What is data partitioning and sharding?

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If you're aspiring to become a certified the Best GCP Cloud Engineer, training in Hyderabad look no further than Quality Thoughts, Hyderabad’s premier institute for Google Cloud Platform (GCP) training. Our course is expertly designed to help graduates, postgraduates, and even working professionals from non-technical backgrounds, education gaps, or those looking to switch job domains build a strong foundation in cloud computing using GCP.

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Data Partitioning and Sharding

 are techniques used to split large datasets into smaller, manageable parts to improve performance, scalability, and maintainability.

Data Partitioning refers to dividing data into segments based on a specific criterion, such as date, region, or customer ID. In BigQuery, for example, partitioning can be done on a DATE column, allowing queries to scan only relevant partitions, which reduces cost and speeds up execution.

Sharding is a horizontal scaling technique where data is split across multiple databases or storage instances, often based on a hash or range of a key. Each shard operates independently, which helps in distributing load and handling large volumes of data across systems.

While both aim to handle big data efficiently, partitioning is typically within a single system, and sharding distributes data across systems. In practice, these methods are sometimes combined—partitioning within each shard—to further optimize performance and manageability in large-scale data processing systems like those built on GCP.

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