How to load data from Oracle DB to Databricks Lakehouse

Learn how to use Airbyte to synchronize your Oracle DB data into Databricks Lakehouse within minutes.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Oracle DB connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Databricks Lakehouse for your extracted Oracle DB data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Oracle DB to Databricks Lakehouse in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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How to Sync to Manually

Step 1: Export Data from Oracle Database

  1. Connect to your Oracle database using SQL*Plus or any other Oracle database client.
  2. Determine the data you want to export. You may want to export entire tables or just a subset of data, depending on your requirements.
  3. Export the data to a CSV file or another suitable format using Oracle’s export utilities like expdp or sqlplus. For example, you can use the following command in SQL*Plus to export a table to a CSV file:

SPOOL /path/to/your/outputfile.csv
SELECT /*csv*/ * FROM your_table;
SPOOL OFF

  1. Compress the file to reduce the size and transfer time, using a tool like gzip.

Step 2: Upload Data to Cloud Storage

  1. Choose a cloud storage service compatible with Databricks, such as AWS S3, Azure Blob Storage, or Google Cloud Storage.
  2. Upload the exported file(s) to the chosen cloud storage. You can use the cloud provider’s web interface, CLI, or SDKs to upload the files.

Step 3: Set Up Databricks Environment

  1. Log in to your Databricks workspace.
  2. Create a new cluster or use an existing cluster that meets your workload requirements.
  3. Install any necessary libraries on the cluster that may be required for reading from your cloud storage or processing the data.

Step 4: Read Data into Databricks

  1. Mount the cloud storage to DBFS (Databricks File System) using Databricks’ built-in utilities. This will allow you to access the data as if it were a local file system. For example, to mount an S3 bucket, you can use the following command:

dbutils.fs.mount("s3a://your-bucket-name", "/mnt/your-mount-name")

  1. Read the data into a Spark DataFrame using the appropriate Spark APIs. For example, to read a CSV file:

df = spark.read.csv("/mnt/your-mount-name/path/to/your/outputfile.csv")

Step 5: Data Transformation (Optional)

  1. Perform any necessary data transformations using Spark DataFrame transformations.
  2. Cleanse and prepare the data for storage in Databricks Lakehouse.

Step 6: Write Data to Databricks Lakehouse

  1. Define the target location within Databricks Lakehouse where you want to store the data.
  2. Write the data from the Spark DataFrame to Databricks Lakehouse using DataFrame writer API. For example, to write data to Delta Lake format:

df.write.format("delta").save("/mnt/your-mount-name/delta/your-table")

Step 7: Validate Data Transfer

  1. Verify the data has been transferred correctly by reading a sample of the data from Databricks Lakehouse and comparing it against the original data from Oracle.
  2. Perform any additional validation checks as necessary, such as row counts, data types, and integrity constraints.

Step 8: Clean Up

  1. Un-mount the cloud storage if it is no longer needed.
  2. Delete any temporary files that were created during the process.

Step 9: Automate and Schedule (Optional)

  1. Create a Databricks job to automate the data transfer process.
  2. Schedule the job to run at your desired frequency.