Information Analytics Automation Scripts with SQL Saved Procedures

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# Introduction

 
Information has turn out to be a better commodity to retailer within the present digital period. With the benefit of getting considerable knowledge for enterprise, analyzing knowledge to assist corporations acquire perception has turn out to be extra vital than ever.

In most companies, knowledge is saved inside a structured database, and SQL is used to accumulate it. With SQL, we will question knowledge within the kind we wish, so long as the script is legitimate.

The issue is that, generally, the question to accumulate the info we wish is advanced and never dynamic. On this case, we will use SQL saved procedures to streamline tedious scripts into easy callables.

This text discusses creating knowledge analytics automation scripts with SQL saved procedures.

Curious? Right here’s how.

# SQL Saved Procedures

 
SQL saved procedures are a group of SQL queries saved instantly throughout the database. In case you are adept in Python, you’ll be able to consider them as features: they encapsulate a sequence of operations right into a single executable unit that we will name anytime. It’s helpful as a result of we will make it dynamic.

That’s why it’s useful to know SQL saved procedures, which allow us to simplify code and automate repetitive duties.

Let’s strive it out with an instance. On this tutorial, I’ll use MySQL for the database and inventory knowledge from Kaggle for the desk instance. Arrange MySQL Workbench in your native machine and create a schema the place we will retailer the desk. In my instance, I created a database known as finance_db with a desk known as stock_data.

We are able to question the info utilizing one thing like the next.

USE finance_db;

SELECT * FROM stock_data;

Basically, a saved process has the next construction.

DELIMITER $$
CREATE PROCEDURE procedure_name(param_1, param_2, . . ., param_n)
BEGIN
    instruct_1;
    instruct_2;
    . . .
    instruct_n;
END $$
DELIMITER ;

As you’ll be able to see, the saved process can obtain parameters which might be handed into our question.

Let’s look at an precise implementation. For instance, we will create a saved process to mixture inventory metrics for a particular date vary.

USE finance_db;
DELIMITER $$
CREATE PROCEDURE AggregateStockMetrics(
    IN p_StartDate DATE,
    IN p_EndDate DATE
)
BEGIN
    SELECT
        COUNT(*) AS TradingDays,
        AVG(Shut) AS AvgClose,
        MIN(Low) AS MinLow,
        MAX(Excessive) AS MaxHigh,
        SUM(Quantity) AS TotalVolume
    FROM stock_data
    WHERE 
        (p_StartDate IS NULL OR Date >= p_StartDate)
      AND (p_EndDate IS NULL OR Date 

Within the question above, we created the saved process named AggregateStockMetrics. This process accepts a begin date and finish date as parameters. The parameters are then used as situations to filter the info.

You’ll be able to name the saved process like this:

CALL AggregateStockMetrics('2015-01-01', '2015-12-31');

The process will execute with the parameters we go. For the reason that saved process is saved within the database, you should utilize it from any script that connects to the database containing the process.

With saved procedures, we will simply reuse logic in different environments. For instance, I’ll name the process from Python utilizing the MySQL connector.

To do this, first set up the library:

pip set up mysql-connector-python

Then, create a operate that connects to the database, calls the saved process, retrieves the outcome, and closes the connection.

import mysql.connector

def call_aggregate_stock_metrics(start_date, end_date):
    cnx = mysql.connector.join(
        person="your_username",
        password='your_password',
        host="localhost",
        database="finance_db"
    )
    cursor = cnx.cursor()
    strive:
        cursor.callproc('AggregateStockMetrics', [start_date, end_date])
        outcomes = []
        for lead to cursor.stored_results():
            outcomes.lengthen(outcome.fetchall())
        return outcomes
    lastly:
        cursor.shut()
        cnx.shut()

The outcome will likely be much like the output under.

[(39, 2058.875660431691, 1993.260009765625, 2104.27001953125, 140137260000.0)]

That’s all it is advisable learn about SQL saved procedures. You’ll be able to lengthen this additional for automation utilizing a scheduler in your pipeline.

# Wrapping Up

 
SQL saved procedures present a way to encapsulate advanced queries into dynamic, single-unit features that may be reused for repetitive knowledge analytics duties. The procedures are saved throughout the database and are simple to make use of from totally different scripts or purposes equivalent to Python.

I hope this has helped!
 
 

Cornellius Yudha Wijaya is a knowledge science assistant supervisor and knowledge author. Whereas working full-time at Allianz Indonesia, he likes to share Python and knowledge suggestions by way of social media and writing media. Cornellius writes on a wide range of AI and machine studying subjects.