Probabilistic Streaming Data Sketches // Provides an interface to the 'Apache DataSketches' (https://datasketches.apache.org/) library of streaming algorithms for approximate analytics on data too large to hold or process exactly. Sketches are compact, mergeable summaries built in a single pass over a stream that answer queries such as approximate distinct counts, quantiles and ranks, frequent items and point-frequency estimates, weighted sampling, and set membership with mathematically proven error bounds. Implements Karnin-Lang- Liberty (KLL), Relative Error Quantiles (REQ), t-Digest, HyperLogLog (HLL), Compressed Probabilistic Counting (CPC), Theta, Frequent Items, Count-Min, Array of Doubles, Variance Optimal (VarOpt), Exact and Bounded Probabilistic Proportional- to-Size (EBPPS), and Bloom filter sketches, with native serialization for interoperability with other 'Apache DataSketches' implementations.