{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:KDNHC5QKRXD3RFG4JTOLHYIZ3U","short_pith_number":"pith:KDNHC5QK","schema_version":"1.0","canonical_sha256":"50da71760a8dc7b894dc4cdcb3e119dd32498fc73de778f9446ea3fc574756e0","source":{"kind":"arxiv","id":"2605.20500","version":1},"attestation_state":"computed","paper":{"title":"A Multi-Layer Testing Framework for Automated Data Quality Assurance in Cloud-Native ELT Pipelines","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Hassan Reza, Ismail Gargouri","submitted_at":"2026-05-19T21:12:36Z","abstract_excerpt":"Ensuring data quality in cloud-native Extract-Load-Transform (ELT) pipelines is increasingly challenging due to heterogeneous data sources, evolving schemas, and multi-backend execution environments. This paper presents a unified, multi-layer testing framework that integrates orchestration-level validation, declarative dbt tests, large language model (LLM)-generated semantic tests, and cross-store consistency checking between DuckDB and Snowflake, orchestrated through Apache Airflow. Controlled anomaly-injection experiments demonstrate that a manual-only baseline detected 7 of 16 injected anom"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.20500","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-05-19T21:12:36Z","cross_cats_sorted":[],"title_canon_sha256":"7701ef376910fe924b41101b31cd4e444fe0bf5ce1e38736234f0bf6e1dec8b6","abstract_canon_sha256":"42d95a0dc8360b1178851d29f1b18c2a1fc98abb32be079ff198534386e560e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T01:04:39.954625Z","signature_b64":"QIOrBIdIrYVnwvxsKhJVAp2icmr5chSjEM+GDvLUgKc6Td6htow1/5+N4abr7xFNs2KOUwAtw8hpb7uM1twsBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50da71760a8dc7b894dc4cdcb3e119dd32498fc73de778f9446ea3fc574756e0","last_reissued_at":"2026-05-21T01:04:39.954194Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T01:04:39.954194Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Multi-Layer Testing Framework for Automated Data Quality Assurance in Cloud-Native ELT Pipelines","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Hassan Reza, Ismail Gargouri","submitted_at":"2026-05-19T21:12:36Z","abstract_excerpt":"Ensuring data quality in cloud-native Extract-Load-Transform (ELT) pipelines is increasingly challenging due to heterogeneous data sources, evolving schemas, and multi-backend execution environments. This paper presents a unified, multi-layer testing framework that integrates orchestration-level validation, declarative dbt tests, large language model (LLM)-generated semantic tests, and cross-store consistency checking between DuckDB and Snowflake, orchestrated through Apache Airflow. Controlled anomaly-injection experiments demonstrate that a manual-only baseline detected 7 of 16 injected anom"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.20500","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.20500/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.20500","created_at":"2026-05-21T01:04:39.954261+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.20500v1","created_at":"2026-05-21T01:04:39.954261+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.20500","created_at":"2026-05-21T01:04:39.954261+00:00"},{"alias_kind":"pith_short_12","alias_value":"KDNHC5QKRXD3","created_at":"2026-05-21T01:04:39.954261+00:00"},{"alias_kind":"pith_short_16","alias_value":"KDNHC5QKRXD3RFG4","created_at":"2026-05-21T01:04:39.954261+00:00"},{"alias_kind":"pith_short_8","alias_value":"KDNHC5QK","created_at":"2026-05-21T01:04:39.954261+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KDNHC5QKRXD3RFG4JTOLHYIZ3U","json":"https://pith.science/pith/KDNHC5QKRXD3RFG4JTOLHYIZ3U.json","graph_json":"https://pith.science/api/pith-number/KDNHC5QKRXD3RFG4JTOLHYIZ3U/graph.json","events_json":"https://pith.science/api/pith-number/KDNHC5QKRXD3RFG4JTOLHYIZ3U/events.json","paper":"https://pith.science/paper/KDNHC5QK"},"agent_actions":{"view_html":"https://pith.science/pith/KDNHC5QKRXD3RFG4JTOLHYIZ3U","download_json":"https://pith.science/pith/KDNHC5QKRXD3RFG4JTOLHYIZ3U.json","view_paper":"https://pith.science/paper/KDNHC5QK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.20500&json=true","fetch_graph":"https://pith.science/api/pith-number/KDNHC5QKRXD3RFG4JTOLHYIZ3U/graph.json","fetch_events":"https://pith.science/api/pith-number/KDNHC5QKRXD3RFG4JTOLHYIZ3U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KDNHC5QKRXD3RFG4JTOLHYIZ3U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KDNHC5QKRXD3RFG4JTOLHYIZ3U/action/storage_attestation","attest_author":"https://pith.science/pith/KDNHC5QKRXD3RFG4JTOLHYIZ3U/action/author_attestation","sign_citation":"https://pith.science/pith/KDNHC5QKRXD3RFG4JTOLHYIZ3U/action/citation_signature","submit_replication":"https://pith.science/pith/KDNHC5QKRXD3RFG4JTOLHYIZ3U/action/replication_record"}},"created_at":"2026-05-21T01:04:39.954261+00:00","updated_at":"2026-05-21T01:04:39.954261+00:00"}