anchor.diff:  TIP: The sample data sets include summarized data. For example, the `total`
   value is a sum of the requests that were received by a specific service at a
   particular time. If your data is stored in Elasticsearch, you can generate
  -this type of sum or average by using search queries. One of the benefits of
  +this type of sum or average by using aggregations. One of the benefits of
   summarizing data this way is that Elasticsearch automatically distributes
   these calculations across your cluster. You can then feed this summarized data
  -into the {xpack} {ml} features instead of raw results, which reduces the volume
  +into {xpack} {ml} instead of raw results, which reduces the volume
   of data that must be considered while detecting anomalies. For the purposes of
  -this tutorial, however, these summary values are provided directly in the JSON
  -source files. They are not generated by Elasticsearch queries.
  +this tutorial, however, these summary values are stored in Elasticsearch,
  +rather than created using the {ref}/search-aggregations.html[_aggregations framework_].
  +//TBD link to working with aggregations page
   
   Before you load the data set, you need to set up {ref}/mapping.html[_mappings_]
   for the fields. Mappings divide the documents in the index into logical groups
  
positive.code: === Identifying Data for Analysis
  
  For the purposes of this tutorial, we provide sample data that you can play with.
  When you consider your own data, however, it's important to take a moment
  and think about where the {xpack} {ml} features will be most impactful.
  
  The first consideration is that it must be time series data.
  Generally, it's best to use data that is in chronological order. When the data
  feed occurs in ascending time order, the statistical models and calculations are
  very efficient and occur in real-time.
  //TBD: Talk about handling out of sequence data?
  
  The second consideration, especially when you are first learning to use {ml},
  is the importance of the data and how familiar you are with it. Ideally, it is
  
negative.code: analytical task.
  
  NOTE: This tutorial uses Kibana to create jobs and view results, but you can
  alternatively use APIs to accomplish these tasks.
  For API reference information, see <<ml-apis>>.
  
  To work with jobs in Kibana:
  