Louis_Frolio
Databricks Employee
Databricks Employee

‎05-14-2025 06:42 AM

Databricks does not offer a specific Spark connector for Java comparable to the Snowflake Spark connector mentioned in the provided URL. However, Databricks supports directly writing data to Databricks tables using Spark APIs. In your use case of transferring data from S3 to a Databricks table, you can achieve this fully using Spark without relying on JDBC.
Here’s a streamlined approach to replace the JDBC write operation with Spark-based writes: 1. Reading Data from S3: Use the Spark read function with the appropriate format based on your data (e.g., csv, parquet, etc.) and specify the S3 path. scala val data = spark.read.format("parquet").load("s3://bucket-name/folder-name") Ensure you configure your AWS credentials for accessing S3.
  1. Writing Data to Databricks Table: Use the Delta format or another supported format to write data directly to a Databricks table: scala data.write.format("delta").save("/mnt/databricks-table-path") If the table is pre-defined, you can use the saveAsTable method instead: scala data.write.format("delta").mode("overwrite").saveAsTable("database.table_name")
This approach eliminates the need for JDBC and integrates seamlessly with Databricks' native capabilities. However, if Java compatibility is an absolute requirement, these Spark APIs can still be invoked via Java bindings provided by Apache Spark. Concepts like DataStreamReader and DataStreamWriter in Java mirror their Scala equivalents.
 
Hope this helps, Lou.