anchor.diff:  There are many other aggregations capabilities that we won't go into detail here. The {ref}/search-aggregations.html[aggregations reference guide] is a great starting point if you want to do further experimentation.
   
   [[getting-started-next-steps]]
  -=== Where to go from here
  +== Where to go from here
   
   Now that you've set up a cluster, indexed some documents, and run some
   searches and aggregations, you might want to:
  
positive.code: 
  [float]
  [[getting-started-batch-processing]]
  ==== Batch processing
  
  In addition to being able to index, update, and delete individual documents, Elasticsearch also provides the ability to perform any of the above operations in batches using the {ref}/docs-bulk.html[`_bulk` API]. This functionality is important in that it provides a very efficient mechanism to do multiple operations as fast as possible with as few network roundtrips as possible.
  
  
negative.code: In addition to the `match_all`, `match`, `bool`, and `range` queries, there are a lot of other query types that are available and we won't go into them here. Since we already have a basic understanding of how they work, it shouldn't be too difficult to apply this knowledge in learning and experimenting with the other query types.
  
  [[getting-started-aggregations]]
  == Analyze results with aggregations
  
  Aggregations provide the ability to group and extract statistics from your data. The easiest way to think about aggregations is by roughly equating it to the SQL GROUP BY and the SQL aggregate functions. In Elasticsearch, you have the ability to execute searches returning hits and at the same time return aggregated results separate from the hits all in one response. This is very powerful and efficient in the sense that you can run queries and multiple aggregations and get the results back of both (or either) operations in one shot avoiding network roundtrips using a concise and simplified API.
  
  