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Begin by logging into your Everhour account. Navigate to the reporting section and utilize the export feature to download the data you need. Everhour allows exporting data in various formats such as CSV or Excel, both of which are suitable for importing into a MySQL database. Choose the CSV format for ease of use in the subsequent steps.
Open the exported CSV file in a spreadsheet application like Excel or Google Sheets. Review the data to ensure all necessary information is included and clean up any inconsistencies or errors. Verify that the data types (such as dates and numbers) are correct and consistent. Save the cleaned file as a CSV if using a spreadsheet application for editing.
If you do not already have a MySQL database set up, you need to create one. Install MySQL Server on your local machine or a server if necessary. Use the MySQL Workbench or command line to create a new database. For example, you can use the command:
```sql
CREATE DATABASE everhour_data;
```
Define a new table structure in your MySQL database to match the columns of your CSV file. Use appropriate data types for each column based on the data in your CSV file. Here is an example SQL command to create a table:
```sql
CREATE TABLE time_entries (
id INT PRIMARY KEY,
project_name VARCHAR(255),
user_name VARCHAR(255),
hours DECIMAL(5,2),
date DATE
);
```
Adjust the column names and data types according to your CSV file's structure.
Use the MySQL `LOAD DATA INFILE` command to import the CSV data into your MySQL table. Ensure that the CSV file is accessible to the MySQL server. Here is an example of how to load the data:
```sql
LOAD DATA INFILE '/path/to/your/csvfile.csv'
INTO TABLE time_entries
FIELDS TERMINATED BY ','
ENCLOSED BY '"'
LINES TERMINATED BY '\n'
IGNORE 1 ROWS;
```
Replace `/path/to/your/csvfile.csv` with the actual path to your CSV file. The `IGNORE 1 ROWS` clause is used to skip the header row of the CSV file.
Once the data is loaded into the MySQL table, verify its integrity. Run a few SELECT queries to check that the data has been imported correctly. Check for any discrepancies in the data types, missing values, or truncation issues. For example:
```sql
SELECT * FROM time_entries LIMIT 10;
```
To facilitate future data imports, write a script or batch file that automates the export from Everhour, data cleaning, and importing into MySQL. This could involve using shell scripting, Python, or any other scripting language you are comfortable with. Schedule this script to run periodically using a task scheduler (like cron jobs in Unix-based systems or Task Scheduler in Windows) to keep your MySQL database updated with the latest data from Everhour.
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.
Everhour is a time tracking and project management tool that helps businesses and teams to manage their time more efficiently. It integrates with popular project management tools like Asana, Trello, and Basecamp, allowing users to track time spent on tasks and projects directly from those platforms. Everhour also offers features like budget tracking, invoicing, and reporting, giving businesses a comprehensive view of their time and project management. With Everhour, teams can easily collaborate, manage their workload, and stay on top of deadlines, ultimately improving productivity and profitability.
Everhour's API provides access to a wide range of data related to time tracking and project management. The following are the categories of data that can be accessed through Everhour's API:
1. Time tracking data: This includes data related to the time spent on tasks, projects, and clients.
2. Project management data: This includes data related to projects, tasks, and subtasks, such as their status, due dates, and assignees.
3. User data: This includes data related to users, such as their name, email address, and role.
4. Billing data: This includes data related to billing, such as the amount billed, the currency used, and the payment status.
5. Reporting data: This includes data related to reports, such as the type of report, the date range, and the data included in the report.
6. Integration data: This includes data related to integrations with other tools, such as the name of the integration, the status, and the configuration settings.
Overall, Everhour's API provides a comprehensive set of data that can be used to track time, manage projects, and analyze performance.
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:





