How to load data from My Hours to Databricks Lakehouse

Learn how to use Airbyte to synchronize your My Hours data into Databricks Lakehouse within minutes.

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Set up a My Hours 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 My Hours 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 My Hours 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 My Hours

Begin by exporting your data from My Hours. Log into your My Hours account and navigate to the section where you can export data. Typically, this involves accessing the "Reports" or "Data Export" functionality. Choose the data you wish to export and select a suitable format (e.g., CSV, Excel). Download the file to your local system.

Open the exported file to ensure the data is structured correctly. Clean the data if necessary, removing any unwanted columns or rows and ensuring consistent formatting. Save your cleaned data in a CSV format, as it is commonly used and easily processed by Databricks.

If you haven't already, set up an account with Databricks and create a new Lakehouse environment. This involves selecting your cloud provider (AWS, Azure, or Google Cloud Platform) and creating a cluster to process your data. Follow the Databricks setup instructions to configure your environment properly.

Before moving the data into Databricks Lakehouse, you need to upload it to cloud storage that is accessible by Databricks. Depending on your cloud provider, this could be AWS S3, Azure Blob Storage, or Google Cloud Storage. Use the cloud provider’s web interface or CLI to upload your cleaned CSV file to your designated storage bucket or container.

In your Databricks workspace, use the Databricks notebook interface to access the data stored in your cloud storage. You will need to configure your Databricks environment to have the appropriate permissions to read from your cloud storage. Use Spark to read the CSV file into a DataFrame. For example, in a Python notebook, you could use:

```python
df = spark.read.csv('path_to_your_cloud_storage_file', header=True, inferSchema=True)
```

Once the data is in a DataFrame, you can perform any transformations needed to prepare it for analysis and storage in the Lakehouse. This may include data type conversions, renaming columns, or filtering records. After transforming the data, write the DataFrame to a Delta Lake table within your Databricks Lakehouse environment:

```python
df.write.format('delta').mode('overwrite').save('/mnt/datalake/your-delta-table')
```

After loading the data into your Delta Lake table, verify that the data has been loaded correctly by querying the table in Databricks. Check for any discrepancies or errors. Additionally, configure access permissions to ensure that only authorized users can access the data. This may involve setting up role-based access controls (RBAC) within Databricks.

By following these steps, you can successfully move your data from My Hours into Databricks Lakehouse without using any third-party connectors or integrations.