There are certain SQL concepts which you should be familiar with if you plan to attend an #SQL interview. No matter which RDBMS you use wether it is MySQL, Oracle, Microsoft SQL Server, #PostgreSQL or any other, these SQL concepts are common for all of the popular RDBMS.
Are you a wine enthusiast? If you are, then youβve probably heard of Wine Magazine.
π₯Let us solve one of the Advanced SQL interview questions on RANK function.
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β Question is taken from Wine Magazine that involves clauses like PARTITION BY, UNION ALL, CASE statements, and Window functions.
We will use a 3-step framework to solve the problem that can be used to solve any coding problem.
Find the cheapest and the most expensive variety in each region. Output the region along with the corresponding most expensive and the cheapest variety. Be aware that there are 2 region columns, the price from that row applies to both of them.
How should you π₯πππ«π§ ππ¨π°ππ« ππ? π
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It's easy to be overwhelmed by how broad #PowerBI isπ
If you're starting out, here's the path I recommendβ¬
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Begin by learning to organize data into tables, create relationships, and add calculated columns and measures.
This is the most important part of your journey, as understanding the #data is always the first step in Power BI development.
βΆοΈPractice Writing SQL Queries using Real
Dataset π―
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βThe very first thing, we must do when writing #SQL queries, is to understand the underlying data. Once we understand the data and how this data is stored across different tables, it becomes much simpler to write SQL #Queries to retrieve any information from that dataβ
β List of SQL Queries:
We shall write SQL #Queries using this data. For each of these queries, you would find the problem statement and then the screen shot of the expected output. Under each of these 20 problem statement
π¨βπ» 10 different ways to remove duplicate records in SQL
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1 - Delete using Unique Identifier.
2 - Using SELF join.
3 - Using Window function.
4 - Using MIN function. This delete even multiple duplicate records.
5 - Using backup table.
6 - Using backup table without dropping the original table.
7 - Delete using CTID.
8 - By creating a temporary unique id column.
6 Must have line charts for business data analysis ππ π―
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1. Indexed chart for comparing trend 2. #Sparkline for tiny insight 3. Spaghetti line #chart for bringing focus 4. Smoothed line chart for evening out edge 5. Forecast chart to predict future value based on trend & seasonality 6. Line chart combination when just a line won't do
βΆ Line charts are similar to #scatterplots except that they connect the data points with lines. Choose a line chart when ordering and joining the #data points by their X-axis values highlights meaningful changes in the vertical Y-axis variable.