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Begin by logging into your Monday.com account. Navigate to the board or workspace containing the data you wish to export. Use the export feature available in Monday.com to download the data as a CSV file. This can typically be done by clicking on the three-dot menu on the top right of the board and selecting "Export to Excel." This will provide you with a CSV file, which is a format easily manageable for importing into other systems.
Open the exported CSV file using a spreadsheet application like Microsoft Excel or Google Sheets. Review the data for consistency and completeness. Ensure that there are no empty fields, duplicates, or formatting errors that might cause issues during the import process. Make any necessary adjustments to ensure the data is clean and ready for import.
Depending on the schema and structure required by your Databricks Lakehouse, you may need to reformat or transform the data. This can include renaming columns to match the lakehouse schema, converting data types, or splitting/combining columns. Save the final, cleaned, and formatted data as a CSV file.
Log into your Databricks account and navigate to the Databricks workspace. If you do not have an account, you will need to create one and set up a new workspace. Ensure that you have the necessary permissions to upload and manage data within the workspace.
Use the Databricks UI or the Databricks CLI to upload your prepared CSV file to the Databricks File System (DBFS). In the Databricks UI, you can use the 'Data' tab, click 'Add Data', and then 'Upload File' to select and upload your CSV file. Ensure the file is uploaded to a location in DBFS that is accessible by your Databricks notebooks or jobs.
Once your data is uploaded to DBFS, create a new notebook in Databricks. Use PySpark or Scala to read the CSV file from DBFS into a Spark DataFrame. For example, using PySpark, you can run:
```python
df = spark.read.csv("/dbfs/path/to/your/file.csv", header=True, inferSchema=True)
```
This command reads the CSV file into a DataFrame, with the first row used as headers and automatic schema inference.
Finally, write the DataFrame to a Delta Lake table in the Databricks Lakehouse to ensure it is stored efficiently and can be queried effectively. Use the following command in your notebook:
```python
df.write.format("delta").mode("overwrite").save("/delta/path/to/your/table")
```
Replace `/delta/path/to/your/table` with the appropriate Delta Lake path for your data. This step ensures your data is now part of the Databricks Lakehouse, ready for analysis and processing.
By following these steps, you will successfully transfer data from Monday.com to a Databricks Lakehouse without using third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Monday is the first day of the week in most countries and is typically associated with the start of a new work or school week. It is often viewed as a day of productivity and setting goals for the week ahead. Many people may feel a sense of dread or stress on Mondays, commonly referred to as the "Monday blues." However, others may view it as an opportunity to start fresh and tackle new challenges. Some cultures also have specific traditions or superstitions associated with Mondays, such as avoiding certain activities or wearing specific colors. Overall, Monday represents a new beginning and a chance to make the most of the week ahead.
Monday's API provides access to a wide range of data related to project management and team collaboration. The following are the categories of data that can be accessed through Monday's API:
1. Boards: This category includes data related to the boards created in Monday, such as board name, description, and status.
2. Items: This category includes data related to the items created within a board, such as item name, description, and status.
3. Users: This category includes data related to the users who have access to a board, such as user name, email address, and role.
4. Groups: This category includes data related to the groups created within a board, such as group name, description, and members.
5. Columns: This category includes data related to the columns created within a board, such as column name, type, and settings.
6. Updates: This category includes data related to the updates made to a board or item, such as update text, creator, and timestamp.
7. Notifications: This category includes data related to the notifications sent to users, such as notification type, recipient, and timestamp.
Overall, Monday's API provides access to a comprehensive set of data that can be used to build custom integrations and applications to enhance project management and team collaboration.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
