In above example, productID field is unique for each record. In addition, table in a relational database consists of an attribute which uniquely identifies each record. Here is a sample product table:PproductIDProduct_Nameprice1Pen202Pencil103Book100Each column, representing attribute of entity is also called field in database terminology. One row of product table describes one example of product. The product table consists of columns, each corresponding to attribute. Name of product and its price are attributes of product entity. There may be a one to one or one to many type of relationship between two tables.Primary keyAn entity is characterized by attributes. It also involves identifying relationship amongst the tables. Generally, each table represents one entity.Relational DatabaseRelational database design involves identifying independent entities involved and defining table structure for each entity. In a relational database, data is organised in one or more tables (called relations). Relational database model was proposed by E.F.Codd in 1970. This leads to data redundancy (unnecessary repetition of data in multiple files) and compromises data integrity.Database is a program independent and organised collection of data which removes data redundancy and ensures data integrity. However, data files are largely unstructured. In order to retain the data permanently so that it can be retrieved and used whenever required, it is stored in computer files.Computer files may store data in human readable text form, or computer program may apply certain encryption on it before storing. Data provided by user as well as data generated during or as a result of processing is stored in computer’s memory (RAM) which is temporary in nature. You can analyze every dataset using this method told elbow and ensure your analysis results.Python Tutorial By KnowledgeHut Any computer program requires data for processing. Hence, we are sure that our analysis is true. In this article, we analyzed the data using Python, SQL, and Tableau simultaneously and got the same results. It is very important for data analyst dummies whether they analyze data true or not. These results are shown in descending form as python and SQL results. As we see, there are three brands same as the result we get from python and SQL results. For example, the product named Surly’s mean product price is 1332 rounded. We see the products’ average prices more than 1000 below. We use brand_id and brand_name columns from product.brand table and use the list_price column from the order_item table as seen in Table 12. We use product.brand and order_item tables again. Now, we analyze the question with Tableau. Table-11: Analyze Result With SQL Step-3: Show With Tableau In this step, we create a new python file as Jupyter Notebook named “ConnectionToSQLServerWithPython.ipynb” which we save intoĬ:\Users\TOSHIBA\Desktop\CLARUSWAY\ML\MyProjects\SampleSales. Step 1: Create a Python File as Jupyter Notebook It is shown the steps of this phase below. Now we connect to the database using python scripts. Table-1: Database Schema in Microsoft SQL Server Management Studio 2. These table names are product.brand, product.category, product.stock, sale.customer, sale.order_item, sale.orders, sale.staff, sale.store It is based on a relational database management system (RDBMS). The table shows the connection between the nine tables. Creating SQL Schemaįirst, we prepare the database schema beforehand, as shown in Table 1 below. We also analyze data from the database using Microsoft SQL Server Management Studio. We connect to the database, pull data, and analyze it using Python and Tableau. There are three stores total of 10 persons. There is a selling bicycle data in the SQL database in which there are nine brands, seven categories, and 321 products of bicycles. In this article, you will learn to match your analysis results using Python, SQL, and Tableau and try to get the same results. Therefore, you should check whether our analysis results are accurate or not. How to Analyze Data Using Python, SQL, and Tableau SimultaneouslyĪ bad strategy means losing money for a company.
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