Despite advancements in public clouds, region and zone outages still occur once or twice a year. This blog offers best practices to improve performance and reduce latencies in a multi-region deployment.
This blog post explores the most popular multi-region database deployment options by designing a data layer for a Slack-like corporate messenger.
Back in 2016 I started at Nutanix, fresh after finishing my graduate studies. This was barely a couple of weeks after two of Nutanix’s employees, Karthik and Kannan, left to start Yugabyte with Mikhail Bautin. They were still the subject of a few watercooler chats at Nutanix, mainly because of their bold decision to enter the database market, one that is definitely not known for being easy.
It became apparent to me why databases are hard.
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We are excited to announce the availability of row-level geo-partitioning in YugabyteDB, a feature heavily requested by our user community and enterprise customers alike. This feature allows fine-grained control over pinning data in a user table (at a per-row level) to geographic locations, thereby allowing the data residency to be managed at the database level.
Making the nodes of a multi-region database cluster aware of the location characteristics of the data they store allows conforming to regulatory compliance requirements such as GDPR by keeping the appropriate subset of data local to different regions,
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Our mission at Yugabyte is to build the default distributed SQL database for cloud native applications in a multi cloud world. To further that mission, this release brings major enhancements to multi-region deployments, performance, and security features offered by the database – while simultaneously improving on high availability, horizontal scalability, and ease of managing the database.
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Enterprises of all sizes are embracing rapid modernization of user-facing applications as part of their broader digital transformation strategy. The relational database (RDBMS) infrastructure that such applications rely on suddenly needs to support much larger data sizes and transaction volumes. However, a monolithic RDBMS tends to quickly get overloaded in such scenarios. One of the most common architectural patterns used to scale an RDBMS is to “shard” the data. In this blog, we will learn what data sharding is and how it can be used to scale a SQL database.
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