Updated upgrading to v1.9 guide to included parallel batch execution and added links to incremental microbatch page

website/docs/docs/build/incremental-microbatch.md

@@ -25,7 +25,8 @@ Incremental models in dbt are a [materialization](/docs/build/materializations)
 Microbatch is an incremental strategy designed for large time-series datasets:
 - It relies solely on a time column ([`event_time`](/reference/resource-configs/event-time)) to define time-based ranges for filtering. Set the `event_time` column for your microbatch model and its direct parents (upstream models). Note, this is different to `partition_by`, which groups rows into partitions.
 - It complements, rather than replaces, existing incremental strategies by focusing on efficiency and simplicity in batch processing.
-- Unlike traditional incremental strategies, microbatch doesn't require implementing complex conditional logic for [backfilling](#backfills). 
+- Unlike traditional incremental strategies, microbatch enables you to [reprocess failed batches](/docs/build/incremental-microbatch#retry), auto-detect [parallel batch execution](#parallel-batch-execution), and eliminate the need to implement complex conditional logic for [backfilling](#backfills).
+
 - Note, microbatch might not be the best strategy for all use cases. Consider other strategies for use cases such as not having a reliable `event_time` column or if you want more control over the incremental logic. Read more in [How `microbatch` compares to other incremental strategies](#how-microbatch-compares-to-other-incremental-strategies).
 
 ### How microbatch works

website/docs/docs/dbt-versions/core-upgrade/06-upgrading-to-v1.9.md

@@ -49,6 +49,8 @@ Starting in Core 1.9, you can use the new [microbatch strategy](/docs/build/incr
 - Simplified query design: Write your model query for a single batch of data. dbt will use your `event_time`, `lookback`, and `batch_size` configurations to automatically generate the necessary filters for you, making the process more streamlined and reducing the need for you to manage these details.
 - Independent batch processing: dbt automatically breaks down the data to load into smaller batches based on the specified `batch_size` and processes each batch independently, improving efficiency and reducing the risk of query timeouts. If some of your batches fail, you can use `dbt retry` to load only the failed batches.
 - Targeted reprocessing: To load a *specific* batch or batches, you can use the CLI arguments `--event-time-start` and `--event-time-end`.
+- [Automatic parallel batch execution](/docs/build/incremental-microbatch#parallel-batch-execution): Process multiple batches at the same time, instead of one after the other (sequentially) for faster processing of your microbatch models. dbt intelligently auto-detects if your batches can run in parallel, while also allowing you to manually override parallel execution with the `concurrent_batches` config.
+
 
 Currently microbatch is supported on these adapters with more to come:
  * postgres