![]() ![]() As data grows both in volume and importance, sharding helps businesses address the data growth challenge by splitting the dataset into logical pieces and running multiple datasets simultaneously. See More: Why Enterprises Should Move on from Legacy Database Infrastructure What Is Database Sharding?Īdi Gelvan, the CEO of Speedb, an Israeli startup providing drop-in data engines for NoSQL, says database sharding is a relatively simple way to store larger data sets and handle the increased load by separating the database into smaller parts. Here, we look at what database sharding means, how it can be optimally leveraged, and its alternatives. Database sharding has often been used to meet these needs. Whether hosted on-premise or in the cloud, data needs to be structured, organized and backed up to reduce inefficiencies, prepare for outages, and save costs. The need to store, secure, structure, and analyze data is one of the top priorities for IT strategists, considering how vital historical data is for strategic decision-making and measuring performance. However, as it turns out, for specific use cases, on-premise solutions are becoming more affordable than the cloud itself. The arrival of cloud services and intense competition in the sector enabled organizations to find an alternative to expensive on-premise storage options. The proliferation of smart devices worldwide, be it at homes or organizations, and the growing use of data has created a perfect data storm that organizations have had to contend with over the past decade. Here’s a look at what database sharding means, its pros and cons, and the best practices to make it work. The concept is simplistic and enables scalability in distributed computing, but there are many factors to consider to derive the maximum benefit from it. If Database sharding sounds a bit complicated, it implies partitioning an on-prem server into multiple smaller servers, known as shards, each of which can carry different records. ![]()
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