For bounded data, how do I get Flink to "trigger" once flatmap has finished outputting all its data

Question

I've explicitly set "batch mode" in Flink's StreamExecutionEnvironmen settings, as I'm working with bounded data.

The bounded data passes through a flatmap; and the flatmap is windowed using GlobalWindows. Since the data is bounded, there is a FINITE (though initially unknown) number of elements that will be outputted by the Collection.out() operations in the FlatMap. I'd like to trigger a Reduce() function. However, I can't figure out how to tell Flink the following: once the FlatMap has finished outputting all its elements, proceeed with the remainder of the code, eg do the reduce. (From the documentation, GlogalWindows always use the NeverTrigger, so I need to explicitly call a trigger I presume.) (Note: The CountTrigger won't work I believe, since I don't know apriori the number of elements that the flatmap will output.)

Bonus: Technically, the reduce operation can start as soon as the flatmap starts outputting its output. So, I'm not sure exactly how Flink works, but ideally, the reduce starts right away but only "completes" after the window closes....(And the window should close, in the case of bounded data, once the flatmap stops outputting the the output data.)

=== Edit #1:

Per @kkrugler, here's the skeleton code:

sosCleavedFeaturesEtc
    .flatMap((Tuple4<Float2FloatAVLTreeMap, List<ImmutableFeatureV2>, List<ImmutableFeatureV2>, Integer> tuple4, Collector<Tuple4<Float2FloatAVLTreeMap, List<ImmutableFeatureV2>, Integer, Integer>> out) -> {
        ...
        IntStream.range(0, numBlocksForClustering + 1)
        .forEach(blockIdx -> out.collect(Tuple4.of(rtMapper, unmodifiableLstCleavedFeatures, diaWindowNum, blockIdx)));
    })
    .flatMap((Tuple4<Float2FloatAVLTreeMap, List<ImmutableFeatureV2>, Integer, Integer> tuple4, Collector<Tuple2<Float2FloatAVLTreeMap, Cluster>> out) -> {
        ...
        setClusters
            .stream()
            .filter(cluster -> cluster.getClusterSize() >= minFeaturesInCluster)
            .forEach(e -> out.collect(Tuple2.of(rtMapper, e)));
    })
    .map(tuple -> {
        ...
    })
    .filter(repFeature -> {
        ...
    })
    .windowAll(GlobalWindows.create())
        ...trigger??...
    .aggregate(...});