spb/tools-spboucher-ai

1{2  "aliases": [3    "Iceberg",4    "PyIceberg"5  ],6  "workflows": [7    "Shared lakehouse tables read by Trino and written by Spark",8    "Incremental ingestion with snapshot-based change reads",9    "Reading a governed table from Python via PyIceberg and Arrow"10  ],11  "advantages": [12    "Vendor-neutral format with broad engine support",13    "Transactions and time travel on plain object storage",14    "Partition changes without data migration"15  ],16  "limitations": [17    "Requires a catalog service and maintenance jobs",18    "Overkill for single-user local analytics",19    "Write support in lightweight engines is still partial"20  ],21  "prerequisites": [22    "Object or file storage plus a catalog; an engine or PyIceberg client"23  ],24  "audiences": [25    "Data platform engineers",26    "Analytics teams sharing lake data across engines"27  ],28  "alternatives": [29    "delta-lake",30    "apache-parquet",31    "duckdb",32    "apache-spark"33  ],34  "tags": [35    "lakehouse",36    "table-format",37    "parquet",38    "open-source",39    "data-lake"40  ],41  "use_cases": [42    "query-parquet-files",43    "govern-lake-data"44  ],45  "operating_systems": [46    "any"47  ],48  "deployment_models": [49    "on-premises",50    "managed-cloud"51  ],52  "integrations": [53    "Apache Spark",54    "Trino",55    "Apache Flink",56    "DuckDB",57    "ClickHouse",58    "Snowflake",59    "AWS Glue",60    "Apache Polaris"61  ],62  "data_formats": [63    "Parquet",64    "ORC",65    "Avro",66    "Iceberg metadata (JSON/Avro)"67  ],68  "languages": [69    "Java",70    "Python",71    "Rust",72    "Go"73  ],74  "self_hostable": true,75  "discovery_method": "editorial-research",76  "depth": "standard",77  "editorial_status": "reviewed",78  "visibility": "public",79  "featured": false,80  "author": "batch-D",81  "slug": "apache-iceberg",82  "name": "Apache Iceberg",83  "vendor": {84    "name": "Apache Software Foundation",85    "slug": "apache-software-foundation",86    "website": "https://www.apache.org/"87  },88  "homepage_url": "https://iceberg.apache.org/",89  "docs_url": "https://iceberg.apache.org/docs/latest/",90  "repo_url": "https://github.com/apache/iceberg",91  "license_url": "https://github.com/apache/iceberg/blob/main/LICENSE",92  "product_type": "specification",93  "product_status": "active",94  "summary": "Open table format for data lakes that adds ACID snapshots, schema and partition evolution and time travel over Parquet, ORC or Avro files",95  "description": "Apache Iceberg is an open table format maintained by the Apache Software Foundation under Apache 2.0. It defines metadata (table metadata files, manifest lists and manifests) that tracks which Parquet, ORC or Avro data files make up each snapshot of a table, so that engines can commit atomic changes, read a consistent snapshot, travel back in time, evolve schemas and change partitioning without rewriting data. Hidden partitioning lets queries filter on raw columns while the layout stays an implementation detail. The REST catalog specification standardizes how engines discover and update tables, and catalogs such as Apache Polaris, Nessie, AWS Glue and vendor offerings implement it.\n\nIceberg is supported by Spark, Trino, Flink, DuckDB, ClickHouse, Snowflake, BigQuery, Athena, Dremio and PyIceberg, the Python client used to read tables into Arrow or write small batches. It is the usual answer when several engines must share one governed copy of lake data. The cost is metadata and catalog operations to run and maintain, compaction and snapshot expiration jobs to schedule, and the fact that single-user analytics on local Parquet rarely needs it.",96  "capabilities": [97    "Atomic snapshots and serializable commits over object storage",98    "Schema evolution and partition evolution without rewrites",99    "Hidden partitioning and time travel",100    "Manifest-based file pruning with column statistics",101    "REST catalog specification",102    "Support in Spark, Trino, Flink, DuckDB, ClickHouse and PyIceberg"103  ],104  "categories": [105    {106      "slug": "data-catalogs",107      "primary": true108    },109    {110      "slug": "analytics-engines"111    }112  ],113  "execution_modes": [114    "self-hosted",115    "cloud"116  ],117  "interfaces": [118    "library",119    "api"120  ],121  "source_model": "open-source",122  "license_spdx": "Apache-2.0",123  "pricing_model": [124    "open-source",125    "free"126  ],127  "sources": [128    {129      "url": "https://iceberg.apache.org/",130      "type": "official_site",131      "supports": [132        "name",133        "summary"134      ],135      "checked_at": "2026-11-02",136      "title": "Apache Iceberg"137    },138    {139      "url": "https://iceberg.apache.org/docs/latest/",140      "type": "official_docs",141      "supports": [142        "capabilities",143        "integrations"144      ],145      "checked_at": "2026-11-02",146      "title": "Iceberg documentation"147    },148    {149      "url": "https://github.com/apache/iceberg/blob/main/LICENSE",150      "type": "license_page",151      "supports": [152        "license_spdx",153        "source_model"154      ],155      "checked_at": "2026-11-02",156      "title": "Iceberg LICENSE (Apache 2.0)"157    },158    {159      "url": "https://github.com/apache/iceberg",160      "type": "repository",161      "supports": [162        "repo_url"163      ],164      "checked_at": "2026-11-02",165      "title": "apache/iceberg"166    }167  ],168  "confidence": "high"169}170