Jepsen tests and a chaos-driven testing framework play a crucial role in identifying and addressing vulnerabilities. However, traditional testing methods fall short for cloud-native applications and distributed systems. This blog provides the latest Jepsen updates for YugabyteDB, as well as sharing information on additional in-house built frameworks for resiliency and consistency testing that go far beyond Jepsen.
Database High Availability has traditionally been achieved through enhanced and automated disaster recovery solutions. Although these measures reduce the impact of failures, they still result in complete downtime for the application during the failover process. In this blog, we compare this approach with cloud-native solutions for high availability, focusing on resiliency built directly into the database.
The cloud revolution, initially promising for application scaling, falls short in database scalability, facing challenges with concurrent user management and processing. To truly harness cloud benefits and meet peak demands, organizations must consider horizontally scalable, cloud-native databases over traditional, monolithic SQL systems.
Discover how a Yugabyte telecom customer transformed their operations by transitioning to YugabyteDB, a modern, cloud-native, distributed SQL database. The shift to a highly scalable and resilient data layer allowed them to lead the telecom services sector, meeting evolving customer demands in the dynamic digital landscape.
In this excerpt from the recent white paper, Migrating From Monolithic to Cloud Native Operational Databases, we look at three of the challenges faced by companies using the current generation of NoSQL databases and why distributed SQL may be a better choice.
Temenos could not continue to rely on monolithic databases for manufacturing business operations. They needed a high-availability, scale-out transactional database, so they turned to YugabyteDB. They recently announced that the Temenos Banking Cloud is achieving 100,000 business transactions per second. Learn how that performance benchmark was achieved.
Recently, I came across a sample e-commerce application that demonstrates how to use Next.js, GraphQL engine, PostgreSQL, and a few other frameworks to build a modern web application. The application supports basic e-commerce capabilities such as product inventory and order management, recommendation system, and checkout function. This made me curious as to how much effort it would take to complete a retail application migration from an on-premise to cloud native solution. So I decided to try.
Java is the quintessential language runtime for enterprise applications built on monoliths, microservices, and modular architecture patterns. But when it comes to “Enterprise Java,” Spring is the de facto framework of choice.
The Spring ecosystem—with the simplicity of Spring Boot—has grown to provide integration touchpoints to a majority of the Java ecosystem. For starters, it offers a clean abstraction and “glue” code to build cohesive enterprise applications. However,
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The last decade has seen a rise in usage of distributed SQL databases for transactional, cloud native applications. Popular options include Amazon Aurora, Google Spanner, YugabyteDB, and CockroachDB. All of these databases are resilient to failures and achieve high availability. However, they vary dramatically in the set of relational database management system (RDBMS) features they support.
Google Spanner and CockroachDB have chosen to rewrite the SQL engine from scratch. As a result, they do not implement many of the advanced features supported by a traditional RDBMS.
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YugabyteDB just turned 5 years old, and I cannot help but reminisce about our journey in building this database over the last half-decade. I vividly remember the genesis of the YugabyteDB project, which started with Kannan, Mikhail, and myself meeting for lunch at a restaurant to discuss the future of cloud native databases. After iterating on the idea over a number of such discussions,
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