DuckDB AMI

This is repackaged software with additional charges for 24/7 support and guaranteed 24hr response SLA.

Product Overview

DuckDB with 24/7 cloudimg support. In-process SQL OLAP database for analytical workloads. Zero external dependencies. Blazing-fast performance - #1 in ClickBench and TPC-H benchmarks. Reads Parquet, CSV, JSON directly. Parallel execution and larger-than-memory processing. Multiple DuckDB versions available on launch spanning multiple OS variants. Production-ready for data analytics and ETL.

Why Choose DuckDB?

Blazing-fast analytical queries. Zero dependencies - single binary. In-process database eliminates client-server overhead. Columnar storage for OLAP workloads. Vectorized query execution. Direct Parquet and CSV querying without import. Larger-than-memory processing with spilling. Full ACID transactions. Rich SQL dialect with window functions. Python, R, Java integration. Runs anywhere - laptop to cloud. MIT open-source license.

Pre-Configured Integration

DuckDB pre-installed and configured. Python 3 with DuckDB package. CloudWatch Agent for monitoring. Systems Manager Agent. ENA drivers for enhanced networking. NVMe drivers for optimal I/O. Security hardened. Optimized for analytical workloads. Pre-configured for S3 access. Ready for data science workflows.

Key Features

In-process SQL OLAP database. Columnar storage with vectorized execution. Zero external dependencies. Direct Parquet, CSV, JSON querying. Parallel query execution. Larger-than-memory workloads with spilling. Full ACID transactions. Window functions and CTEs. Extensions for spatial, full-text search. Python, R, Java, Node.js APIs. S3 and cloud storage integration. Partitioned dataset support.

Use Cases

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

Performance & Analytics

#1 in ClickBench and TPC-H benchmarks. Vectorized OLAP execution. Columnar storage. Parallel processing. Efficient compression. Direct Parquet reading. Filter and projection pushdown. JOIN optimizations. Query streaming to Python/R.

File Format Support

Native Parquet with metadata pushdown. CSV with auto type detection. JSON and NDJSON. Excel via extension. Direct S3 access. Remote HTTP/HTTPS. Partitioned datasets. Hive partitioning. Delta Lake and Iceberg support.

Python & Data Science

Native Python API with zero-copy Arrow. Pandas interchange without copying. Polars integration. NumPy support. Jupyter ready. SQL in notebooks. Results as DataFrames. User-defined Python functions. SQL over Pandas.

SQL Capabilities

Full SQL:2016. Window functions. CTEs. Recursive queries. JSON operators. String/regex functions. Date/time arithmetic. FILTER clause. PIVOT/UNPIVOT. UDFs. Query macros.

Extensions

Spatial for GIS (PostGIS compatible). Full-text search. JSON extension. HTTP/HTTPS. AWS for S3. Parquet and Arrow. Delta Lake and Iceberg. Community ecosystem.

Support Included

24/7 cloudimg support. Guaranteed 24hr response SLA. Average one-hour response for critical issues. DuckDB architecture guidance. Query optimization. Data pipeline design. Python integration assistance. Performance tuning. S3 configuration. Migration from other databases.

FAQ

Q: Support included? A: 24/7 with 24hr response. Architecture, optimization, integration, performance.

Q: Versions available? A: Multiple DuckDB versions on launch spanning multiple OS variants.

Q: What is in-process DB? A: Runs in app process. No server. Zero network overhead. Like SQLite for analytics.

Q: How fast? A: #1 in ClickBench/TPC-H. Vectorized execution, columnar storage.

Q: Query S3 directly? A: Yes. AWS extension for direct S3 Parquet/CSV querying. Metadata pushdown.

Q: Recommended instances? A: r5/r6i for analytics. c5/c6i for compute. m5/m6i balanced. Min t3.medium.

Q: Use with Python? A: pip install duckdb. Zero-copy Arrow with Pandas. Query DataFrames.

Q: Difference vs traditional? A: OLAP optimized. Columnar. In-process. Fast file analytics.

Q: Handle big data? A: Yes. Larger-than-memory with spilling. Parallel. Efficient for TBs.

Q: Load Parquet? A: SELECT * FROM 'file.parquet'. Direct query. No import.

Trademarks

This software listing is packaged by cloudimg. The respective trademarks mentioned in the offering are owned by the respective companies, and their use does not imply any affiliation or endorsement.