diff --git a/docs/user_guides/fs/compute_engines.md b/docs/user_guides/fs/compute_engines.md
index da655cf5b..6adb0c9d4 100644
--- a/docs/user_guides/fs/compute_engines.md
+++ b/docs/user_guides/fs/compute_engines.md
@@ -4,31 +4,31 @@ In order to execute a feature pipeline to write to the Feature Store, as well as
Hopsworks Feature Store APIs are built around dataframes, that means feature data is inserted into the Feature Store from a Dataframe and likewise when reading data from the Feature Store, it is returned
as a Dataframe.
-As such, Hopsworks supports three computational engines:
+As such, Hopsworks supports five computational engines:
1. [Apache Spark](https://spark.apache.org): Spark Dataframes and Spark Structured Streaming Dataframes are supported, both from Python environments (PySpark) and from Scala environments.
2. [Python](https://www.python.org/): For pure Python environments without dependencies on Spark, Hopsworks supports [Pandas Dataframes](https://pandas.pydata.org/) and [Polars Dataframes](https://pola.rs/).
3. [Apache Flink](https://flink.apache.org): Flink Data Streams are currently supported as an experimental feature from Java/Scala environments.
-3. [Apache Beam](https://beam.apache.org/) *experimental*: Beam Data Streams are currently supported as an experimental feature from Java/Scala environments.
+4. [Apache Beam](https://beam.apache.org/) *experimental*: Beam Data Streams are currently supported as an experimental feature from Java/Scala environments.
+5. [Java](https://www.java.com): For pure Java environments without dependencies on Spark, Hopsworks supports writing using List of POJO Objects.
Hopsworks supports running [compute on the platform itself](../../concepts/dev/inside.md) in the form of [Jobs](../projects/jobs/pyspark_job.md) or in [Jupyter Notebooks](../projects/jupyter/python_notebook.md).
Alternatlively, you can also connect to Hopsworks using Python or Spark from [external environments](../../concepts/dev/outside.md), given that there is network connectivity.
## Functionality Support
-Hopsworks is aiming to provide funtional parity between the computational engines, however, there are certain Hopsworks functionalities which are exclusive to the engines.
+Hopsworks is aiming to provide functional parity between the computational engines, however, there are certain Hopsworks functionalities which are exclusive to the engines.
-| Functionality | Method | Spark | Python | Flink | Beam | Comment |
-| ----------------------------------------------------------------- | ------ | ----- | ------ | ------ | ------ | ------- |
-| Feature Group Creation from dataframes | [`FeatureGroup.create_feature_group()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#create_feature_group) | :white_check_mark: | :white_check_mark: | - | - | Currently Flink/Beam doesn't support registering feature group metadata. Thus it needs to be pre-registered before you can write real time features computed by Flink/Beam.|
-| Training Dataset Creation from dataframes | [`TrainingDataset.save()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/training_dataset_api/#save) | :white_check_mark: | - | - | - | Functionality was deprecated in version 3.0 |
-| Data validation using Great Expectations for streaming dataframes | [`FeatureGroup.validate()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#validate) [`FeatureGroup.insert_stream()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#insert_stream) | - | - | - | - | `insert_stream` does not perform any data validation even when a expectation suite is attached. |
-| Stream ingestion | [`FeatureGroup.insert_stream()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#insert_stream) | :white_check_mark: | - | :white_check_mark: | :white_check_mark: | Python/Pandas/Polars has currently no notion of streaming. |
-| Stream ingestion | [`FeatureGroup.insert_stream()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#insert_stream) | :white_check_mark: | - | :white_check_mark: | :white_check_mark: | Python/Pandas/Polars has currently no notion of streaming. |
-| Reading from Streaming Storage Connectors | [`KafkaConnector.read_stream()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/storage_connector_api/#read_stream) | :white_check_mark: | - | - | - | Python/Pandas/Polars has currently no notion of streaming. For Flink/Beam only write operations are supported |
-| Reading training data from external storage other than S3 | [`FeatureView.get_training_data()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_view_api/#get_training_data) | :white_check_mark: | - | - | - | Reading training data that was written to external storage using a Storage Connector other than S3 can currently not be read using HSFS APIs, instead you will have to use the storage's native client. |
-| Reading External Feature Groups into Dataframe | [`ExternalFeatureGroup.read()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/external_feature_group_api/#read) | :white_check_mark: | - | - | - | Reading an External Feature Group directly into a Pandas/Polars Dataframe is not supported, however, you can use the [Query API](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/query_api/) to create Feature Views/Training Data containing External Feature Groups. |
-| Read Queries containing External Feature Groups into Dataframe | [`Query.read()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/query_api/#read) | :white_check_mark: | - | - | - | Reading a Query containing an External Feature Group directly into a Pandas/Polars Dataframe is not supported, however, you can use the Query to create Feature Views/Training Data and write the data to a Storage Connector, from where you can read up the data into a Pandas/Polars Dataframe. |
+| Functionality | Method | Spark | Python | Flink | Beam | Java | Comment |
+| ----------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------ | ------------------ | ---------------------- | ------------------ | ------------------ |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| Feature Group Creation from dataframes | [`FeatureGroup.create_feature_group()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#create_feature_group) | :white_check_mark: | :white_check_mark: | - | - | - | Currently Flink/Beam/Java doesn't support registering feature group metadata. Thus it needs to be pre-registered before you can write real time features computed by Flink/Beam. |
+| Training Dataset Creation from dataframes | [`TrainingDataset.save()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/training_dataset_api/#save) | :white_check_mark: | - | - | - | - | Functionality was deprecated in version 3.0 |
+| Data validation using Great Expectations for streaming dataframes | [`FeatureGroup.validate()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#validate) [`FeatureGroup.insert_stream()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#insert_stream) | - | - | - | - | - | `insert_stream` does not perform any data validation even when a expectation suite is attached. |
+| Stream ingestion | [`FeatureGroup.insert_stream()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_group_api/#insert_stream) | :white_check_mark: | - | :white_check_mark: | :white_check_mark: | :white_check_mark: | Python/Pandas/Polars has currently no notion of streaming. |
+| Reading from Streaming Storage Connectors | [`KafkaConnector.read_stream()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/storage_connector_api/#read_stream) | :white_check_mark: | - | - | - | - | Python/Pandas/Polars has currently no notion of streaming. For Flink/Beam/Java only write operations are supported |
+| Reading training data from external storage other than S3 | [`FeatureView.get_training_data()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/feature_view_api/#get_training_data) | :white_check_mark: | - | - | - | - | Reading training data that was written to external storage using a Storage Connector other than S3 can currently not be read using HSFS APIs, instead you will have to use the storage's native client. |
+| Reading External Feature Groups into Dataframe | [`ExternalFeatureGroup.read()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/external_feature_group_api/#read) | :white_check_mark: | - | - | - | - | Reading an External Feature Group directly into a Pandas/Polars Dataframe is not supported, however, you can use the [Query API](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/query_api/) to create Feature Views/Training Data containing External Feature Groups. |
+| Read Queries containing External Feature Groups into Dataframe | [`Query.read()`](https://docs.hopsworks.ai/feature-store-api/{{{ hopsworks_version }}}/generated/api/query_api/#read) | :white_check_mark: | - | - | - | - | Reading a Query containing an External Feature Group directly into a Pandas/Polars Dataframe is not supported, however, you can use the Query to create Feature Views/Training Data and write the data to a Storage Connector, from where you can read up the data into a Pandas/Polars Dataframe. |
## Python
@@ -77,3 +77,7 @@ Apache Beam integration with Hopsworks feature store was only tested using Dataf
For more details head over to the [Getting Started Guide](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/java/beam).
+## Java
+It is also possible to interact to Hopsworks feature store using pure Java environments without dependencies on Spark, Flink or Beam.
+
+For more details head over to the [Getting Started Guide](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/java).
\ No newline at end of file
diff --git a/docs/user_guides/integrations/index.md b/docs/user_guides/integrations/index.md
index db8d38b1d..bf52b139b 100644
--- a/docs/user_guides/integrations/index.md
+++ b/docs/user_guides/integrations/index.md
@@ -3,9 +3,10 @@
Hopsworks is an open platform aiming to be accessible from a variety of tools. Learn in this section how to connect to Hopsworks from
- [Python](python)
+- [Java](java)
- [Databricks](databricks/networking)
- [AWS SageMaker](sagemaker)
- [AWS EMR](emr/networking)
- [Azure HDInsight](hdinsight)
- [Azure Machine Learning](mlstudio_designer)
-- [Apache Spark](spark)
+- [Apache Spark](spark)
\ No newline at end of file
diff --git a/docs/user_guides/integrations/java.md b/docs/user_guides/integrations/java.md
new file mode 100644
index 000000000..4e730000e
--- /dev/null
+++ b/docs/user_guides/integrations/java.md
@@ -0,0 +1,100 @@
+---
+description: Documentation on how to connect to Hopsworks from a Java client.
+---
+
+# Java client
+
+Starting from version 3.9.0-RC13, HSFS provides a pure Java client. This guide explains how to use the client to connect to Hopsworks and read or write feature data.
+
+## Generate an API key
+
+For instructions on how to generate an API key follow this [user guide](../projects/api_key/create_api_key.md). For the Java client to work correctly make sure you add the following scopes to your API key:
+
+ 1. featurestore
+ 2. project
+ 3. job
+ 4. kafka
+
+## Add the HSFS dependency to your project:
+
+The HSFS library is available on the Hopsworks' Maven repository. If you are using Maven as build tool, you can add the following in your pom.xml file:
+
+```xml
+
+
+ Hops
+ Hops Repository
+ https://archiva.hops.works/repository/Hops/
+
+ true
+
+
+ true
+
+
+
+```
+
+The artifactId for the HSFS Java build is hsfs, if you are using Maven as build tool, you can add the following dependency:
+
+```xml
+
+ com.logicalclocks
+ hsfs
+ ${hsfs.version}
+
+```
+
+!!!note "Java Version"
+
+ Please note that the Java client has been tested with Java versions up to Java 17
+
+## Connecting to the Feature Store
+
+You are now ready to connect to the Hopsworks Feature Store from a Java client:
+
+```Java
+//Import necessary classes
+import com.logicalclocks.hsfs.FeatureStore;
+import com.logicalclocks.hsfs.FeatureView;
+import com.logicalclocks.hsfs.HopsworksConnection;
+
+//Establish connection with Hopsworks.
+HopsworksConnection hopsworksConnection = HopsworksConnection.builder()
+ .host("my_instance") // DNS of your Feature Store instance
+ .port(443) // Port to reach your Hopsworks instance, defaults to 443
+ .project("my_project") // Name of your Hopsworks Feature Store project
+ .apiKeyValue("api_key") // The API key to authenticate with the feature store
+ .hostnameVerification(false) // Disable for self-signed certificates
+ .build();
+
+//get feature store handle
+FeatureStore fs = hopsworksConnection.getFeatureStore();
+
+//get feature view handle
+FeatureView fv = fs.getFeatureView(fvName, fvVersion);
+
+// get feature vector
+List