Data analysis has always been a challenging task, and handling multiple datasets at once further complicates things. Imagine having three diverse tables, each holding crucial employee data. Your task is to condense these into a single table: employee data, project assignment data, and project performance data

The first set, employee data, features records about every worker. The project assignment data delivers specifics about the projects each employee is working on, and the project performance data records how each employee is performing in their respective projects.

Our goal here is to combine these three datasets into one. We want a single table that provides an employee's name, position, employee number, the projects they are engaged in, and the performance metrics for each project.

First, let's quickly review our SQL data. By running the 'Select * from' query on each table, we can examine the datasets. Executing the query on 'employee data', we found ten entries, with each one presenting details about a unique worker. Running the query on 'project assignment data' yielded 60 records indicating that employees are assigned to five different projects.

SELECT * FROM employee_data; SELECT * FROM project_assignment_data; SELECT * FROM project_performance_data;

Now, for the merging process. We'll consider employee data as the initial or primary table. We'll assign this table an alias- 'ED'. This procedure facilitates an easy reference to the columns of this table like ED.employee number, ED.position, ED. name

The next stage involves connecting this table with the project assignment data. Here, we can use the 'left join' command, with 'project assignment data' given the alias 'PAD'. The identifiers used for joining are ED.employee number and PAD.employee number.

SELECT * FROM employee_data ED LEFT JOIN project_assignment_data PAD ON ED.employee_number = PAD.employee_number;

The new query's output should ideally be 60 records, given each employee is assigned five projects. However, you might notice it only yields 50 records. This is due to the exclusion of data related to employee numbers that are not present in the employee data table.

Next, we need to include performance metrics for each project. Another 'left join' command connects the project performance data, which acquires its alias - 'PPD'. Executing this command successfully shows a table with 50 entries, where every employee's respective performance metrics are aligned beside their name.

SELECT * FROM employee_data ED LEFT JOIN project_assignment_data PAD ON ED.employee_number = PAD.employee_number LEFT JOIN project_performance_data PPD ON (ED.employee_number = PPD.employee_number AND PAD.project_number = PPD.project_number);

The technique of multi-column joining is working here. We are merging tables using more than one shared identifier, hence we use brackets and place these identifiers within them.

Why are we using identifiers from both project assignment data and project performance data? Why can't we restrict the identifiers only to the employee numbers? Eliminating the 'and' part in the identifier will lead to recording more data than necessary, and the database will duplicate for each employee and project because the employee data is cross-multiplying with project assignment data and project performance data.

We need specific and distinct data for employees involved in diverse projects. Thus, employing multiple columns in the join statement rectifies this situation, guaranteeing we get exclusive entries for each worker, aiding in minimising data redundancy and optimising the extraction process.

With the right strategies and tools, what seemed like an intimidating process of merging several datasets can turn into a manageable task. The detailed step-by-step process I've shared shows how we can harness the power of SQL commands to pull together data from multiple tables effectively. Using such techniques can help ensure you extract accurate information without redundancy. Remember, the key lies in identifying common identifiers and using them wisely in the join statement. But what if I told you there's a more comfortable way?

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