spb/tools-spboucher-ai

1{2  "aliases": [3    "delta-rs",4    "deltalake"5  ],6  "workflows": [7    "Incremental ETL with merges on Databricks or Spark",8    "Transactional Parquet writes from Python with deltalake",9    "Reading lake tables from Polars or DuckDB"10  ],11  "advantages": [12    "Simple log-based design on plain object storage",13    "Mature Spark integration",14    "Lightweight Rust and Python access"15  ],16  "limitations": [17    "Feature set advances first on Databricks",18    "Protocol versions must be supported by each reader",19    "Catalog integration less standardized than Iceberg's REST catalog"20  ],21  "prerequisites": [22    "Object or file storage plus Spark or the deltalake Python package"23  ],24  "audiences": [25    "Spark and Databricks users",26    "Python data engineers needing transactional Parquet"27  ],28  "alternatives": [29    "apache-iceberg",30    "apache-parquet",31    "apache-spark",32    "duckdb"33  ],34  "tags": [35    "lakehouse",36    "table-format",37    "parquet",38    "open-source",39    "rust",40    "python"41  ],42  "use_cases": [43    "query-parquet-files",44    "govern-lake-data"45  ],46  "operating_systems": [47    "any"48  ],49  "deployment_models": [50    "on-premises",51    "managed-cloud",52    "embedded"53  ],54  "integrations": [55    "Apache Spark",56    "Databricks",57    "Polars",58    "DuckDB",59    "Trino",60    "Apache Flink",61    "pandas"62  ],63  "data_formats": [64    "Parquet",65    "Delta transaction log (JSON, checkpoints)"66  ],67  "languages": [68    "Scala",69    "Java",70    "Rust",71    "Python"72  ],73  "self_hostable": true,74  "discovery_method": "editorial-research",75  "depth": "standard",76  "editorial_status": "reviewed",77  "visibility": "public",78  "featured": false,79  "author": "batch-D",80  "slug": "delta-lake",81  "name": "Delta Lake",82  "vendor": {83    "name": "Delta Lake project (Linux Foundation; created by Databricks)",84    "slug": "delta-lake-project",85    "website": "https://delta.io/"86  },87  "homepage_url": "https://delta.io/",88  "docs_url": "https://docs.delta.io/",89  "repo_url": "https://github.com/delta-io/delta",90  "license_url": "https://github.com/delta-io/delta/blob/master/LICENSE.txt",91  "product_type": "specification",92  "product_status": "active",93  "summary": "Open table format with a transaction log over Parquet files, created by Databricks and hosted by the Linux Foundation, with Spark, Rust and Python implementations",94  "description": "Delta Lake is an open-source table format that layers an ordered transaction log (the _delta_log directory of JSON and checkpoint files) over Parquet data files to provide ACID transactions, schema enforcement and evolution, time travel and efficient upserts and deletes on object storage. It was created by Databricks, is hosted by the Linux Foundation under the Apache 2.0 license, and is the native table format of the Databricks platform, where most of its advanced features first appear. Besides the Spark connector, the delta-rs Rust library and its Python package deltalake allow reading and writing tables without a JVM, and Delta Kernel provides a connector library for other engines. Polars, DuckDB, Trino, Flink, pandas (via deltalake) and several warehouses read Delta tables; UniForm can expose a Delta table with Iceberg-compatible metadata.\n\nDelta Lake suits teams on Databricks or Spark who need reliable incremental pipelines over a lake, and Python users who want transactional writes to Parquet from a laptop through delta-rs. Compared with Iceberg, governance of the project remains closely tied to one vendor, catalog integration is less standardized, and some features depend on the engine implementing the corresponding protocol version.",95  "capabilities": [96    "Transaction log providing ACID commits over Parquet",97    "Schema enforcement and evolution",98    "Time travel and table history",99    "MERGE, UPDATE and DELETE operations",100    "delta-rs Rust/Python implementation without a JVM",101    "UniForm metadata for Iceberg readers"102  ],103  "categories": [104    {105      "slug": "data-catalogs",106      "primary": true107    },108    {109      "slug": "analytics-engines"110    }111  ],112  "execution_modes": [113    "self-hosted",114    "cloud",115    "local"116  ],117  "interfaces": [118    "library"119  ],120  "source_model": "open-source",121  "license_spdx": "Apache-2.0",122  "pricing_model": [123    "open-source",124    "free"125  ],126  "sources": [127    {128      "url": "https://delta.io/",129      "type": "official_site",130      "supports": [131        "name",132        "summary",133        "vendor"134      ],135      "checked_at": "2026-11-02",136      "title": "Delta Lake"137    },138    {139      "url": "https://docs.delta.io/",140      "type": "official_docs",141      "supports": [142        "capabilities",143        "integrations"144      ],145      "checked_at": "2026-11-02",146      "title": "Delta Lake documentation"147    },148    {149      "url": "https://github.com/delta-io/delta/blob/master/LICENSE.txt",150      "type": "license_page",151      "supports": [152        "license_spdx",153        "source_model"154      ],155      "checked_at": "2026-11-02",156      "title": "Delta Lake LICENSE.txt (Apache 2.0)"157    },158    {159      "url": "https://github.com/delta-io/delta",160      "type": "repository",161      "supports": [162        "repo_url"163      ],164      "checked_at": "2026-11-02",165      "title": "delta-io/delta"166    }167  ],168  "confidence": "high"169}170