Please open Telegram to view this post
VIEW IN TELEGRAM
Here are five of the most commonly used SQL queries in data science:
1. SELECT and FROM Clauses
- Basic data retrieval:
2. WHERE Clause
- Filtering data:
3. GROUP BY and Aggregate Functions
- Summarizing data:
4. JOIN Operations
- Combining data from multiple tables:
5. Subqueries and Nested Queries
- Advanced data retrieval:
1. SELECT and FROM Clauses
- Basic data retrieval:
SELECT column1, column2 FROM table_name;
2. WHERE Clause
- Filtering data:
SELECT * FROM table_name WHERE condition;
3. GROUP BY and Aggregate Functions
- Summarizing data:
SELECT column1, COUNT(*), AVG(column2) FROM table_name GROUP BY column1;
4. JOIN Operations
- Combining data from multiple tables:
SELECT a.column1, b.column2
FROM table1 a
JOIN table2 b ON a.common_column = b.common_column;
5. Subqueries and Nested Queries
- Advanced data retrieval:
SELECT column1
FROM table_name
WHERE column2 IN (SELECT column2 FROM another_table WHERE condition);
Please open Telegram to view this post
VIEW IN TELEGRAM
Please open Telegram to view this post
VIEW IN TELEGRAM
📖 Big Data Analytics tools
Big Data Analytics tools like Hadoop and Spark enable fast processing of massive datasets, while platforms like Tableau and Power BI help visualize insights. These tools empower businesses to make data-driven decisions in real-time.
Big Data Analytics tools like Hadoop and Spark enable fast processing of massive datasets, while platforms like Tableau and Power BI help visualize insights. These tools empower businesses to make data-driven decisions in real-time.
Please open Telegram to view this post
VIEW IN TELEGRAM
Data pipelines are a fundamental component of managing and processing data efficiently within modern systems. These pipelines typically encompass 5 predominant phases: Collect, Ingest, Store, Compute, and Consume.
1. Collect:
Data is acquired from data stores, data streams, and applications, sourced remotely from devices, applications, or business systems.
2. Ingest:
During the ingestion process, data is loaded into systems and organized within event queues.
3. Store:
Post ingestion, organized data is stored in data warehouses, data lakes, and data lakehouses, along with various systems like databases, ensuring post-ingestion storage.
4. Compute:
Data undergoes aggregation, cleansing, and manipulation to conform to company standards, including tasks such as format conversion, data compression, and partitioning. This phase employs both batch and stream processing techniques.
5. Consume:
Processed data is made available for consumption through analytics and visualization tools, operational data stores, decision engines, user-facing applications, dashboards, data science, machine learning services, business intelligence, and self-service analytics.
The efficiency and effectiveness of each phase contribute to the overall success of data-driven operations within an organization.
Please open Telegram to view this post
VIEW IN TELEGRAM
Please open Telegram to view this post
VIEW IN TELEGRAM
📖 SQL Joins - Part 1
📍Types of joins used very often includes -
✔️LEFT JOIN - All data from the left table but common data from the right table
✔️RIGHT JOIN - All data from right table and common data from the left table
✔️INNER JOIN - Only common data from both the tables
✔️OUTER JOIN - All the data from both the tables keeping null values with no common keys
✔️UNION - Stack table data on top of one another
✔️CROSS JOIN - All possible combinations of data from both the tables
📍Types of joins used very often includes -
✔️LEFT JOIN - All data from the left table but common data from the right table
✔️RIGHT JOIN - All data from right table and common data from the left table
✔️INNER JOIN - Only common data from both the tables
✔️OUTER JOIN - All the data from both the tables keeping null values with no common keys
✔️UNION - Stack table data on top of one another
✔️CROSS JOIN - All possible combinations of data from both the tables