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remove mentions of the exact domain from the content (#902)

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* remove mentions of the exact domain from the content

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Andrey Vasnetsov
2024-05-15 18:01:28 +02:00
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qdrant-landing/content/documentation/examples/databricks.md
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Apache Spark is designed to scale horizontally, meaning it can handle expensive operations like generating vector embeddings by distributing computation across a cluster of machines. This scalability is crucial when dealing with large datasets.
In this example, we will demonstrate how to vectorize a dataset with dense and sparse embeddings using Qdrant's [FastEmbed](https://qdrant.github.io/fastembed/) library. We will then load this vectorized data into a Qdrant cluster using the [Qdrant Spark connector](https://qdrant.tech/documentation/frameworks/spark/) on Databricks.
In this example, we will demonstrate how to vectorize a dataset with dense and sparse embeddings using Qdrant's [FastEmbed](https://qdrant.github.io/fastembed/) library. We will then load this vectorized data into a Qdrant cluster using the [Qdrant Spark connector](/documentation/frameworks/spark/) on Databricks.
### Setting up a Databricks project
- Set up a **[Databricks cluster](https://docs.databricks.com/en/compute/configure.html)** following the official documentation guidelines.
- Install the **[Qdrant Spark connector](https://qdrant.tech/documentation/frameworks/spark/)** as a library:
- Install the **[Qdrant Spark connector](/documentation/frameworks/spark/)** as a library:
- Navigate to the `Libraries` section in your cluster dashboard.
- Click on `Install New` at the top-right to open the library installation modal.
- Search for `io.qdrant:spark:VERSION` in the Maven packages and click on `Install`.