DOCSP-33956: atomic typos fy2024-q4 (#148)

rustagir · web-flow · commit aafabc689422 · 2023-11-15T15:22:53.000-05:00
* DOCSP-33956: atomic typos fy2024-q4 * vale need to fix * vale fixes * CC suggestions * left over fixes
diff --git a/source/introduction/install.txt b/source/introduction/install.txt
@@ -97,7 +97,7 @@ You can download the connector source and JAR files from the following locations
      - `mongo-kafka-connect <https://search.maven.org/artifact/org.mongodb.kafka/mongo-kafka-connect>`__
 
 You can identify the contents of the JAR files by the suffix in the
-filename. Consult the following table for a description of each suffix:
+file name. Consult the following table for a description of each suffix:
 
 .. list-table::
    :widths: 25 75
diff --git a/source/introduction/kafka-connect.txt b/source/introduction/kafka-connect.txt
@@ -69,7 +69,7 @@ For more information on Kafka Connect, see the following resources:
      reliable pipeline.
    - There are a large number of community maintained connectors for connecting
      Apache Kafka to popular datastores like MongoDB, PostgreSQL, and MySQL using the
-     Kafka Connect framework. This reduces the amount of boilerplate code you need to
+     Kafka Connect framework. This reduces the amount of boilerplate code you must
      write and maintain to manage database connections, error handling,
      dead letter queue integration, and other problems involved in connecting Apache Kafka
      with a datastore.
@@ -85,5 +85,6 @@ cluster as a data source, and a MongoDB cluster as a data sink.
 .. figure:: /includes/figures/connect-data-flow.png
    :alt: Dataflow diagram of Kafka Connect deployment. 
 
-All connectors and datastores in the example pipeline are optional, and you can
-swap them out for the connectors and datastores you need for your deployment.
+All connectors and datastores in the example pipeline are optional.
+You can replace them with the connectors and datastores you need 
+for your deployment.
diff --git a/source/monitoring.txt b/source/monitoring.txt
@@ -41,7 +41,7 @@ to satisfy those use cases.
 .. tip:: Computed Values
 
    To learn what types of metrics the connector provides and when
-   you must implement logic to compute a value, see
+   to implement logic to compute a value, see
    :ref:`<kafka-monitoring-types-of-metrics>`.
 
 Sink Connector
@@ -58,22 +58,22 @@ to satisfy those use cases:
    * - Use Case
      - Metrics to Use
 
-   * - You need to know if a component of your pipeline is falling behind.
+   * - You want to know if a component of your pipeline is falling behind.
      - Use the ``latest-kafka-time-difference-ms``
        metric. This metric indicates the interval of time between
        when a record arrived in a Kafka topic and when your connector
        received that record. If the value of this metric is increasing,
        it signals that there may be a problem with {+kafka+} or MongoDB. 
 
-   * - You need to know the total number of records your connector
+   * - You want to know the total number of records your connector
        wrote to MongoDB.
      - Use the ``records`` metric.
 
-   * - You need to know the total number of write errors your connector
+   * - You want to know the total number of write errors your connector
        encountered when attempting to write to MongoDB.
      - Use the ``batch-writes-failed`` metric.
 
-   * - You need to know if your MongoDB performance is getting slower
+   * - You want to know if your MongoDB performance is getting slower
        over time.
      - Use the ``in-task-put-duration-ms`` metric to initially diagnose
        a slowdown.
@@ -84,8 +84,8 @@ to satisfy those use cases:
        - ``batch-writes-failed-duration-over-<number>-ms``
        - ``processing-phase-duration-over-<number>-ms``
  
-   * - You need to find a bottleneck in how {+kafka-connect+} and your MongoDB sink
-       connector write {+kafka+} records to MongoDB.
+   * - You want to find the time {+kafka-connect+} and the MongoDB sink
+       connector spend writing records to MongoDB.
      - Compare the values of the following metrics:
      
        - ``in-task-put-duration-ms``
@@ -108,17 +108,17 @@ to satisfy those use cases:
    * - Use Case
      - Metrics to Use
 
-   * - You need to know if a component of your pipeline is falling behind.
+   * - You want to know if a component of your pipeline is falling behind.
      - Use the ``latest-mongodb-time-difference-secs``
        metric. This metric indicates how old the most recent change
        stream event your connector processed is. If this metric is increasing,
        it signals that there may be a problem with {+kafka+} or MongoDB. 
 
-   * - You need to know the total number of change stream events your source connector
+   * - You want to know the total number of change stream events your source connector
        has processed. 
      - Use the ``records`` metric.
 
-   * - You need to know the percentage of records your connector
+   * - You want to know the percentage of records your connector
        received but failed to write to {+kafka+}.
      - Perform the following calculation with the ``records``,
        ``records-filtered``, and ``records-acknowledged`` metrics:
@@ -127,7 +127,7 @@ to satisfy those use cases:
       
           (records - (records-acknowledged + records-filtered)) / records
 
-   * - You need to know the average size of the documents your connector
+   * - You want to know the average size of the documents your connector
        has processed.
      - Perform the following calculation with the ``mongodb-bytes-read`` and
        ``records`` metrics:
@@ -139,14 +139,14 @@ to satisfy those use cases:
        To learn how to calculate the average size of records over a span of
        time, see :ref:`mongodb-bytes-read <kafka-monitoring-averge-record-size-span>`.
 
-   * - You need to find a bottleneck in how {+kafka-connect+} and your MongoDB source
-       connector write MongoDB documents to {+kafka+}.
+   * - You want to find the time {+kafka-connect+} and the MongoDB
+       source connector spend writing records to {+kafka+}.
      - Compare the values of the following metrics:
      
        - ``in-task-poll-duration-ms``
        - ``in-connect-framework-duration-ms``
 
-   * - You need to know if your MongoDB performance is getting slower
+   * - You want to know if your MongoDB performance is getting slower
        over time.
      - Use the ``in-task-poll-duration-ms`` metric to initially diagnose
        a slowdown.
@@ -220,7 +220,7 @@ types of quantities:
 - The value related to the most recent occurrence of an event
 
 For some use cases, you must perform extra computations with the
-metrics the connector provides. For example, you must compute the
+metrics the connector provides. For example, you can compute the
 following values from provided metrics:
 
 - The rate of change of a metric
diff --git a/source/quick-start.txt b/source/quick-start.txt
@@ -22,7 +22,7 @@ Kafka topic, and to read data from a Kafka topic and write it to MongoDB.
 
 To complete the steps in this guide, you must download and work in a
 **sandbox**, a containerized development environment that includes services
-you need to build a sample *data pipeline*.
+required to build a sample *data pipeline*.
 
 Read the following sections to set up your sandbox and sample data pipeline.
 
diff --git a/source/security-and-authentication/mongodb-aws-auth.txt b/source/security-and-authentication/mongodb-aws-auth.txt
@@ -24,7 +24,7 @@ AWS IAM credentials, see the guide on :atlas:`How to Set Up Unified AWS Access <
 
 .. important::
 
-   You need to use {+connector+} version 1.5 of later to connect to a MongoDB
+   You must use {+connector+} version 1.5 of later to connect to a MongoDB
    server set up to authenticate using your AWS IAM credentials. AWS IAM
    credential authentication is available in MongoDB server version 4.4
    and later.
@@ -39,7 +39,7 @@ connection URI connector property as shown in the following example:
 
    connection.uri=mongodb://<AWS access key id>:<AWS secret access key>@<hostname>:<port>/?authSource=<authentication database>&authMechanism=MONGODB-AWS&authMechanismProperties=AWS_SESSION_TOKEN:<AWS session token>
 
-The preceding example uses the following placeholders which you need to
+The preceding example uses the following placeholders which you must
 replace:
 
 .. list-table::
diff --git a/source/sink-connector/fundamentals/write-strategies.txt b/source/sink-connector/fundamentals/write-strategies.txt
@@ -326,7 +326,7 @@ business key, perform the following tasks:
 #. Specify the ``DeleteOneBusinessKeyStrategy`` write model strategy in the
    connector configuration.
 
-Suppose you need to delete a calendar event from a specific year from
+Suppose you want to delete a calendar event from a specific year from
 a collection that contains a document that resembles the following:
 
 .. _delete-one-business-key-sample-document:
diff --git a/source/source-connector/configuration-properties/startup.txt b/source/source-connector/configuration-properties/startup.txt
@@ -75,7 +75,8 @@ Settings
        | **Description:**
        | Actuated only if ``startup.mode=timestamp``. Specifies the
          starting point for the change stream. To learn more about
-         Change Stream parameters, see the :rapid:`Server manual entry </reference/operator/aggregation/changeStream/>`.
+         Change Stream parameters, see the :manual:`Server manual entry
+         </reference/operator/aggregation/changeStream/>`.
        | **Default**: ``""``
        | **Accepted Values**:
 
diff --git a/source/source-connector/fundamentals/change-streams.txt b/source/source-connector/fundamentals/change-streams.txt
@@ -45,8 +45,9 @@ To learn more about the oplog, see the MongoDB manual entry on the
 Aggregation
 ~~~~~~~~~~~
 
-Use an aggregation pipeline to configure your source connector's change stream.
-Some of the ways you can configure your connector's change stream are as follows:
+Use an aggregation pipeline to configure your source connector's change
+stream. You can configure a connector change stream to use an
+aggregation pipeline to perform tasks including the following operations:
 
 - Filter change events by operation type
 - Project specific fields
@@ -87,7 +88,7 @@ The oplog is a special capped collection which cannot use indexes.  For more
 information on this limitation, see
 :manual:`Change Streams Production Recommendations </administration/change-streams-production-recommendations/#indexes>`.
 
-If you need to improve change stream performance, use a faster disk for
+If you want to improve change stream performance, use a faster disk for
 your MongoDB cluster and increase the size of your WiredTiger cache. To
 learn how to set your WiredTiger cache, see the guide on the
 :manual:`WiredTiger Storage Engine </core/wiredtiger/#memory-use>`.
diff --git a/source/source-connector/usage-examples/copy-existing-data.txt b/source/source-connector/usage-examples/copy-existing-data.txt
@@ -10,7 +10,7 @@ This usage example demonstrates how to copy data from a MongoDB collection to an
 Example
 -------
 
-Suppose you need to copy a MongoDB collection to {+kafka+} and filter some of the data.
+Suppose you want to copy a MongoDB collection to {+kafka+} and filter some data.
 
 Your requirements and your solutions are as follows:
 
diff --git a/source/source-connector/usage-examples/custom-pipeline.txt b/source/source-connector/usage-examples/custom-pipeline.txt
@@ -19,7 +19,7 @@ For more information, see the MongoDB Server manual entry on
 Example
 -------
 
-Suppose you're an event coordinator who needs to collect names and arrival times
+Suppose you are coordinating an event and want to collect names and arrival times
 of each guest at a specific event. Whenever a guest checks into the event,
 an application inserts a new document that contains the following details:
 
diff --git a/source/source-connector/usage-examples/schema.txt b/source/source-connector/usage-examples/schema.txt
@@ -7,7 +7,7 @@ Specify a Schema
 This usage example demonstrates how you can configure your {+source-connector+}
 to apply a custom **schema** to your data. A schema is a
 definition that specifies the structure and type information about data in an
-{+kafka+} topic. Use a schema when you need to ensure the data on the topic populated
+{+kafka+} topic. Use a schema when you must ensure the data on the topic populated
 by your source connector has a consistent structure.
 
 To learn more about using schemas with the connector, see the 
@@ -17,7 +17,7 @@ Example
 -------
 
 Suppose your application keeps track of customer data in a MongoDB
-collection, and you need to publish this data to a Kafka topic. You want
+collection, and you want to publish this data to a Kafka topic. You want
 the subscribers of the customer data to receive consistently formatted data.
 You choose to apply a schema to your data.
 
diff --git a/source/tutorials/migrate-time-series.txt b/source/tutorials/migrate-time-series.txt
@@ -19,7 +19,7 @@ data consists of measurements taken at time intervals, metadata that describes
 the measurement, and the time of the measurement.
 
 To convert data from a MongoDB collection to a time series collection using
-the connector, you need to perform the following tasks:
+the connector, you must perform the following tasks:
 
 #. Identify the time field common to all documents in the collection.
 #. Configure a source connector to copy the existing collection data to a
diff --git a/source/tutorials/replicate-with-cdc.txt b/source/tutorials/replicate-with-cdc.txt
@@ -16,7 +16,7 @@ Overview
 Follow this tutorial to learn how to use a
 **change data capture (CDC) handler** to replicate data with the {+connector+}.
 A CDC handler is an application that translates CDC events into MongoDB
-write operations. Use a CDC handler when you need to reproduce the changes
+write operations. Use a CDC handler when you must reproduce the changes
 in one datastore into another datastore.
 
 In this tutorial, you configure and run MongoDB Kafka source and sink
diff --git a/source/tutorials/tutorial-setup.txt b/source/tutorials/tutorial-setup.txt
@@ -5,7 +5,7 @@ Kafka Connector Tutorial Setup
 ==============================
 
 The tutorials in this section run on a development environment using Docker to
-package the dependencies and configurations you need to run the
+package the dependencies and configurations required to run the
 {+connector-long+}. Make sure you complete the development environment setup
 steps before proceeding to the tutorials.
 
@@ -102,8 +102,8 @@ Set Up Your Development Environment with Docker
 
    .. step:: Verify the Successful Setup
 
-      Confirm the development environment started normally by running the
-      following commands:
+      Confirm that the development environment started successfully by
+      running the following commands:
 
       .. code-block:: bash
 
diff --git a/source/whats-new.txt b/source/whats-new.txt
@@ -236,7 +236,7 @@ What's New in 1.6
   and :ref:`source connector <source-configuration-error-handling>`
   that can override the {+kafka-connect+} framework's error handling behavior
 - Added ``mongo-kafka-connect-<version>-confluent.jar``, which contains
-  the connector and all dependencies needed to run it on the Confluent Platform
+  the connector and all dependencies required to run it on the Confluent Platform
 
 Sink Connector
 ~~~~~~~~~~~~~~
