vale need to fix

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.

source/monitoring.txt

@@ -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 must 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 must 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 must 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 must know if your MongoDB performance is getting slower
        over time.
      - Use the ``in-task-put-duration-ms`` metric to initially diagnose
        a slowdown.
@@ -84,7 +84,7 @@ 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
+   * - You must find a bottleneck in how {+kafka-connect+} and your MongoDB sink
        connector write {+kafka+} records to MongoDB.
      - Compare the values of the following metrics:
 
@@ -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 must 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 must 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 must 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 must 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
+   * - You must find a bottleneck in how {+kafka-connect+} and your MongoDB source
        connector write MongoDB documents 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 must know if your MongoDB performance is getting slower
        over time.
      - Use the ``in-task-poll-duration-ms`` metric to initially diagnose
        a slowdown.

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*.
+you must have to build a sample *data pipeline*.
 
 Read the following sections to set up your sandbox and sample data pipeline.
 

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.
@@ -39,8 +39,8 @@ 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
-replace:
+The preceding example uses the following placeholders which you must
+replace with your own credentials:
 
 .. list-table::
    :header-rows: 1

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:

source/source-connector/fundamentals/change-streams.txt

@@ -87,7 +87,7 @@ 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>`.

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 must copy a MongoDB collection to {+kafka+} and filter some of the data.
 
 Your requirements and your solutions are as follows:
 

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 are coordinating an event and need to collect names and arrival times
+Suppose you are coordinating an event and must 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:
 

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 must 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.
 

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

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

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 you must have to run the
 {+connector-long+}. Make sure you complete the development environment setup
 steps before proceeding to the tutorials.