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docs: Updated spark.md

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@@ -6,7 +6,7 @@ aliases: [ ../integrations/spark/ ]
# Apache Spark
[Apache Spark](https://spark.apache.org/) is a distributed computing framework designed for big data processing and analytics. This connector enables [Qdrant](https://qdrant.tech/) to be a storage destination in Spark.
[Spark](https://spark.apache.org/) is a distributed computing framework designed for big data processing and analytics. The [Qdrant-Spark connector](https://github.com/qdrant/qdrant-spark) enables Qdrant to be a storage destination in Spark.
## Installation
@@ -14,7 +14,7 @@ You can set up the Qdrant-Spark Connector in a few different ways, depending on
### GitHub Releases
The simplest way to get started is by downloading pre-packaged JAR file releases from the [Qdrant-Spark GitHub releases page](https://github.com/qdrant/qdrant-spark/releases). These JAR files come with all the necessary dependencies to get you going.
The simplest way to get started is by downloading pre-packaged JAR file releases from the [GitHub releases page](https://github.com/qdrant/qdrant-spark/releases). These JAR files come with all the necessary dependencies.
### Building from Source
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For use with Java and Scala projects, the package can be found [here](https://central.sonatype.com/artifact/io.qdrant/spark).
## Getting Started
## Usage
After successfully installing the Qdrant-Spark Connector, you can start integrating Qdrant with your Spark applications. Below, we'll walk through the basic steps of creating a Spark session with Qdrant support and loading data into Qdrant.
Below, we'll walk through the steps of creating a Spark session with Qdrant support and loading data into Qdrant.
### Creating a single-node Spark session with Qdrant Support
To begin, import the necessary libraries and create a Spark session with Qdrant support. Here's how:
To begin, import the necessary libraries and create a Spark session with Qdrant support:
```python
from pyspark.sql import SparkSession
@@ -214,9 +214,9 @@ The connector supports ingesting multiple named/unnamed, dense/sparse vectors.
## Databricks
You can use the `qdrant-spark` connector as a library in [Databricks](https://www.databricks.com/) to ingest data into Qdrant.
You can use the `qdrant-spark` connector as a library in [Databricks](https://www.databricks.com/).
- Go to the `Libraries` section in your cluster dashboard.
- Go to the `Libraries` section in your Databricks cluster dashboard.
- Select `Install New` to open the library installation modal.
- Search for `io.qdrant:spark:VERSION` in the Maven packages and click `Install`.
@@ -226,7 +226,7 @@ You can use the `qdrant-spark` connector as a library in [Databricks](https://ww
Qdrant supports all the Spark data types, and the appropriate data types are mapped based on the provided schema.
## Options and Spark types 🛠️
## Configuration Options
| Option | Description | Column DataType | Required |
| :--------------------------- | :------------------------------------------------------------------ | :---------------------------- | :------- |