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Category: Spring

Change Data Capture (CDC) Using a Spring Data Processing Pipeline

Change Data Capture (CDC) Using a Spring Data Processing Pipeline

Change Data Capture (CDC) is a technique to capture changes in a source database system in real-time. The goal is to stream those changes as events through a data processing pipeline for further processing.

CDC enables many use cases, especially in modern microservices-based architecture that involves a lot of bounded services. It is the de-facto choice for use cases such as search indexes, in-memory data cache, real-time notifications, data sync between sources,

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Connecting a Spring Boot Application to YugabyteDB Managed and Deploying It to Kubernetes on minikube

Connecting a Spring Boot Application to YugabyteDB Managed and Deploying It to Kubernetes on minikube

Spring Boot is one of the most popular frameworks for building cloud native applications. It makes configuring an application easy and offers tons of starters to get you off the ground quickly. Each Spring Boot application is stand-alone and self-contained, which makes them easy to deploy in a distributed fashion – to containers or, even better, on Kubernetes.

YugabyteDB Managed (formerly Yugabyte Cloud) is Yugabyte’s DB-as-a-service and is a perfect match for Spring Boot applications,

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Distributed SQL Summit Recap: Cloud Native Spring for Relational Databases

Distributed SQL Summit Recap: Cloud Native Spring for Relational Databases

Editor’s note: Below is the final recap from last year’s event. There’s still time to join us live for the upcoming Distributed SQL Summit, Jan 20-22 in India Standard Time.

At the Distributed SQL Summit 2020, DaShaun Carter, formerly a Tanzu Solution Engineer at VMware presented the talk, “Cloud Native Spring for Relational Databases”.

In the talk, DaShaun takes us down about a possible path to distributed SQL,

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Building Serverless Applications Using Spring Boot, AWS Lambda, and YugabyteDB

Building Serverless Applications Using Spring Boot, AWS Lambda, and YugabyteDB

Introduction to Serverless Applications

Serverless applications allow developers to run code without having to provision or manage any servers; developers can just concentrate on implementing the business logic of their applications.

As workloads move to the cloud, serverless applications are gaining tremendous popularity with developers. Serverless frameworks allow developers to program for the cloud to take advantage of elastic scaling for workloads and provide cost benefits of using pay-for-use features,

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Run the REST Version of Spring PetClinic with Angular and Distributed SQL on GKE

Run the REST Version of Spring PetClinic with Angular and Distributed SQL on GKE

Java developers know that Spring Data makes it easy to use data access technologies, relational and non-relational databases, map-reduce frameworks, and cloud-based data services. When YugabyteDB is combined with Spring, Java developers are able to leverage their familiarity with PostgreSQL while gaining the added benefits of distributed SQL. These “out-of-the-box” benefits include geo-data distribution, high performance, and horizontal scalability, which are impossible or difficult to achieve with monolithic SQL databases.

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INSERT INTO Yugabyte (We’re Hiring Developer Advocates)

INSERT INTO Yugabyte (We’re Hiring Developer Advocates)

It has been an exciting last few weeks here at Yugabyte! We closed our Series B funding round, Bill Cook (formerly of Pivotal and Greenplum) joined as our new CEO, we reaffirmed our commitment to open source, and achieved a bunch of cool community adoption milestones. We are hiring for several roles across engineering, customer success, and marketing. Particularly, with a new round of funding and exploding interest in Distributed SQL,

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Scaling Relational Spring Microservices Without Load Balancers

Scaling Relational Spring Microservices Without Load Balancers

This article was originally posted on JAXenter.com.

Modern cloud native applications demand relational databases to be highly available while being able to scale to millions of requests (RPS) and thousands of transactions per second (TPS) on demand. This is becoming essential to meet the seamless experience demanded by business applications and their users. High availability and scalability in NoSQL databases like Apache Cassandra and MongoDB are well understood, but have been challenging problems to solve in relational databases.

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Developing Reactive Microservices with Spring Data and Distributed SQL

Developing Reactive Microservices with Spring Data and Distributed SQL

In 2016 in the keynote presentation of Spring One Platform, Juergen Hoeller announced Spring WebFlux, one of the most highly anticipated projects being worked on by the Spring Team due to the performance gains that reactive streams promised for web controllers. Subsequently, with Spring Framework 5.0, Spring Reactive MVC went GA along with the release of WebFlux API, making the reactive stream based web controller mainstream.

Fast-forward to 2020, Spring WebFlux MVC has gained wide adoption in cloud native applications,

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Best Practices for Deploying   Confluent Kafka, Spring Boot & Distributed SQL Based Streaming Apps on Kubernetes

Best Practices for Deploying Confluent Kafka, Spring Boot & Distributed SQL Based Streaming Apps on Kubernetes

In our previous post “Develop IoT Apps with Confluent Kafka, KSQL, Spring Boot & Distributed SQL”, we highlighted how Confluent Kafka, KSQL, Spring Boot and YugabyteDB can be integrated to develop an application responsible for managing Internet-of-Things (IoT) sensor data. In this post, we will review the challenges and best practices associated with deploying such a stateful streaming application on Kubernetes.

Challenges of Streaming Apps

Streaming apps are inherently stateful in nature given the large volume of data managed and that too continuously.

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