data modeling

Database pioneer Michael Stonebraker argues that 'big data' is really at least four different problems: big volumes with small analytics, big analytics, velocity, and variety. He comments on where each market is heading.

Jon Gjengset's 2020 PhD dissertation presentation at MIT on partial state in dataflow-based materialized views, the idea behind the Noria database. The Q&A is cut out.

Linus Tech Tips documents a major RAID failure on its Whonnock server and the attempt to recover the lost data with a data recovery firm.

Martin Kleppmann argues that databases are global shared mutable state and proposes treating them as an ever-growing log of immutable facts. Derived data such as caches, indexes and views is then built by stream processors like Apache Samza. The talk draws on Kafka and LinkedIn's experience with commit logs.

Michael Arntzenius's Strange Loop talk on Datafun, a functional query language that brings database-style querying into a general-purpose language. It discusses bridging database theory and programming languages.

A guest lecture Jon Gjengset gave at Two Sigma on using Rust for large, high-performance systems. It covers why Rust suits such projects, the Noria research database, a concurrent data structure used in it, and practical experience.

Martin Kleppmann's Kafka Summit 2018 keynote asks whether Kafka can be considered a database. He covers event logs and related data-systems ideas.

Richard Feldman's elm-conf talk on designing models so that bugs from impossible application states cannot occur, using Elm's compiler and types. It demonstrates techniques for making invalid states unrepresentable.

John Oliver's Last Week Tonight segment explains how data brokers collect and sell personal information, including a stunt in which his team bought data to identify members of Congress.

Lee Byron of Facebook explains how persistent immutable data structures work and how to use them in React apps with Immutable.js. He covers memoization benefits and avoiding coupling through shared mutable state.

Benn Jordan investigates infrasound and low-frequency noise from data centers, including xAI's Colossus and a crypto mine, and reports his own experiments on the effects. The video covers the health and community impacts on nearby residents.

Peter Norvig's 2007 Google talk on theorizing from data, covering what huge datasets can do for language understanding, translation, information extraction and inference. It argues that more data often beats cleverer models.

A Business Insider investigation into the US boom in AI data centers, mapping where they are, who owns them, and how much power and water they consume. It looks at the costs borne by communities, with a focus on Virginia.

Scott Manley examines why SpaceX, Google and others are looking at orbital data centers. He walks through the economics, heat rejection and solar power arguments, with links to the Lumen/Starcloud and Google Suncatcher papers.

Scott Manley explains the thermal balance of spacecraft and how a satellite could be configured to radiate away the heat from datacenter-scale computing in space. His rough numbers suggest it is feasible.

Raymond Hettinger explains Python's dataclasses module, showing the problem it solves and its key design decisions. He places it in a progression from tuples and dicts through named tuples and attrs.

James Hamilton of AWS gives a Velocity 2010 keynote on datacenter infrastructure innovation, focused on power and cooling costs and a breakdown of where datacenter spending goes.

Rick Houlihan's AWS re:Invent 2018 deep dive on advanced DynamoDB design patterns. It covers index overloading, GSI sharding, composite keys, graph processing and transactions.

SQLite creator Richard Hipp gives a history of SQLite and explains where it shines as a database at the edge of the network with no DBA. He covers long-term support, testing and how it is used in products.

Lukas Eder's Devoxx talk on how modern cost-based SQL optimizers rewrite and plan queries in ways developers would not expect. It argues that declarative SQL, used well, can run complex queries very fast.

Bryan Cantrill presents Manta, an object store with in-situ compute that lets tools like grep, awk, and sed run map-reduce style over huge data. He frames it as bringing the Unix philosophy to big data.

A Clojure/conj 2019 talk by Wilker Silva on how namespace-qualified keywords and context-free federated names can be used pragmatically for data modeling, drawn from his experience at Nubank. It builds toward a graph-based view of connecting data from many sources.

An AWS re:Invent 2019 session for advanced DynamoDB users on design patterns used by large customers, including Amazon retail. It covers GSI sharding, index overloading, graph processing, composite keys and transactional workflows.

Zig creator Andrew Kelley's RedisConf 2020 talk on modeling data flow with asynchronous I/O in Zig. It explains how Zig's async/await avoids function coloring, and Loris Cro demonstrates OkRedis, a zero-allocation Redis client.

Martin Fowler's GOTO 2012 introduction to NoSQL databases: where they came from, their data models, how consistency thinking differs, and when to use them. He argues they won't make relational databases obsolete and introduces polyglot persistence.

Peter Alvaro's Strange Loop 2015 talk argues that query languages let you state what you mean, and applies that to distributed programming. It covers Datalog, Dedalus and the Bloom lineage for reasoning about distributed systems.

A 49-minute demo of Palantir Foundry by its chief architect. It walks through data integration, ML and AI workflows, analytics, and application building across the platform.

Andrew Kelley, creator of Zig, presents practical data-oriented design techniques for shrinking memory footprint and making programs cache friendly. He ends with a case study of how these ideas sped up the Zig compiler.

Zoz's DEF CON 23 talk explores practical, often explosive ways to destroy data and hard drives quickly. It tests methods such as thermite and other physical techniques for rapid destruction.

A Philosophy Tube video essay on data, surveillance and the algorithms built on personal information, touching on Cambridge Analytica and the 'nothing to hide' argument.

Chandler Carruth's CppCon 2016 talk on designing hybrid data structures for performance. He covers cache locality, working-set size and allocation overhead, and why standard C++ containers fall short.

Martin Kleppmann's Strange Loop talk on database transactions, covering why they were dropped by NoSQL, their reputation for being slow, and newer algorithms that make serializable transactions perform and scale. It touches on consistency, linearizability and the CAP theorem.

Dave Chinner's linux.conf.au 2012 talk on XFS scalability, covering pain points with large numbers of files and complex metadata, and the work to fix XFS metadata performance.

An AWS re:Invent 2018 session on how DynamoDB was built as a hyper-scale NoSQL database. It discloses underpinnings of the service and how it is run for over 100,000 customers.

Ahmon Dancy of Franz Inc explains low-level programming in Common Lisp, including off-heap data structures used in AllegroGraph to manage memory outside the garbage collector.

A 2006 Google TechTalk in which SQLite's author Richard Hipp introduces the embeddable, zero-configuration C database library. He covers its history, strengths and weaknesses, and where it beats client/server databases.

Scott Wlaschin's NDC talk on using statically typed functional programming, F# in particular, to model a business domain. Types are used to represent the domain precisely and to encode business rules so that illegal states cannot be built, with the compiler acting as an instant unit test.

Overclocker der8auer tours Hetzner's Falkenstein data center in Germany, showing custom racks, own mainboards, passively cooled servers and ATX-style cases on shelves.

Journalist Matthew Cole's Black Hat 2013 talk on the CIA's operational security failures in the 2003 Milan kidnapping. Phone and location metadata exposed the team and led to arrest warrants.

Peter Norvig, then Director of Research at Google, talks at Startup School 2008 about leveraging large amounts of data and about technical challenges in Google projects such as image search. A central idea is that data can beat hand-written programming.