YugabyteDB is purpose built for geo-distributed applications that require high availability, high performance and regulatory compliance. In this blog, we are going to “look under the hood,” to explore exactly how YugabyteDB distributes data across multiple clouds, regions and availability zones.
In this post, we comparethe high availability and transactions architecture of PostgreSQL and MongoDB to that of YugabyteDB.
In this post, we reviewed the building blocks of the YugabyteDB and mapped them to the corresponding concepts (if any) in PostgreSQL and MongoDB.
Observability is absolutely vital to operating business-critical data infrastructure at scale. This is why observability is not an afterthought in the YugabyteDB architecture. Every layer of YugabyteDB exposes metrics in the Prometheus format at the /prometheus-metrics endpoint. Given that Prometheus is essentially the de-facto standard for build-your-own infrastructure monitoring and alerting, you can get started with monitoring YugabyteDB in almost no time.
This post details how to install and configure both YugabyteDB and Prometheus on Docker so that you can run simple queries on the Prometheus UI.
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FoundationDB enjoys a unique spot in the transactional NoSQL space given its positioning as a basic key-value database that can be used to build new, more application-friendly databases. Given that many of the guarantees provided by its core engine (such as multi-shard ACID transactions and high fault tolerance) are similar to those provided by the YugabyteDB database, our users often ask us for a comparison. These users are essentially trying to understand whether they should build their app directly using one of the three YugabyteDB APIs or should they explore/build a new database layer on FoundationDB first.
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At last month’s KubeCon + CloudNativeCon in Seattle, the single biggest change from previous container-related conferences was the excitement among the end user companies around their adoption of Kubernetes and the associated cloud native infrastructure ecosystem. The CNCF End User Community page today lists 50+ enterprises and 21+ case studies including those from industry bellwethers such as Capital One, Netflix, Nordstrom and Pinterest. There is a common adoption pattern among all these case studies —
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After launching YugabyteDB in November 2017, Team YugaByte celebrated 2018 as its first full year in the market as a cloud native, transactional database company. Exhilarating is the one word that best summarizes our 2018 experience. From a product and engineering standpoint, we launched two major releases (and tens of minor releases) and saw users adopt each of the releases at an amazing pace. This story of exceeded expectations repeated itself at every other function in the company whether it be sales,
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The SQL vs. NoSQL database split emerged in 2006-2007, but NoSQL’s compromises led developers to continue using SQL/RDBMS for critical workloads. However, recent changes in the NoSQL world have seen the adoption of ACID transactions, which were previously absent, and this post aims to inform architects of these changes and why they are happening now.
This post aims to help application developers understand the choice of SQL vs. NoSQL in the context of the data modeling needs of an application.
MongoDB’s “schemaless” JSON data modeling was initially attractive to web app developers looking to escape the constraints of traditional relational databases, but issues with data durability and ACID transactions have been a consistent challenge. While the recent MongoDB 4.0 release includes multi-document transaction support, this post explores where the platform falls short for transactional, high performance apps.
