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1. ๐๐ง๐๐๐ฑ๐ข๐ง๐ : Create indexes on frequently queried columns to speed up data retrieval.
2. ๐๐๐ซ๐ญ๐ข๐๐๐ฅ ๐๐๐๐ฅ๐ข๐ง๐ : Upgrade your database server by adding more CPU, RAM, or storage to handle increased load.
3. ๐๐๐๐ก๐ข๐ง๐ : Store frequently accessed data in-memory (e.g., Redis, Memcached) to reduce database load and improve response time.
4. ๐๐ก๐๐ซ๐๐ข๐ง๐ : Distribute data across multiple servers by splitting the database into smaller, independent shards, allowing for horizontal scaling and improved performance.
5. ๐๐๐ฉ๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง: Create multiple copies (replicas) of the database across different servers, enabling read queries to be distributed across replicas and improving availability.
6. ๐๐ฎ๐๐ซ๐ฒ ๐๐ฉ๐ญ๐ข๐ฆ๐ข๐ณ๐๐ญ๐ข๐จ๐ง: Fine-tune SQL queries, eliminate expensive operations, and leverage indexes effectively to improve execution speed and reduce database load.
7. ๐๐จ๐ง๐ง๐๐๐ญ๐ข๐จ๐ง ๐๐จ๐จ๐ฅ๐ข๐ง๐ : Reduce the overhead of opening/closing database connections by reusing existing ones, improving performance under heavy traffic.
8. ๐๐๐ซ๐ญ๐ข๐๐๐ฅ ๐๐๐ซ๐ญ๐ข๐ญ๐ข๐จ๐ง๐ข๐ง๐ : Split large tables into smaller, more manageable parts (partitions), each containing a subset of the columns/features from the original table.
9. ๐๐๐ง๐จ๐ซ๐ฆ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง: Store data in a redundant but structured format to minimize complex joins and speed up read-heavy workloads.
10. ๐๐๐ญ๐๐ซ๐ข๐๐ฅ๐ข๐ณ๐๐ ๐๐ข๐๐ฐ๐ฌ: Pre-compute and store results of complex queries as separate tables to avoid expensive recalculation, reducing database load and improving response times.
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Ex_Files_Intro_to_PostgreSQL.zip
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Whether youโre building models, leading teams, or breaking into the field โ there are a few core concepts you need to understand deeply (not just mention in interviews).
In this carousel, we break down:
โ Supervised vs Unsupervised learning
โ Overfitting & underfitting
โ Cross-validation strategies
โ Precision vs recall trade-offs
โ Feature engineering techniques
โ Dimensionality reduction methods
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