[SPARK-54576][SQL] Add documentation for new Datasketches-based aggregate functions

[dtenedor]

What changes were proposed in this pull request?

This PR adds comprehensive documentation for Spark SQL's sketch-based approximate functions powered by the Apache DataSketches library. The new documentation page (sql-ref-sketch-aggregates.md) covers:

Function Reference:

• HyperLogLog (HLL) Sketch Functions: hll_sketch_agg, hll_union_agg, hll_sketch_estimate, hll_union
• Theta Sketch Functions: theta_sketch_agg, theta_union_agg, theta_intersection_agg, theta_sketch_estimate, theta_union, theta_intersection, theta_difference
• KLL Quantile Sketch Functions: kll_sketch_agg_*, kll_sketch_to_string_*, kll_sketch_get_n_*, kll_sketch_merge_*, kll_sketch_get_quantile_*, kll_sketch_get_rank_*
• Approximate Top-K Functions: approx_top_k_accumulate, approx_top_k_combine, approx_top_k_estimate

Best Practices:

• Guidance on choosing between HLL and Theta sketches
• Accuracy vs. memory trade-offs for each sketch type
• Tips for storing and reusing sketches

Common Use Cases and Examples:

• Tracking daily unique users with HLL sketches (ETL workflow)
• Computing percentiles over time with KLL sketches
• Set operations with Theta sketches (intersection, difference for cohort analysis)
• Finding trending items with Top-K sketches

The PR also adds links to this new documentation page from:

• sql-ref-functions.md (under Aggregate-like Functions)
• sql-ref.md (under Functions section)
• _data/menu-sql.yaml (navigation menu)

Why are the changes needed?

Spark SQL has added several sketch-based approximate functions using the Apache DataSketches library (HLL sketches in 3.5.0, Theta/KLL/Top-K sketches in 4.1.0), but there was no comprehensive documentation explaining:

• How to use these functions together in practical ETL workflows
• How to store sketches and merge them across multiple data batches
• Best practices for choosing the right sketch type and tuning accuracy parameters

This documentation fills that gap and helps users understand the full power of sketch-based analytics in Spark SQL.

Does this PR introduce any user-facing change?

Yes, this PR adds new documentation pages that are user-facing. No code changes are included.

How was this patch tested?

Documentation-only change. The examples were verified against the existing function implementations and test cases in the codebase.

Was this patch authored or co-authored using generative AI tooling?

Yes, code assistance with claude-4.5-opus-high in combination with manual editing by the author.

[dtenedor]

cc @cboumalh I added some Spark documentation for the new Datasketches based aggregate functions we have so far. We can maybe keep extending this as well as we add new functions later.