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Data Science
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Learn how to analyze data effectively and manage databases with ease. Buy ads: https://telega.io/c/sql_databases
Mostrar más📈 Análisis del canal de Telegram Data Science
El canal Data Science (@sql_databases) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 71 041 suscriptores, ocupando la posición 2 273 en la categoría Educación y el puesto 4 764 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 71 041 suscriptores.
Según los últimos datos del 05 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -54, y en las últimas 24 horas de 6, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 12.21%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.97% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 8 672 visualizaciones. En el primer día suele acumular 2 110 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 0.
- Intereses temáticos: El contenido se centra en temas clave como database, learning, linkedin, udemy, 029k|.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn how to analyze data effectively and manage databases with ease.
Buy ads: https://telega.io/c/sql_databases”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 06 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
71 041
Suscriptores
+624 horas
+237 días
-5430 días
Archivo de publicaciones
71 041
📖 Top 10 Database Scaling Techniques You Should Know:
1. 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠: Create indexes on frequently queried columns to speed up data retrieval.
2. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠: Upgrade your database server by adding more CPU, RAM, or storage to handle increased load.
3. 𝐂𝐚𝐜𝐡𝐢𝐧𝐠: Store frequently accessed data in-memory (e.g., Redis, Memcached) to reduce database load and improve response time.
4. 𝐒𝐡𝐚𝐫𝐝𝐢𝐧𝐠: Distribute data across multiple servers by splitting the database into smaller, independent shards, allowing for horizontal scaling and improved performance.
5. 𝐑𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Create multiple copies (replicas) of the database across different servers, enabling read queries to be distributed across replicas and improving availability.
6. 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Fine-tune SQL queries, eliminate expensive operations, and leverage indexes effectively to improve execution speed and reduce database load.
7. 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧 𝐏𝐨𝐨𝐥𝐢𝐧𝐠: Reduce the overhead of opening/closing database connections by reusing existing ones, improving performance under heavy traffic.
8. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐏𝐚𝐫𝐭𝐢𝐭𝐢𝐨𝐧𝐢𝐧𝐠: Split large tables into smaller, more manageable parts (partitions), each containing a subset of the columns/features from the original table.
9. 𝐃𝐞𝐧𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Store data in a redundant but structured format to minimize complex joins and speed up read-heavy workloads.
10. 𝐌𝐚𝐭𝐞𝐫𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐕𝐢𝐞𝐰𝐬: Pre-compute and store results of complex queries as separate tables to avoid expensive recalculation, reducing database load and improving response times.
71 041
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🔰 The 4 Types of SQL Joins
SQL joins combine rows from two or more tables based on a related column. Here are the different types of joins you can use:
1⃣ Inner Join
Returns only the matching rows between both tables. It keeps common data only.
🔢 Left Join
Returns all rows from the left table and matching rows from the right table. If a row in the left table doesn’t have a match in the right table, the right table’s columns will contain NULL values in that row.
🔢 Right Join
Returns all rows from the right table and matching rows from the left table. If no matching record exists in the left table for a record in the right table, the columns from the left table in the result will contain NULL values.
🔢 FULL OUTER JOIN
Returns all rows from both tables, filling in NULL for missing matches.
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📊 Your Data Analyst journey doesn’t start with tools — it starts with a roadmap.
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+1
80% of data problems can be solved with just 16 SQL functions.
I’ve been working with data for years and this truth keeps proving itself:
You don’t need fancy tools.
You need to master the fundamentals.
For data analysts, data scientists, and data engineers:
SQL isn’t optional.
Because data lives in databases.
And databases speak SQL-ish.
Most problems fall into 2 categories:
Aggregate functions (summarise data):
SUM() - Total revenue
COUNT() - Total orders
AVG() - Average purchase value
MIN() - Smallest sale
MAX() - Biggest transaction
STRING_AGG() - Combine text values
Window functions (compare rows):
ROW_NUMBER() - Pagination
RANK() - Leaderboards with ties
DENSE_RANK() - Performance tiers
NTILE() - Split into quartiles
LEAD() - Compare current vs next
LAG() - Compare current vs previous
FIRST_VALUE() - Highest value per group
LAST_VALUE() - Lowest value per group
SUM() OVER() - Running totals
AVG() OVER() - Moving averages
Aggregates collapse rows → one summary result
Window functions keep all rows → add calculations across them
