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    Information Analytics Automation Scripts with SQL Saved Procedures

    Oliver ChambersBy No Comments4 Mins Read
    Information Analytics Automation Scripts with SQL Saved Procedures


    Information Analytics Automation Scripts with SQL Saved ProceduresPicture by Editor

     

    # Introduction

     
    Information has turn into a better commodity to retailer within the present digital period. With the benefit of getting plentiful knowledge for enterprise, analyzing knowledge to assist firms achieve perception has turn into extra vital than ever.

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

    The issue is that, typically, the question to amass the info we wish is complicated 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 straight inside the database. If you’re adept in Python, you may consider them as features: they encapsulate a collection 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 grasp SQL saved procedures, which allow us to simplify code and automate repetitive duties.

    Let’s attempt 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 referred to as finance_db with a desk referred to as stock_data.

    We will question the info utilizing one thing like the next.

    USE finance_db;

SELECT * FROM stock_data;

     

    Typically, 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 may see, the saved process can obtain parameters which are 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 $$
CREATE PROCEDURE AggregateStockMetrics(
    IN p_StartDate DATE,
    IN p_EndDate DATE
)
BEGIN
    SELECT
        COUNT(*) AS TradingDays,
        AVG(Shut) AS AvgClose,
        MIN(Low) AS MinLow,
        MAX(Excessive) AS MaxHigh,
        SUM(Quantity) AS TotalVolume
    FROM stock_data
    WHERE 
        (p_StartDate IS NULL OR Date >= p_StartDate)
      AND (p_EndDate IS NULL OR Date <= p_EndDate);
END $$
DELIMITER ;

     

    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 move. For the reason that saved process is saved within the database, you need to use 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 end result, 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()
    attempt:
        cursor.callproc('AggregateStockMetrics', [start_date, end_date])
        outcomes = []
        for end in cursor.stored_results():
            outcomes.lengthen(end result.fetchall())
        return outcomes
    lastly:
        cursor.shut()
        cnx.shut()

     

    The end result will probably be just like the output beneath.

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

     

    That’s all it is advisable to find out 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 technique to encapsulate complicated queries into dynamic, single-unit features that may be reused for repetitive knowledge analytics duties. The procedures are saved inside the database and are simple to make use of from totally different scripts or purposes comparable to Python.

    I hope this has helped!
     
     

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

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