Data Engineering Whitepapers

Last updated Jan 17, 2025

A curated list of influential whitepapers in the field of data engineering.

• Data Lakehouse: Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics ^6a7f75
• Data Catalog: Ground: A Data Context Service
• Apache Spark: Spark: Cluster Computing with Working Sets
• Data Engineering Architecture: The Google File System
• Streaming: The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing
• Google File System (GFS): The Google File System ^fdbf43
• MapReduce: MapReduce: Simplified Data Processing on Large Clusters
• Data Warehousing: Dremel: Interactive Analysis of Web-Scale Datasets
• Data Mesh: How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh
• DuckDB: MotherDuck: DuckDB in the cloud and in the client- A paper that introduces the 1-5-Tier Architecture. ^9f7136
  • Morsel-Driven Parallelism: A NUMA-Aware Query Evaluation Framework for the Many-Core Age
  • Unnesting Arbitrary Queries: Unnest subqueries queries in SQL.
• Analytics Development Lifecycle (ADLC): The Analytics Development Lifecycle (ADLC) by dbt
• Vectorized Engine: Everything You Always Wanted to Know About Compiled and Vectorized Queries But Were Afraid to Ask. ^db4178
• Redshift Files: Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet ^2fbdb2
• Dremel Encoding: Dremel: Interactive Analysis of Web-Scale Datasets used in Apache Parquet. ^51fc46
• Bluesky: The AT Protocol: Usable Decentralized Social Media with Martin Kleppmann ^039de7
• HILDA: Human-in-the-Loop Data Analysis: A Personal Perspective ^678997
• Schema Evolution:
  • 1995: A survey of schema versioning issues for database systems by John F. Roddick ^eb6505
  • 2012: Automating the database schema evolution process
• NoSQL
  • Bigtable: A Distributed Storage System for Structured Data, 2006
  • Dynamo: Amazon’s Highly Available Key-value Store, 2007

• LLM:
  • GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models ^9b7681
    • More on Limitations of LLMs
  • Knowledge Graphs as a source of trust for LLM-powered enterprise question answering by Juan Sequeda, Dean Allemang, Bryon Jacob
• Anthropic: Constitutional AI: Harmlessness from AI Feedback
• Rest meets react: self-improvement for multi-step reasoning llm agent by Google
• PaLM 2 Technical Report - Google - Demonstrates advanced capabilities in processing structured data and numerical reasoning
• RAG: Retrieval-Augmented Generation for Large Language Models: A Survey

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Created 2024-01-05