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Sharded Data

Sharded Data:


Sharding is a database partitioning technique that divides a large dataset into smaller, more manageable pieces, called shards. Each shard is stored on a separate database server or node. Sharding is used to improve the scalability, performance, and availability of a database.


Benefits of Sharding:


Tools and Products for Sharded Data:

1. Vitess:

2. CockroachDB:

3. MongoDB Sharding:

4. Horizontal Pod Autoscaler (HPA):

5. ProxySQL:

6. ShardingSphere:

7. Atlas Search:

Related Terms to Sharded Data:

Other related terms include:


Prerequisites for Sharding Data:

In addition to the above, it is important to have a clear understanding of the requirements of the application that will be using the sharded data. This includes the expected traffic volume, the types of queries that will be performed, and the performance and availability requirements.

What’s next?

Next Steps After Sharding Data:

  1. Performance Monitoring:
  1. Scalability Planning:
  1. Data Consistency Management:
  1. Disaster Recovery Planning:
  1. Schema Changes:
  1. Application Development and Optimization:
  1. Security and Compliance:
  1. Ongoing Maintenance and Optimization: