Changing "MarkLogic Spark connector"

Depending on the context, changed it to either "MarkLogic connector for Apache Spark", "MarkLogic connector", or just "connector".

The one exception is the config.yml file the docs, where "MarkLogic connector for Apache Spark" did not fit. So using "MarkLogic Apache Spark connector" there instead of just "MarkLogic Spark connector". 
4b45ec

Author: rjrudin

CONTRIBUTING.md

@@ -3,7 +3,7 @@ have cloned this repository to your local workstation.
 
 # Do this first!
 
-In order to develop and/or test the MarkLogic Spark connector, or to try out the PySpark instructions below, you first 
+In order to develop and/or test the connector, or to try out the PySpark instructions below, you first 
 need to deploy the test application in this project to MarkLogic. You can do so either on your own installation of 
 MarkLogic, or you can use `docker-compose` to install a 3-node MarkLogic cluster with a load balancer in front of it. 
 
@@ -75,7 +75,7 @@ You can then run the tests from within the Docker environment via the following
 
 The documentation for this project 
 [has instructions on using PySpark](https://marklogic.github.io/marklogic-spark-connector/getting-started-pyspark.html) 
-with the MarkLogic Spark connector. The documentation instructs a user to obtain the connector from this repository's 
+with the connector. The documentation instructs a user to obtain the connector from this repository's 
 releases page. For development and testing, you will most likely want to build the connector yourself by running the 
 following command from the root of this repository:
 

README.md

@@ -1,7 +1,7 @@
-# MarkLogic Spark Connector
+# MarkLogic Connector for Apache Spark
 
-The MarkLogic Spark connector is an [Apache Spark 3 connector](https://spark.apache.org/docs/latest/) that supports
-reading data from and writing data to MarkLogic.
+The MarkLogic connector for Apache Spark is an [Apache Spark 3 connector](https://spark.apache.org/docs/latest/) that 
+supports reading data from and writing data to MarkLogic.
 
 Please see [the User Guide](http://marklogic.github.io/marklogic-spark-connector) for more information.
 

docs/_config.yml

@@ -1,4 +1,4 @@
-title: MarkLogic Spark Connector
+title: MarkLogic Apache Spark Connector
 remote_theme: just-the-docs/just-the-docs
 plugins:
   - jekyll-remote-theme

docs/configuration.md

@@ -4,7 +4,7 @@ title: Configuration Reference
 nav_order: 5
 ---
 
-The MarkLogic Spark connector has 3 sets of configuration options - connection options, reading options, and writing 
+The MarkLogic connector has 3 sets of configuration options - connection options, reading options, and writing 
 options. Each set of options is defined in a separate table below.
 
 ## Connection options

docs/getting-started/getting-started.md

@@ -6,7 +6,7 @@ has_children: true
 permalink: /docs/getting-started
 ---
 
-This guide provides instructions on using the MarkLogic Spark connector with multiple popular Spark environments. 
+This guide provides instructions on using the MarkLogic connector with multiple popular Spark environments. 
 Before trying the connector in any of these environments, please [follow the instructions in the Setup guide](setup.md)
 to obtain the connector and deploy an example application to MarkLogic.
 

docs/getting-started/java.md

@@ -5,12 +5,12 @@ parent: Getting Started
 nav_order: 4
 ---
 
-The MarkLogic Spark connector is published to [Maven Central](https://central.sonatype.com/namespace/com.marklogic) and 
+The MarkLogic connector is published to [Maven Central](https://central.sonatype.com/namespace/com.marklogic) and 
 can thus be expressed as a regular dependency of a Java application that also depends on the Spark APIs. 
 
 As an example, please see the project configuration in the 
 [java-dependency example project](https://github.com/marklogic/marklogic-spark-connector/blob/master/examples/java-dependency)
-for how to depend on the MarkLogic Spark connector as a library. The `org.example.App` class in the project demonstrates
+for how to depend on the MarkLogic connector as a library. The `org.example.App` class in the project demonstrates
 a very simple Spark Java program for accessing the data in the application deployed via the [Setup guide](setup.md).
 
 Note - if you are using Java 11 or higher, you may run into a `NoClassDefFoundError` for a class in the `javax.xml.bind`

docs/getting-started/jupyter.md

@@ -6,7 +6,7 @@ nav_order: 3
 ---
 
 [Project Jupyter](https://jupyter.org/) provides a set of tools for working with notebooks, code, and data. The 
-MarkLogic Spark connector can be easily integrated into these tools to allow users to access and analyze data in 
+MarkLogic connector can be easily integrated into these tools to allow users to access and analyze data in 
 MarkLogic. 
 
 Before going further, be sure you've followed the instructions in the [setup guide](setup.md) for
@@ -28,7 +28,7 @@ corner of the Notebook interface and select "Python 3 (ipykernel)" to create a n
 ## Using the connector
 
 In the first cell in the notebook created above, enter the following to allow Jupyter Notebook to access the MarkLogic 
-Spark connector and also to initialize Spark:
+connector and also to initialize Spark:
 
 ```
 import os

docs/getting-started/pyspark.md

@@ -6,8 +6,7 @@ nav_order: 2
 ---
 
 [PySpark](https://spark.apache.org/docs/latest/api/python/index.html) is a Python API for Spark and an excellent choice
-for learning how to use Spark. This guide describes how to install PySpark and use it with the MarkLogic Spark 
-connector. 
+for learning how to use Spark. This guide describes how to install PySpark and use it with the MarkLogic connector. 
 
 Before going further, be sure you've followed the instructions in the [Getting Started](getting-started.md) guide for
 obtaining the connector and deploying an example application to MarkLogic.
@@ -34,15 +33,15 @@ Run PySpark from the directory that you downloaded the connector to per the [set
 
 The `--jars` command line option is PySpark's method for utilizing Spark connectors. Each Spark environment should have
 a similar mechanism for including third party connectors; please see the documentation for your particular Spark
-environment. In the example above, the `--jars` option allows for the MarkLogic Spark connector to be used within
+environment. In the example above, the `--jars` option allows for the connector to be used within
 PySpark.
 
 When PySpark starts, you should see information like this on how to configure logging:
 
     Setting default log level to "WARN".
     To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
 
-Setting the default log level to `INFO` or `DEBUG` will show logging from the MarkLogic Spark connector. This will also
+Setting the default log level to `INFO` or `DEBUG` will show logging from the MarkLogic connector. This will also
 include potentially significant amounts of log messages from PySpark itself.
 
 ### Reading data with the connector

docs/getting-started/setup.md

@@ -10,7 +10,7 @@ environments, as those examples depend on an application being deployed to MarkL
 
 ## Obtaining the connector
 
-The MarkLogic Spark connector can be downloaded from
+The MarkLogic connector can be downloaded from
 [this repository's Releases page](https://github.com/marklogic/marklogic-spark-connector/releases). Each Spark 
 environment should have documentation on how to include third-party connectors; please consult your Spark 
 environment's documentation on how to achieve this.

docs/index.md

@@ -4,7 +4,7 @@ title: Introduction
 nav_order: 1
 ---
 
-The MarkLogic Spark connector is an [Apache Spark 3 connector](https://spark.apache.org/docs/latest/) that supports
+The MarkLogic connector for Apache Spark is an [Apache Spark 3 connector](https://spark.apache.org/docs/latest/) that supports
 reading data from and writing data to MarkLogic. Within any Spark 3 environment, the connector enables users to easily 
 query for data in MarkLogic, manipulate it using widely-known Spark operations, and then write results back to 
 MarkLogic or disseminate them to another system. Data can also be easily imported into MarkLogic by first reading it 

docs/reading.md

@@ -4,7 +4,7 @@ title: Reading Data
 nav_order: 3
 ---
 
-The MarkLogic Spark connector allows for data to be retrieved from MarkLogic as rows via an 
+The MarkLogic connector allows for data to be retrieved from MarkLogic as rows via an 
 [Optic query](https://docs.marklogic.com/guide/app-dev/OpticAPI#id_46710). The 
 sections below provide more detail on configuring how data is retrieved and converted into a Spark DataFrame.
 
@@ -150,7 +150,7 @@ repository.
 
 The Spark connector framework supports pushing down multiple operations to the connector data source. This can
 often provide a significant performance boost by allowing the data source to perform the operation, which can result in
-both fewer rows returned to Spark and less work for Spark to perform. The MarkLogic Spark connector supports pushing
+both fewer rows returned to Spark and less work for Spark to perform. The MarkLogic connector supports pushing
 down the following operations to MarkLogic:
 
 - `count`

docs/writing.md

@@ -4,7 +4,7 @@ title: Writing Data
 nav_order: 4
 ---
 
-The MarkLogic Spark connector allows for writing rows in a Spark DataFrame to MarkLogic as documents. 
+The MarkLogic connector allows for writing rows in a Spark DataFrame to MarkLogic as documents. 
 The sections below provide more detail about how this process works and how it can be controlled.
 
 ## Basic write operation
@@ -104,7 +104,7 @@ temporal collection.
 
 ## Streaming support
 
-The MarkLogic Spark connector supports 
+The connector supports 
 [streaming writes](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html) to MarkLogic. 
 The connector configuration does not change; instead, different Spark APIs are used to read a stream of data and 
 write that stream to MarkLogic. 
@@ -134,7 +134,7 @@ spark.readStream \
     .processAllAvailable()
 ```
 
-The above example will stream the data in the `./data/csv-files/100-employees.csv` file through the MarkLogic Spark 
+The above example will stream the data in the `./data/csv-files/100-employees.csv` file through the 
 connector and into MarkLogic. This will result 100 new JSON documents in the `streaming-example` collection. 
 
 The ability to stream data into MarkLogic can make Spark an effective tool for obtaining data from a variety of data 
@@ -158,7 +158,7 @@ assist with debugging the cause of the error.
 
 ## Tuning performance
 
-The MarkLogic Spark connector uses MarkLogic's 
+The connector uses MarkLogic's 
 [Data Movement SDK](https://docs.marklogic.com/guide/java/data-movement) for writing documents to a database. The 
 following options can be set to adjust how the connector performs when writing data:
 
@@ -178,7 +178,7 @@ resource consumption and throughput from Spark to MarkLogic.
 
 Spark supports 
 [several save modes](https://spark.apache.org/docs/latest/sql-data-sources-load-save-functions.html#save-modes) 
-when writing data. The MarkLogic Spark connector requires the `append` mode to be used. Because Spark defaults to 
+when writing data. The MarkLogic connector requires the `append` mode to be used. Because Spark defaults to 
 the `error` mode, you will need to set this to `append` each time you use the connector to write data. 
 
 `append` is the only supported mode due to MarkLogic not having the concept of a single "table" that a document 

examples/getting-started/marklogic-spark-getting-started.ipynb

@@ -51,7 +51,7 @@
     }
    ],
    "source": [
-    "# Make the MarkLogic Spark connector available to the underlying PySpark application.\n",
+    "# Make the MarkLogic connector available to the underlying PySpark application.\n",
     "import os\n",
     "os.environ['PYSPARK_SUBMIT_ARGS'] = '--jars \"marklogic-spark-connector-2.0.0.jar\" pyspark-shell'\n",
     "\n",

examples/java-dependency/README.md

@@ -1,4 +1,4 @@
-This project is a simple example of creating a Spark application in Java that depends on the MarkLogic Spark 
+This project is a simple example of creating a Spark application in Java that depends on the MarkLogic 
 connector as a normal dependency expressed through Gradle.
 
 Please see the [Java setup guide](https://marklogic.github.io/marklogic-spark-connector/docs/java)