Scaling data from big to enormous presents three main challenges: handling increasing data size, updating data efficiently, and ensuring scalability. To address scalability, the document proposes using in-memory rollups, a partially ordered set for indexing, and allowing joins at any point in a snowflake dimension to enable non-collapsed aggregates across large datasets without blocking or locks.

1. Scaling Mondrian
From Big Data to Ludicrous Data

2. First challenge: The size of the data

3. Second challenge: Updating the data
Nothing fancy here.
- Apply deltas on cache
- Programmatic roles

4. Third challenge: Scalability
Synchronize without locks and blocks

5. Third challenge: Scalability
In-memory rollups
Gender Country Sales
M USA 7
M CANADA 8
F USA 4
F CANADA 2
Country Sales
USA 11
CANADA 10

6. Third challenge: Scalability
Indexing problem
- Rollups are a combinatory problem with exponential growth of complexity
- Huge caches of segments.
- O(n) isn't good enough.

7. Third challenge: Scalability
Indexing solution
- The PartiallyOrderedSet.
A hierarchical java.util.Set implementation where elements might or
might not be related to one another.

8. Third challenge: Scalability
Non collapsed snowflaked aggregates
- Join at any point in a snowflake dimension

9. Resources
Press release
http://www.informationweek.com/development/database/yahoo-and-hadoop-in-it-for-the-long-term/240002133