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Begin by exporting your Trello board data. Trello allows you to export boards in JSON format. Navigate to the Trello board you want to export, click on "Show Menu" > "More" > "Print and Export" > "Export as JSON". Save the JSON file to your local machine.
Download and install the ClickHouse client on your local machine. This is necessary for interacting with your ClickHouse database directly. You can find installation instructions on the official ClickHouse documentation website.
Determine the structure of your Trello data and create a corresponding table in ClickHouse to store the data. Use the ClickHouse client to execute SQL queries. For example:
```sql
CREATE TABLE trello_data (
id String,
name String,
description String,
due_date DateTime,
status String
) ENGINE = MergeTree()
ORDER BY id;
```
Write a script in a language such as Python to parse the exported JSON file. The script should extract relevant fields such as card ID, name, description, due date, and status. Python’s built-in `json` module can be used for this task.
Continue with the same script to transform the parsed data into a format suitable for SQL insertion into ClickHouse. Prepare the data as a list of tuples or a CSV string, ensuring that date and time formats match ClickHouse’s requirements.
Use the ClickHouse client or a Python library like `clickhouse-driver` to connect to your ClickHouse instance and execute an `INSERT` SQL command. For example, using Python:
```python
from clickhouse_driver import Client
client = Client('localhost')
client.execute('INSERT INTO trello_data VALUES', data) # 'data' is your transformed dataset
```
After inserting the data, run a `SELECT` query in the ClickHouse client to verify that all records have been correctly transferred and stored in the database. Check for any discrepancies or errors in data transformation and insertion:
```sql
SELECT * FROM trello_data;
```
This guide should help you manually transfer data from Trello to ClickHouse without relying on 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.
Trello is a web-based, Kanban-style, list-making application and is a subsidiary of Atlassian. Originally created by Fog Creek Software in 2011, it was spun out to form the basis of a separate company in 2014 and later sold to Atlassian in January 2017. The company is based in New York City.
Trello's API provides access to a wide range of data related to boards, cards, lists, members, and organizations. Here are the categories of data that Trello's API gives access to:
- Boards: Information about boards, including their name, description, URL, and members.
- Cards: Details about individual cards, such as their name, description, due date, and attachments.
- Lists: Information about lists, including their name, position, and cards.
- Members: Data related to members, such as their name, email address, and avatar URL.
- Organizations: Details about organizations, including their name, description, and members.
In addition to these categories, Trello's API also provides access to data related to actions, checklists, labels, and more. With this data, developers can build custom integrations and applications that interact with Trello in a variety of ways. For example, they can create custom reports, automate workflows, or build dashboards that display Trello data in real-time.
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: