Sharding is a type of database partitioning, which divides the large databases into smaller and easily available chunks called shards. In RDBMS, it is widely known as horizontal partitioning. It’s basically splitting and maintaining the database by rows rather than columns.

As the amount of data an organization STORES increases and when the amount of data needed to run the business exceeds the current capacity of the ENVIRONMENT, some mechanism for breaking the information into manageable chunks is required. With NoSQL solutions, organizations have started practicing automatic sharding TECHNIQUES as a mean to continue to store data while minimizing downtime.

The loads of the required system can be elastically managed using automatic sharding. With smart technologies around, it is possible to configure the system proactively, which can automatically create shards based on demand. The strategy may vary depending upon the type of data, users information and users DISTRIBUTION across regions. For example, if you have a site with large user base having maximum active users from US region than Asia, then it make sense to shard your database from regional perspective.

Sharding is a type of database partitioning, which divides the large databases into smaller and easily available chunks called shards. In RDBMS, it is widely known as horizontal partitioning. It’s basically splitting and maintaining the database by rows rather than columns.

As the amount of data an organization stores increases and when the amount of data needed to run the business exceeds the current capacity of the environment, some mechanism for breaking the information into manageable chunks is required. With NoSQL solutions, organizations have started practicing automatic sharding techniques as a mean to continue to store data while minimizing downtime.

The loads of the required system can be elastically managed using automatic sharding. With smart technologies around, it is possible to configure the system proactively, which can automatically create shards based on demand. The strategy may vary depending upon the type of data, users information and users distribution across regions. For example, if you have a site with large user base having maximum active users from US region than Asia, then it make sense to shard your database from regional perspective.