DuckDB

1. Overview and In-Process Architecture

   Embedded in-process analytical database, columnar vectorized execution and use cases.

2. Client APIs

   Python, R, Java, Node, CLI and WASM clients.

3. Importing and Exporting Data

   Reading CSV, JSON and Parquet files, COPY and querying files directly.

4. Parquet

   Reading and writing Parquet, projection and filter pushdown, partitioned files.

5. Data Types

   Nested types such as LIST, STRUCT and MAP, plus numeric, temporal and typing rules.

6. SQL Query Syntax

   SELECT, FROM, joins, GROUP BY ALL, QUALIFY, friendly SQL extensions and CTEs.

7. Window Functions

   OVER, partitions, frames and aggregate window functions.

8. Transactions and Concurrency

   MVCC, ACID transactions, single-writer model and concurrent access.

9. Persistence and Storage Format

   Single-file database format, row groups, compression and checkpointing.

10. Performance Guide

    Indexes, joins, memory and larger-than-memory processing, profiling and tuning.

11. Extensions

    httpfs, spatial, json, iceberg, delta and loading extensions.

12. Remote and Cloud Storage

    Querying S3 and HTTP files, secrets and remote reads.

13. Configuration

    Settings such as memory limit, threads and temp directory.

14. Execution Format and Internals

    Vectors, data chunks and vectorized execution.

Each section links to the official page. Titles and summaries by Extra Credit; the docs belong to their authors.

Source: endoflife.date

• Dask
• Databricks
• dbt
• Delta Lake
• Dgraph
• Druid
• DynamoDB
• Elasticsearch
• etcd
• Fivetran
• Flink
• Hive