Merge pull request MicrosoftDocs#21123 from weitzhandler/patch-2

rjagiewich · web-flow · commit ee05c0a8de87 · 2018-12-21T10:07:58.000-06:00
Updated syntax
diff --git a/articles/cosmos-db/modeling-data.md b/articles/cosmos-db/modeling-data.md
@@ -67,7 +67,7 @@ Now let's take a look at how we would model the same data as a self-contained en
         ] 
     }
 
-Using the approach above we have now **denormalized** the person record where we **embedded** all the information relating to this person, such as their contact details and addresses, in to a single JSON document.
+Using the approach above we have now **denormalized** the person record where we **embedded** all the information relating to this person, such as their contact details and addresses, into a single JSON document.
 In addition, because we're not confined to a fixed schema we have the flexibility to do things like having contact details of different shapes entirely. 
 
 Retrieving a complete person record from the database is now a single read operation against a single collection and for a single document. Updating a person record, with their contact details and addresses, is also a single write operation against a single document.
@@ -168,7 +168,7 @@ Take this JSON snippet.
         ]
     }
 
-This could represent a person's stock portfolio. We have chosen to embed the stock information in to each portfolio document. In an environment where related data is changing frequently, like a stock trading application, embedding data that changes frequently is going to mean that you are constantly updating each portfolio document every time a stock is traded.
+This could represent a person's stock portfolio. We have chosen to embed the stock information into each portfolio document. In an environment where related data is changing frequently, like a stock trading application, embedding data that changes frequently is going to mean that you are constantly updating each portfolio document every time a stock is traded.
 
 Stock *zaza* may be traded many hundreds of times in a single day and thousands of users could have *zaza* on their portfolio. With a data model like the above we would have to update many thousands of portfolio documents many times every day leading to a system that won't scale well. 
 
@@ -255,7 +255,7 @@ If we look at the JSON below that models publishers and books.
     ...
     {"id": "100", "name": "Learn about Azure Cosmos DB" }
     ...
-    {"id": "1000", "name": "Deep Dive in to Azure Cosmos DB" }
+    {"id": "1000", "name": "Deep Dive into Azure Cosmos DB" }
 
 If the number of the books per publisher is small with limited growth, then storing the book reference inside the publisher document may be useful. However, if the number of books per publisher is unbounded, then this data model would lead to mutable, growing arrays, as in the example publisher document above. 
 
@@ -274,7 +274,7 @@ Switching things around a bit would result in a model that still represents the
     ...
     {"id": "100","name": "Learn about Azure Cosmos DB", "pub-id": "mspress"}
     ...
-    {"id": "1000","name": "Deep Dive in to Azure Cosmos DB", "pub-id": "mspress"}
+    {"id": "1000","name": "Deep Dive into Azure Cosmos DB", "pub-id": "mspress"}
 
 In the above example, we have dropped the unbounded collection on the publisher document. Instead we just have a reference to the publisher on each book document.
 
@@ -294,7 +294,7 @@ You might be tempted to replicate the same thing using documents and produce a d
     {"id": "b2", "name": "Azure Cosmos DB for RDBMS Users" }
     {"id": "b3", "name": "Taking over the world one JSON doc at a time" }
     {"id": "b4", "name": "Learn about Azure Cosmos DB" }
-    {"id": "b5", "name": "Deep Dive in to Azure Cosmos DB" }
+    {"id": "b5", "name": "Deep Dive into Azure Cosmos DB" }
 
     Joining documents: 
     {"authorId": "a1", "bookId": "b1" }
@@ -315,7 +315,7 @@ Consider the following.
     {"id": "b1", "name": "Azure Cosmos DB 101", "authors": ["a1", "a2"]}
     {"id": "b2", "name": "Azure Cosmos DB for RDBMS Users", "authors": ["a1"]}
     {"id": "b3", "name": "Learn about Azure Cosmos DB", "authors": ["a1"]}
-    {"id": "b4", "name": "Deep Dive in to Azure Cosmos DB", "authors": ["a2"]}
+    {"id": "b4", "name": "Deep Dive into Azure Cosmos DB", "authors": ["a2"]}
 
 Now, if I had an author, I immediately know which books they have written, and conversely if I had a book document loaded I would know the ids of the author(s). This saves that intermediary query against the join table reducing the number of server round trips your application has to make. 
 
@@ -377,7 +377,7 @@ Sure, if the author's name changed or they wanted to update their photo we'd hav
 
 In the example, there are **pre-calculated aggregates** values to save expensive processing on a read operation. In the example, some of the data embedded in the author document is data that is calculated at run-time. Every time a new book is published, a book document is created **and** the countOfBooks field is set to a calculated value based on the number of book documents that exist for a particular author. This optimization would be good in read heavy systems where we can afford to do computations on writes in order to optimize reads.
 
-The ability to have a model with pre-calculated fields is made possible because Azure Cosmos DB supports **multi-document transactions**. Many NoSQL stores cannot do transactions across documents and therefore advocate design decisions, such as "always embed everything", due to this limitation. With Azure Cosmos DB, you can use server-side triggers, or stored procedures, that insert books and update authors all within an ACID transaction. Now you don't **have** to embed everything in to one document just to be sure that your data remains consistent.
+The ability to have a model with pre-calculated fields is made possible because Azure Cosmos DB supports **multi-document transactions**. Many NoSQL stores cannot do transactions across documents and therefore advocate design decisions, such as "always embed everything", due to this limitation. With Azure Cosmos DB, you can use server-side triggers, or stored procedures, that insert books and update authors all within an ACID transaction. Now you don't **have** to embed everything into one document just to be sure that your data remains consistent.
 
 ## <a name="NextSteps"></a>Next steps
 The biggest takeaways from this article are to understand that data modeling in a schema-free world is as important as ever. 
