spb/tools-spboucher-ai

1{2  "aliases": [3    "Spark",4    "PySpark",5    "Spark SQL"6  ],7  "workflows": [8    "Large-scale ETL into a lakehouse",9    "Streaming aggregation of event data from Kafka",10    "Distributed feature engineering and model training"11  ],12  "advantages": [13    "Scales from a laptop to thousands of nodes",14    "Unified batch, streaming and ML APIs",15    "Wide managed-service availability"16  ],17  "limitations": [18    "JVM startup and configuration overhead for small data",19    "Shuffle and memory tuning require expertise",20    "Python UDFs incur serialization costs"21  ],22  "prerequisites": [23    "Java runtime; Python for PySpark; a cluster manager for distributed runs"24  ],25  "audiences": [26    "Data engineers",27    "ML engineers on large datasets"28  ],29  "alternatives": [30    "duckdb",31    "polars",32    "trino",33    "dask",34    "ray",35    "clickhouse"36  ],37  "tags": [38    "distributed-computing",39    "sql",40    "parquet",41    "streaming",42    "open-source",43    "python"44  ],45  "use_cases": [46    "transform-data",47    "query-parquet-files",48    "stream-events"49  ],50  "operating_systems": [51    "linux",52    "macos",53    "windows"54  ],55  "deployment_models": [56    "on-premises",57    "managed-cloud"58  ],59  "integrations": [60    "Databricks",61    "Delta Lake",62    "Apache Iceberg",63    "Apache Kafka",64    "Kubernetes",65    "Apache Airflow",66    "Jupyter"67  ],68  "data_formats": [69    "Parquet",70    "ORC",71    "JSON",72    "CSV",73    "Avro",74    "Delta Lake",75    "Iceberg"76  ],77  "languages": [78    "Scala",79    "Java",80    "Python",81    "R",82    "SQL"83  ],84  "self_hostable": true,85  "discovery_method": "editorial-research",86  "depth": "standard",87  "editorial_status": "reviewed",88  "visibility": "public",89  "featured": false,90  "author": "batch-D",91  "slug": "apache-spark",92  "name": "Apache Spark",93  "vendor": {94    "name": "Apache Software Foundation",95    "slug": "apache-software-foundation",96    "website": "https://www.apache.org/"97  },98  "homepage_url": "https://spark.apache.org/",99  "docs_url": "https://spark.apache.org/docs/latest/",100  "repo_url": "https://github.com/apache/spark",101  "license_url": "https://github.com/apache/spark/blob/master/LICENSE",102  "product_type": "framework",103  "product_status": "active",104  "summary": "Distributed data processing engine with DataFrame and SQL APIs in Python, Scala, Java and R, plus structured streaming and MLlib",105  "description": "Apache Spark is a distributed computing engine maintained by the Apache Software Foundation under Apache 2.0. Programs express transformations on DataFrames or SQL tables; the Catalyst optimizer and Tungsten execution layer plan them into stages executed by executors across a cluster managed by Kubernetes, YARN or Spark's standalone manager. Spark SQL reads Parquet, ORC, JSON, CSV, JDBC sources and lakehouse tables (Delta Lake, Iceberg, Hudi); Structured Streaming processes Kafka and file streams with the same API; MLlib and GraphFrames cover machine learning and graph computation. Spark 4 added Spark Connect, a thin client protocol that lets Python, Scala, Go or Rust applications drive a remote cluster, together with ANSI SQL mode by default and a Python data source API.\n\nSpark is the standard engine for large-scale ETL and the foundation of Databricks, Amazon EMR, Google Dataproc and Microsoft Fabric. For data that fits on one machine it is slower to start and heavier to configure than DuckDB or Polars, the JVM and shuffle tuning take expertise, and PySpark user-defined functions pay serialization costs unless written with Arrow-based pandas UDFs.",106  "capabilities": [107    "DataFrame and SQL APIs with the Catalyst optimizer",108    "Structured Streaming over Kafka and files",109    "MLlib machine-learning library",110    "Connectors for Parquet, ORC, JSON, JDBC, Delta Lake, Iceberg and Hudi",111    "Spark Connect remote client protocol",112    "Runs on Kubernetes, YARN or standalone clusters"113  ],114  "categories": [115    {116      "slug": "analytics-engines",117      "primary": true118    },119    {120      "slug": "distributed-computing"121    },122    {123      "slug": "etl-elt"124    }125  ],126  "execution_modes": [127    "self-hosted",128    "cloud",129    "local"130  ],131  "interfaces": [132    "library",133    "cli",134    "api"135  ],136  "source_model": "open-source",137  "license_spdx": "Apache-2.0",138  "pricing_model": [139    "open-source",140    "free"141  ],142  "sources": [143    {144      "url": "https://spark.apache.org/",145      "type": "official_site",146      "supports": [147        "name",148        "summary"149      ],150      "checked_at": "2026-11-02",151      "title": "Apache Spark"152    },153    {154      "url": "https://spark.apache.org/docs/latest/",155      "type": "official_docs",156      "supports": [157        "capabilities",158        "languages",159        "data_formats"160      ],161      "checked_at": "2026-11-02",162      "title": "Spark documentation"163    },164    {165      "url": "https://github.com/apache/spark/blob/master/LICENSE",166      "type": "license_page",167      "supports": [168        "license_spdx",169        "source_model"170      ],171      "checked_at": "2026-11-02",172      "title": "Spark LICENSE (Apache 2.0)"173    },174    {175      "url": "https://github.com/apache/spark",176      "type": "repository",177      "supports": [178        "repo_url"179      ],180      "checked_at": "2026-11-02",181      "title": "apache/spark"182    }183  ],184  "confidence": "high"185}186