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Before extracting data, familiarize yourself with the structure and format of data in Monday.com. Identify the boards, items, and columns you wish to export. This understanding will guide you in writing the API queries effectively.
Access the Monday.com API by generating an API token. Log into your Monday.com account, navigate to the Admin section, and find the API tab to generate a new API token. This token will authenticate your requests.
Develop a script (using Python, Node.js, or another language) to query the Monday.com API. Use the token to authenticate your requests and API endpoints to fetch the data. Parse the JSON responses to extract the required data fields. For instance, you can use Python�s `requests` library to make HTTP requests to the API.
Apache Iceberg requires data in a structured format, such as Parquet or ORC. Use a data processing library like Pandas in Python to convert the extracted JSON data into a DataFrame. Then, use a library like `pyarrow` to convert the DataFrame to a Parquet file. Ensure the schema matches your Iceberg table schema.
If you haven't already, set up an Apache Iceberg environment. This typically involves configuring a compute engine like Apache Spark or Flink that supports Iceberg. Ensure your environment is ready to create and manage Iceberg tables.
Use your chosen compute engine to load the Parquet files into Apache Iceberg. For instance, with Apache Spark, use the Spark DataFrame API to read the Parquet file and write it to Iceberg using the `writeTo` function, specifying the Iceberg table name.
After loading the data, perform checks to verify the integrity and completeness. Query the Iceberg tables to ensure that all data has been transferred accurately. Cross-reference with the original data in Monday.com to validate that no data is missing or corrupted.
This guide provides a self-contained process to manually move data from Monday.com to Apache Iceberg, utilizing direct API access and custom scripting without third-party connectors.
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:
