Day 1 of 15 Days of Advanced SQL Series
Both Basics and Advanced SQL covered…
Hello peeps. Hope all’s going well. It’s a smooth week at work and I’m traveling.
As we are nearing to the end of 30 days of Data Structures & Algorithms and System Design ( links below); here comes 15 days of Advanced SQL Series.
Day 2 : SQL Basics, Query Structure, Built In functions Conditions
Day 4 : Set Theory Operations, Stored Procedures and CASE statements in SQL
Day 6 : Subqueries, Group by, order by and Having clauses in SQL and Analytical Functions
Day 7 : Window Functions, Grouping Sets and Constraints in SQL
Day 8 : BigQuery Basics, SELECT, FROM, WHERE and Date and Extract in BigQuery
Day 9 : Common Expression Table, UNNEST Clause, SQL vs NoSQL Databases
Day 10 : Triggers, Pivot and Cursors in SQL
Day 14 : MySQL in Depth
Day 15 : PostgreSQL inDepth
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Complete Data Structures and Algorithm Series
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SQL is a must know skill when it comes to data retrieval and manipulation.
SQL (Structured Query Language) is a programming language used for managing and manipulating relational databases. It is used to insert, update, and query data in a database.
SQL commands include SELECT, INSERT, UPDATE, and DELETE, among others. It is widely used in businesses and organizations of all sizes, and is the standard language for relational database management systems such as MySQL, Oracle, and Microsoft SQL Server.
- SQL works by allowing users to interact with a database through a series of commands. These commands are used to insert, update, and retrieve data from the database. The basic structure of an SQL statement includes a command, followed by the name of the table or tables being affected, and any conditions or constraints that need to be met.
- When an SQL statement is executed, the database management system (DBMS) parses the statement and translates it into a series of instructions that the database can understand. The DBMS then communicates with the database and retrieves or modifies the data as specified by the SQL statement.
- For example, a SELECT statement is used to retrieve data from a database table. The statement specifies the columns and rows that should be retrieved, as well as any conditions that need to be met. The DBMS then retrieves the requested data and returns it to the user in a structured format.
- SQL is declarative, meaning that you specify what you want the outcome to be, but not how the DBMS should get there. The SQL engine optimizes the query and execute the most efficient plan to obtain the desired result.
In summary, SQL allows users to interact with relational databases by sending commands to the DBMS, which in turn communicates with the database to retrieve or modify data based on the SQL statement.
It’s also a part of the Data Engineering series and Data Analytics Series which is running in parallel.
I use SQL everyday at my work. So, in this series I’ll cover what’s really important for you to know to use it in the real life projects/at work.
Goal
Let’s set a clear objective.
The goal is to develop an intuition and understand (in the depth) the practical side of Advanced SQL. We would be writing queries.
Topics that I’ll cover —
SQL Basics
Aggregations
Window Functions
BigQuery
Advanced Functions
Performance Tuning SQL Queries
MySQL, PostgreSQL and MongoDB
Github for Advanced SQL that you can follow —
That’s it for now.
Find Day 2 Below:
Let me know if you have questions in the comment section below. Subscribe/ Follow, Like/Clap as it would encourage me to write more in my free time
Stay Tuned!!
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If you are interested, you can join my newsletter through which I send tech interview tips, techniques, patterns, hacks — Software Development, ML, Data Science, Startups and Technology projects to more than 30K readers. You can subscribe to Tech Brew :
30 days of Data Analytics Series —
Day 1 : Data Analytics basics and kickstart of Data analytics with projects series
Day 3 : Data Analytics Ecosystem — Data Life Cycle, Data Analysis complete process ( most important things)
Day 5 : Statistics
Day 6 : Basic and Advanced SQL
Day 8 : Pandas and Numpy
Day 9 : Data Manipulation
Day 10 : Data Visualization — Part 1
Day 11 : Project 1 : Data Visualization — Part 2
Day 12 : Data Visualization — Part 3
Day 13: Tableau — Part 1
Day 14: Tableau — Part 2
Day 15: Tableau — Part 3
Day 16 : Data Analysis Project 2
Day 17 : Data Analysis Project 3
Day 18: Data Analysis Project 4
Day 20 : Data Analysis Project 6
Day 21 : Data Analysis Project 7
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For Python Projects —
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