Data Engineering

In this enactment, you achieve cunning the consultations to await basis in the CSVs, drift the CSVs into a SQL basisbase, and computeerpart questions encircling the basis. In other signification, you achieve perform:

  1. Data Engineering
  2. Data Analysis

Note: You may heed the expression "Data Modeling" in situate of "Data Engineering," but they are the similar expressions. Basis Engineering is the past later wording instead of Basis Modeling.

Data Modeling

Inspect the CSVs and carriageray out an ERD of the consultations. Feel frank to use a instrument enjoy

Data Engineering

  • Use the knowledge you possess to beget a consultation schema for each of the six CSV improves. Remember to mention basis types, chief keys, extraneous keys, and other constraints.
    • For the chief keys hinder to see if the post is rare, otherwise beget a composite key. Which accepts to chief keys in regulate to rarely substantiate a row.
    • Be secure to beget consultations in the chasten regulate to treat extraneous keys.
  • Imcarriage each CSV imargue into the selfcorresponding SQL consultation. Note be secure to drift the basis in the similar regulate that the consultations were begetd and statement for the headers when drifting to quit errors.

Data Analysis

Once you possess a exhaustive basisbase, do the subjoined:

  1. List the subjoined details of each employee: employee reckon, conclusive spectry, highest spectry, sex, and stipend.
  2. List highest spectry, conclusive spectry, and employ conclusion for employees who were employd in 1986.
  3. List the superintendent of each carriageion delay the subjoined knowledge: carriageion reckon, carriageion spectry, the superintendent's employee reckon, conclusive spectry, highest spectry.
  4. List the carriageion of each employee delay the subjoined knowledge: employee reckon, conclusive spectry, highest spectry, and carriageion spectry.
  5. List highest spectry, conclusive spectry, and sex for employees whose highest spectry is "Hercules" and conclusive spectrys arise delay "B."
  6. List all employees in the Sales carriageion, including their employee reckon, conclusive spectry, highest spectry, and carriageion spectry.
  7. List all employees in the Sales and Development carriageions, including their employee reckon, conclusive spectry, highest spectry, and carriageion spectry.
  8. In descending regulate, inventory the estimate compute of employee conclusive spectrys, i.e., how frequent employees distribute each conclusive spectry.

Bonus (Optional)

As you discuss the basis, you are conquer delay a creeping distrust that the basisset is fake. You suppose that your boss operativeed you ungenuine basis in regulate to experience the basis engineering skills of a new employee. To stabilitate your slice, you run to accept the subjoined strides to breed a visualization of the basis, delay which you achieve menace your boss:

  1. Imcarriage the SQL basisbase into Pandas. (Yes, you could learn the CSVs instantly in Pandas, but you are, succeeding all, up-hill to argue your technical ardor.) This stride may demand some inquiry. Feel frank to use the enactment under to get afloat. Be secure to gain any certain modifications for your username, password, army, carriage, and basisbase spectry:
    from sqlalchemy drift beget_engine engine = beget_engine('postgresql://localhost:5432/<your_db_name>') relevance = engine.connect()
  1. Create a histogram to visualize the most dishonorable stipend ranges for employees.
  2. Create a bar chart of middle stipend by heading.


Evidence in operative, you lag into your boss's duty and introduce the visualization. Delay a sly titter, your boss donation you for your effort. On your way out of the duty, you heed the signification, "Search your ID reckon." You seem down at your mark to see that your employee ID reckon is 499942.

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