A perfect task for an AI agent, BTW.

Why not to Scihub?

    1. You do not talk about Sci-Hub.
    2. You do NOT talk about Sci-Hub.
    3. If a download says "Stop," goes limp,
       or taps out, that download is over. 
    4. Only two tries per mirror. 
    5. One download at a time. 
    6. Shirt and shoes optional. 
    7. Downloads will continue until publicly funded
       research is widely distributed. 
    8. If this is your first time at Sci-Hub, you
       have to download something interesting,
       actually read at least part of it, learn
       something, and then fight ignorance and/or
       stupidity with it.

Wonder why. Did they confuse this with Maoist literature (Little Red Book)?

Peek:

Readings in Database Systems (commonly known as the "Red Book") has offered readers an opinionated take on both classic and cutting-edge research in the field of data management since 1988. Here, we present the Fifth Edition of the Red Book — the first in over ten years. CHAPTERS Preface [HTML] [PDF] Background introduced by Michael Stonebraker [HTML] [PDF] Traditional RDBMS Systems introduced by Michael Stonebraker [HTML] [PDF] Techniques Everyone Should Know introduced by Peter Bailis [HTML] [PDF] New DBMS Architectures introduced by Michael Stonebraker [HTML] [PDF] Large-Scale Dataflow Engines introduced by Peter Bailis [HTML] [PDF] Weak Isolation and Distribution introduced by Peter Bailis [HTML] [PDF] Query Optimization introduced by Joe Hellerstein [HTML] [PDF] Interactive Analytics introduced by Joe Hellerstein [HTML] [PDF] Languages introduced by Joe Hellerstein [HTML] [PDF] Web Data introduced by Peter Bailis [HTML] [PDF] A Biased Take on a Moving Target: Complex Analytics by Michael Stonebraker [HTML] [PDF] A Biased Take on a Moving Target: Data Integration by Michael Stonebraker [HTML] [PDF] Complete Book: [HTML] [PDF] Readings Only: [HTML] [PDF] Previous Editions: [HTML]

There are a lot of red books: https://en.wikipedia.org/wiki/Red_Book

Redbook is also the Audio CD standard. Lots of redbooks exist.

LLMs as DBs (if you squint hard enough)

The object storage stuff is new, but it's mostly confirmed that the older architecture works. MPP with shared (S3) storage and everything above that on local SSD and compute delivers the best performance. Even Snowflake finally came out with "interactive" warehouses with this architecture.

Parquet, Iceberg, and other open formats seem good, but they may hit a complexity wall. There's already some inconsistency between platforms, eg with delete vectors.

Incremental view maintenance interests me as well, and I would like to see it more available on different platforms. It's ironic that people use dbt etc. to test every little edit of their manually coded delta pipelines, but don't look at IVM.

Definitely they should include D4M and GraphQL [1],[2].

Not only D4M can cater for structured relational data, it's also suitable for non-structured and sparse data in spreadsheet, matrices and graph. It's essentially a generalization of SQL but for all things data.

There's also integration of D4M with SciDB [3].

[1] D4M: Dynamic Distributed Dimensional Data Model:

https://d4m.mit.edu/

[2] GraphQL:

https://graphql.org/

[3] D4M: Bringing associative arrays to database engines:

https://arxiv.org/abs/1508.07371