Data Engineering papers/articles

It’s quite common to find folks sharing papers on minor breakthroughs in areas like NLP, Computer Vision, Machine Learning, Deep Learning, and related fields here.

We’ve carefully gathered a collection of papers focused on databases, distributed systems, and data in general. We’ve explored the latest developments in these areas and gained valuable insights from the many articles in our list.

Some of these publications were suggested to us after we made our list public, adding to the richness of our curated collection.

• MapReduce: Simplified Data Processing on Large Clusters
• Spark: Cluster Computing with Working Sets
• Kafka: a Distributed Messaging System for Log Processing
• Dremel: Interactive Analysis of Web-Scale Datasets: A paper describing the technology behind Google BigQuery
• Procella: Unifying serving and analytical data at YouTube
• The Log: What every software engineer should know about real-time data’s unifying abstraction: Not a paper but an article from one of the Kafka creators. He explains the basic data structure, key for many databases, and modern distributed systems.
• Making reliable distributed systems in the presence of software errors
• Conflict-free Replicated Data Types (CRDT)
• Delta State Replicated Data Types
• Time, Clocks and the Ordering of Events in a Distributed System
• Dynamo: Amazon’s Highly Available Key-value Store
• Linearizability: A Correctness Condition for Concurrent Objects
• Space/Time Trade-offs in Hash Coding with Allowable Errors: Not especially interesting reading but had to mention Bloom Filters and this is the original paper. Perhaps the most surprising data structure I discovered working with data.
• Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems: Not a paper but a book. THE book for anyone interested in databases, data, and distributed systems.
• Naiad: A Timely Dataflow System: Precursor paper of Materialize
• The Snowflake Elastic Data Warehouse
• The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing