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Begin by exporting the data you need from Monday.com. Navigate to your board on Monday.com, click on the three dots menu at the top right of the board, and choose the 'Export' option. Select 'Export to Excel' or 'Export to CSV' depending on your preference. Save the exported file to your local system.
Review the exported file to ensure that the data structure aligns with what you need in BigQuery. Open the file in a spreadsheet program like Excel or Google Sheets and clean up any unnecessary columns or rows. Ensure the data types (e.g., strings, integers) are consistent and match the expected schema in BigQuery.
Log in to your Google Cloud Platform account and navigate to BigQuery. Create a new dataset by clicking 'Create Dataset' in the BigQuery console. Give your dataset a unique name and configure any necessary settings like data location and expiration.
Prepare the schema for the table where the data will be imported. This involves specifying the names, data types, and modes (e.g., NULLABLE, REQUIRED) for each column. You can do this manually in the BigQuery console under the dataset you created by selecting 'Create Table' and defining the schema in the UI.
Before loading data into BigQuery, you need to upload it to Google Cloud Storage (GCS). Access Google Cloud Storage from the GCP console and create a new bucket or use an existing one. Upload your CSV or Excel file to this bucket by clicking 'Upload files' and selecting your file.
With your data in GCS, return to BigQuery and load the data from your GCS bucket. In the BigQuery console, navigate to your dataset, click 'Create Table', and select 'Google Cloud Storage' as the source. Enter the GCS URI of your uploaded file. Configure any additional settings, such as field delimiter or skipping header rows, and confirm the schema matches your data.
Once the data import is complete, run a few queries in BigQuery to verify that the data matches what you expected. Check for discrepancies in row counts, data accuracy, and data types. This step ensures that the data transfer from Monday.com to BigQuery was successful and that the dataset is ready for analysis.
By following these steps, you can efficiently transfer data from Monday.com to BigQuery without the need for 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:
