- C-Store (column-oriented database storage and processing): http://db.lcs.mit.edu/projects/cstore/vldb.pdf and https://sookocheff.com/post/databases/c-store/
- BigQuery (the original Dremel-based query engine): https://cloud.google.com/files/BigQueryTechnicalWP.pdf
- Snowflake paper: https://www.snowflake.com/resource/sigmod-2016-paper-snowfla...
- Andy Pavlo's CMU distributed OLAP databases lecture: https://www.youtube.com/watch?v=dPMc7FZ3Gqo&t=1s
- MemSQL architecture: https://www.youtube.com/watch?v=HoompXCG5Mk
Start there and just read more about architecture of the various products. You'll learn the basics of distributed nodes that store data in column-oriented tables partitioned by a key and how to run queries over them using various performance techniques. Then you can dive into the unique differences of the various databases.
1. http://highscalability.com/
2. https://softwareengineeringdaily.com/
Other than that, reading the blogs of the various vendors is how I keep up (using Feedly with RSS). The modern projects are Redshift, BigQuery, Snowflake, Azure DW, MemSQL, Clickhouse, YellowBrick with older projects being Vertica, Teradata, Greenplum. It's also useful to follow the "new" distributed SQL projects like CockroachDB, Citus, TiDB, Vitess, Yugabyte.
Druid has a slight edge in data scale-out and indexed seeks but modern data warehouses are adding similar tiering features, along with with field indexing, full-text search, nested data records, and even in-memory rowstores for OLTP support.
They're all converging on the same feature set and eventually Druid will just become another data warehouse option, although I'd still recommend Clickhouse or MemSQL at that point.