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Spring Data REST Services Powered By Distributed SQL – A Hands-on Lab

Spring Data REST Services Powered By Distributed SQL – A Hands-on Lab

The Spring application development framework is arguably the most popular framework among Java developers. However, given its extensive breadth and depth, it can be difficult to learn for new users. As the name suggests, Spring Boot makes it easy to `boot up` with the Spring framework. It shortens development time by taking an opinionated view of the framework and the associated third-party libraries. Annotation configuration and default codes are two examples of such a view.

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Why Distributed SQL Beats Polyglot Persistence for Building Microservices?

Why Distributed SQL Beats Polyglot Persistence for Building Microservices?

Today’s microservices rely on data with different models and read/write access patterns. Polyglot persistence, first introduced in 2008, states that each such data model should be powered by an independent database that is purpose-built for that model. This post highlights the loss of agility that microservices development and operations suffer when adopting polyglot persistence. We review how distributed SQL serves as an alternative approach that doesn’t compromise this agility.

E-Commerce Example

Polyglot Persistence in Action at an E-Commerce App (Source: Martin Fowler)

Breaking down monolithic applications into smaller,

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Distributed SQL on Google Kubernetes Engine (GKE) with YugabyteDB’s Helm Chart

Distributed SQL on Google Kubernetes Engine (GKE) with YugabyteDB’s Helm Chart

The glory days of the heavy-weight hypervisor are slowly fading away, and in the last few years, containerization of applications and services is the new reality. With containerization, enterprises can prototype, deploy, and meet scale demands more quickly. To systematically and efficiently manage these large-scale deployments, enterprises have bet on technologies like Kubernetes (aka k8s), a powerful container orchestrator, to get the job done. Kubernetes was originally developed by Google, but it has been open sourced since 2014 and is today developed by a large community of contributors.

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New to Google Cloud Databases? 5 Areas of Confusion That You Better Be Aware of

New to Google Cloud Databases? 5 Areas of Confusion That You Better Be Aware of

After billions of dollars in capital expenditure and reference customers in every major vertical, Google Cloud Platform has finally emerged as a credible competitor to Amazon Web Services and Microsoft Azure when it comes to enterprise-ready cloud infrastructure. While Google Cloud’s compute and storage offerings are easier to understand, making sense of its various managed database offerings is not for the faint-hearted. This post introduces app developers to the major Google Cloud database services,

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How DynamoDB’s Pricing Works, Gets Expensive Quickly and the Best Alternatives

How DynamoDB’s Pricing Works, Gets Expensive Quickly and the Best Alternatives

DynamoDB is AWS’s NoSQL alternative to Cassandra, primarily marketed to mid-sized and large enterprises. It works best for those who require a flexible data model, reliable performance, and the automatic scaling of throughput capacity. In a nutshell, DynamoDB’s monthly cost is dictated by data storage, writes and reads. Let’s walk through a synopsis.

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Achieving Sub-ms Latencies on Large Datasets in Public Clouds

Achieving Sub-ms Latencies on Large Datasets in Public Clouds

One of our users was interested to learn more about YugabyteDB’s behavior for a random read workload where the data set does not fit in RAM and queries need to read data from disk (i.e. an uncached random read workload).

The intent was to verify if YugabyteDB was designed well to handle this case with the optimal number of IOs to the disk subsystem.

This post is a sneak peak into just one of the aspects of YugabyteDB’s innovative storage engine,

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Practical Tradeoffs in Google Cloud Spanner, Azure Cosmos DB and YugabyteDB

Practical Tradeoffs in Google Cloud Spanner, Azure Cosmos DB and YugabyteDB

Updated April 2019.

The famed CAP Theorem has been a source of much debate among distributed systems engineers. Those of us building distributed databases are often asked how we deal with it. In this post, we dive deeper into the consistency-availability tradeoff imposed by CAP which is only applicable during failure conditions. We also highlight the lesser-known-but-equally-important consistency-latency tradeoff imposed by the PACELC Theorem that extends CAP to normal operations.

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