How to load data from Postgres to Databricks Lakehouse

1. Connect to your PostgreSQL database using a client like psql, pgAdmin, or any other database tool.
2. Determine the tables or data you want to export.
3. Use the COPY command to export the data to a CSV file. For example:

COPY (SELECT * FROM your_table) TO '/path/to/your/output.csv' WITH CSV HEADER;

1. If you have multiple tables, repeat the process for each table.

1. Access your cloud storage service.
2. Create a bucket or container if you don’t already have one.
3. Upload the CSV files exported from PostgreSQL to the cloud storage bucket.
   
   • For AWS S3, you can use the AWS CLI:

aws s3 cp /path/to/your/output.csv s3://your-bucket-name/path/to/output.csv

• For Azure Blob Storage, you can use the Azure CLI:

az storage blob upload --container-name your-container-name --file /path/to/your/output.csv --name path/to/output.csv

• For GCP Cloud Storage, you can use the gsutil command:

gsutil cp /path/to/your/output.csv gs://your-bucket-name/path/to/output.csv

1. Log in to your Databricks workspace.
2. Navigate to the “Clusters” section and create a new cluster or start an existing one.
3. Ensure that the cluster has the necessary permissions to access the cloud storage where you’ve uploaded the CSV files.

Mount the cloud storage bucket to Databricks to make the data accessible within Databricks notebooks:

1. Open a new notebook in Databricks.
2. Use the DBFS (Databricks File System) commands to mount the bucket. Here’s an example for AWS S3:

dbutils.fs.mount("s3a://your-bucket-name", "/mnt/your-mount-name", extra_configs={"fs.s3a.access.key": "your-access-key", "fs.s3a.secret.key": "your-secret-key"})

1. Replace your-access-key and your-secret-key with your AWS credentials.

1. In your Databricks notebook, read the data from the mounted storage into a DataFrame:

df = spark.read.csv("/mnt/your-mount-name/path/to/output.csv", header=True, inferSchema=True)

1. Perform any necessary data transformations or schema adjustments.
2. Write the DataFrame to the Databricks Lakehouse (Delta Lake):

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

1. If you have multiple CSV files, repeat the loading process for each one.

1. Once the data is stored in Delta format, create tables for querying:

spark.sql("CREATE TABLE your_table USING DELTA LOCATION '/mnt/your-mount-name/delta/your-table'")

1. Validate that the table has been created and contains the expected data by running a query:

display(spark.sql("SELECT * FROM your_table"))

1. Unmount the storage if it’s no longer needed:

dbutils.fs.unmount("/mnt/your-mount-name")

1. Stop the Databricks cluster to save resources if you’re done with the data transfer.

Notes:

• Ensure that the data types in PostgreSQL match the corresponding data types in Databricks to avoid any data type mismatch issues.
• The data transfer can be automated using Databricks jobs if you need to perform this operation regularly.
• Always secure your credentials and consider using a secrets manager to handle access keys and secrets.