Database Transformation: Scheduling Data for Reporting Purposes

In today’s fast-paced data-driven world, organizations rely on reliable data transformation processes to extract insights from their data. One common use case is generating reports that require scheduling of data from existing tables in a database. In this article, we’ll explore the process of transforming your data by creating separate tables for daily schedules and provide a step-by-step guide on how to achieve this.

Understanding Your Data

The given SQL table contains information about employees’ check-in and check-out times at work. The structure is as follows:

ID  | Name   | Surname | CheckInTime       | CheckOutTime
----|--------|---------|-------------------|-----------------
123 | John   | Johnson | 2019-07-05 12:30 | 2019-07-05 15:30
123 | John   | Johnson | 2019-07-06 11:30 | 2019-07-06 14:30
124 | Paul   | Johnson | 2019-07-01 11:30 | 2019-07-01 12:30

The goal is to generate a report that shows the day of each employee’s check-in and check-out, with daily entries from July 1st to July 31st.

Solution Overview

We’ll use a combination of SQL queries and database design to transform your data. We’ll create two separate tables: daily_schedule and employee_data. The first table will store the daily schedule for each day of the month, while the second table will contain all employee data from the original table.

Table Design

We’ll use the following schema:

**Employee Data**
+---------+
| ID      |
+---------+
| 123     |
| 124     |

+---------+--------+----------+------------+
| Name    | Surname| CheckInTime       | CheckOutTime
+---------+--------+----------+------------+
| John    | Johnson | 2019-07-05 12:30 | 2019-07-05 15:30 |
| Paul    | Johnson | 2019-07-01 11:30 | 2019-07-01 12:30|
+---------+--------+----------+------------+

**Daily Schedule**
+---------+---------------+-------------------+---------------+
| Date    | Employee ID   | CheckInTime       | CheckOutTime
+---------+---------------+-------------------+---------------+
| 2019-07-01 | 123           |                   | 
| 2019-07-01 | 124           | 11:30             |
| 2019-07-02 | 123           |                   | 
| ...      | ...           |                   | 
| 2019-07-31 | 123           |                   |
+---------+---------------+-------------------+---------------+

SQL Queries

Creating the daily_schedule table

We’ll use a combination of SELECT, GROUP BY, and DATE_FORMAT to populate the daily schedule:

CREATE TABLE daily_schedule AS
WITH employee_data AS (
  SELECT ID, Name, Surname, CheckInTime, CheckOutTime
  FROM employee_data
),
daily_entries AS (
  SELECT Date, EmployeeID, CheckInTime, CheckOutTime
  FROM employee_data
)
SELECT 
  DATE_FORMAT(Date, '%Y-%m-%d') AS Date,
  EmployeeID,
  IFNULL(CheckInTime, 'No Data') AS CheckInTime,
  IFNULL(CheckOutTime, 'No Data') AS CheckOutTime
FROM daily_entries
GROUP BY EmployeeID, DATE_FORMAT(DATE, '%Y-%m-%d')

Creating the employee_data table

We’ll use a simple SELECT to extract employee data from the original table:

CREATE TABLE employee_data AS (
  SELECT ID, Name, Surname, CheckInTime, CheckOutTime
  FROM employee_data
)

Data Transformation Process

The transformation process involves several steps:

1. Extracting Employee Data: We’ll extract all employee data from the original table into a new table called employee_data.
2. Creating Daily Schedule: We’ll use a combination of SQL queries to create the daily schedule for each day of the month.
3. Populating Daily Schedule: We’ll populate the daily schedule with employee data, using the daily_entries CTE to group by date and employee ID.

Benefits

The proposed solution has several benefits:

• Simplified Data Transformation: By creating two separate tables, we can simplify the data transformation process and avoid complex calculations.
• Improved Data Integrity: The daily schedule table will ensure that each day of the month is represented accurately, reducing errors and inconsistencies.
• Flexibility: The proposed solution allows for easy extension to future months or years, eliminating the need for manual updates.

Conclusion

Transforming your data by creating separate tables for daily schedules is a viable approach for generating reports. By following the steps outlined in this article, you can simplify your data transformation process and ensure accurate results. Remember to test your solution thoroughly to identify any potential issues and refine your approach as needed.

Last modified on 2025-04-04