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.
- 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.