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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
LakeSoul is an end-to-end, realtime cloud-native Lakehouse framework for fast data ingestion, concurrent updates, incremental analytics, multimodal data processing and vector search — powering next-generation BI and AI workloads.
| Date | Stars |
|---|---|
| 2026-07-24 | 3243 |
| 2026-07-25 | 3243 |
| 2026-07-28 | 3243 |
| 2026-07-30 | 3243 |
| 2026-08-06 | 3243 |
Today
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Momentum
0.0
growth rate 0.00%/day
<!-- SPDX-FileCopyrightText: 2023 LakeSoul Contributors SPDX-License-Identifier: Apache-2.0 --> <img src='https://github.com/lakesoul-io/artwork/blob/main/horizontal/color/LakeSoul_Horizontal_Color.svg' alt="LakeSoul" height='200'> <img src='https://github.com/lfai/artwork/blob/main/lfaidata-assets/lfaidata-project-badge/sandbox/color/lfaidata-project-badge-sandbox-color.svg' alt="LF AI & Data Sandbox Project" height='180'>      [](https://deepwiki.com/lakesoul-io/LakeSoul) [中文介绍](README-CN.md) # Beyond Table Formats — A Complete Lakehouse Solution While Apache Iceberg provides a de-factor open table format, LakeSoul aims to deliver a batteries-included, production-ready lakehouse platform. Beyond the table format itself, LakeSoul comes with built-in automated disaggregated multi-level compaction, fine-grained RBAC (including S3 proxy-based access control), high-performance OLAP queries, vector retrieval, and native multimodal data processing powered by Ray and Daft. Instead of assembling and maintaining separate catalogs, compaction services, and auth layers, you get a production-ready lakehouse out of the box. # Rust-Native Core, Consistent Everywhere LakeSoul's metadata management and file format IO are implemented entirely in Rust — a single, high-performance core — with idiomatic bindings for Java, Python, and C++. Whether you're querying via Spark, streaming via Flink, or training models via PyTorch, Ray, or Daft, every engine and every language shares the same ACID guarantees, the same upsert semantics, and the same read performance. There are no per-language/per-engine re-implementations of the table format, no subtle behavioral divergences between bindings, and no fragmented compatibility matrix to navigate. Compute framework support matrix: | Engine | Version | Read | Write | Interface | | ------- | ------------ | ------------- | ------------- | --------------------------- | | Spark | 3.5 | ✓ Batch | ✓ Batch | Java / Python / Scala / SQL | | Flink | 1.20 | ✓ Streaming | ✓ Streaming | Java / SQL | | Presto | 0.296(velox) | ✓ Batch | - | SQL | | Ray | 2.55 | ✓ Distributed | ✓ Distributed | Python | | Daft | 0.7+ | ✓ Distributed | ✓ Distributed | Python | | DuckDB | latest | ✓ Standalone | — | Python | | PyArrow | 16+ | ✓ Standalone | ✓ Standalone | Python | | Pandas | 2.0+ | ✓ Standalone | ✓ Standalone | Python | # Core Features LakeSoul is a cloud-native Lakehouse framework that supports scalable metadata management, ACID transactions, efficient and flexible upsert operation, schema evolution, and unified streaming & batch processing. LakeSoul supports multiple computing engines to read and write lake warehouse table data, including Spark, Flink, Presto, PyTorch, Ray and Daft. LakeSoul supports storage systems such as HDFS and S3. LakeSoul supports two file formats: parquet(default) and [vortex](https://vortex.dev/). Vortex file format can be used to store multimodal data and vector embeddings.  LakeSoul was originally created by DMetaSoul company and was donated to Linux Foundation AI & Data as a sandbox project since May 2023
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Xu Chen · China
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Kai Sun · DMetaSoul · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ff9c995c40ab441f, topic:vector-search, desc:vector search
matched fp:ff9c995c40ab441f, topic:ray
matched fp:ff9c995c40ab441f, topic:pytorch