Columnar storage with vectorized query execution
• Parallel query processing
• Direct Parquet, CSV, JSON, and NDJSON querying
• Larger than memory workloads with disk spilling
• Full ACID compliance
• Advanced SQL features including window functions, CTEs, and macros
• Extensions for spatial analytics, full-text search, and HTTP/HTTPS
• APIs for Python, R, Java, and Node.js
• Native S3 and cloud storage integration
• Partitioned and Hive-style dataset support

Performance and Analytics

• ranked in ClickBench and TPC benchmarks
• Vectorized OLAP execution engine
• Efficient compression and column pruning
• Filter and projection pushdown
• Join and aggregation optimizations
• Direct streaming of query results to Python and R

Data Source Support

• Native Parquet with metadata pushdown
• CSV with automatic schema detection
• JSON and NDJSON
• Excel support via extensions
• Direct S3 access
• Remote HTTP/HTTPS data sources
• Partitioned datasets with Hive partitioning
• Delta Lake and Apache Iceberg support

Python Data Science Ready

• Native Python API with zero-copy Arrow integration
• Pandas and Polars support without data duplication
• NumPy compatibility
• Jupyter Notebook ready
• SQL directly inside notebooks
• Query results as DataFrames
• User-defined Python functions
• SQL over Pandas DataFrames

Extensions Ecosystem

• Spatial extension PostGIS-compatible
• Full-text search
• JSON processing
• HTTP/HTTPS access
• AWS and S3 extensions
• Parquet and Arrow
• Delta Lake and Iceberg
• Active open-source community ecosystem

Common Use Cases

• Interactive analytics on large datasets
• ETL pipelines processing Parquet files on S3
• Data science workflows with Python and R
• Log analysis and aggregation
• Business intelligence and reporting
• Time-series analytics
• Geospatial analytics using the spatial extension
• Real-time dashboards querying S3 data
• CSV and JSON transformation
• Serverless and cloud-native analytics