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Deploying a Real-Time Location App with Hasura GraphQL Engine and Distributed SQL

Deploying a Real-Time Location App with Hasura GraphQL Engine and Distributed SQL

Hasura is one of the leading vendors in the GraphQL ecosystem. They offer an open source engine that connects to your databases and microservices, and then auto-generates a production-ready GraphQL backend. GraphQL is a query language (more specifically a specification) for your API, and a server-side runtime for executing queries by using a type system you define for your data. GraphQL is often used for microservices, mobile apps, and as an alternative to REST.

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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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Distributed SQL Change Management with Liquibase and YugabyteDB on GKE

Distributed SQL Change Management with Liquibase and YugabyteDB on GKE

Liquibase is an open source and extensible change management project that supports a variety of databases including Snowflake, MySQL, and PostgreSQL via JDBC. Liquibase allows users to easily define changes in SQL, XML, JSON, and YAML. These changes are then managed in a version control system so the changes can be documented, ordered, and standardized. For more information on the features and benefits of Liquibase, check out their documentation site.

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Getting Started with SQLPad and Distributed SQL on Google Kubernetes Engine

Getting Started with SQLPad and Distributed SQL on Google Kubernetes Engine

SQLPad is an MIT licensed web app written in React and Node.js for writing and running SQL queries and visualizing the results. SQLPad supports PostgreSQL, MySQL, SQL Server, Crate, Vertica, Presto, SAP HANA, Cassandra, Snowflake, Google BigQuery, SQLite, and many more via ODBC. Because YugabyteDB is PostgreSQL compatible, most third-party tools and apps will work “out of the box.” SQLPad is no exception here.

In this blog post we’ll show you how to:

  • Install a 3 node YugabyteDB cluster on Google Kubernetes Engine
  • Build the sample Northwind database
  • Build and configure SQLPad
  • Start the required SQLPad processes
  • Launch the SQLPad UI and issue a test query to validate the deployment

New to distributed SQL or YugabyteDB?

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Highly Available Prometheus Metrics for Distributed SQL with Thanos on GKE

Highly Available Prometheus Metrics for Distributed SQL with Thanos on GKE

In the last few years, Prometheus has gained huge popularity as a tool for monitoring distributed systems. It has a simple yet powerful data model and query language, however, it can often pose a bit of a challenge when it comes to high availability as well as for historical metric data storage. Adding more Prometheus replicas can be used to improve availability, but otherwise, Prometheus does not offer continuous availability. For example, if one of the Prometheus replicas crashes,

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Part 1: Deploying a Distributed SQL Backend for Apache Airflow on Google Cloud

Part 1: Deploying a Distributed SQL Backend for Apache Airflow on Google Cloud

Apache Airflow is a popular platform for programmatically authoring, scheduling, and monitoring workflows. Airflow has been deployed by companies like Adobe, Airbnb, Etsy, Instacart, and Square. The advantage of defining workflows as code is that they become more maintainable, versionable, testable, and collaborative. Airflow is used to author these workflows as directed acyclic graphs (DAGs) of tasks. Airflow’s scheduler executes your tasks on an array of workers while following the specified dependencies.

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Getting Started with YugabyteDB on Azure Kubernetes Service

Getting Started with YugabyteDB on Azure Kubernetes Service

Microsoft’s Azure Kubernetes Service (AKS) offers a highly available, secure, and fully managed Kubernetes service for developers looking to host their applications on containers in the cloud. AKS features elastic provisioning, an integrated developer experience for rapid application development, enterprise security features, and the most available regions of any cloud provider.

YugabyteDB is a natural fit for AKS because it was designed to support cloud native environments since its initial design.

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Spanning the Globe without Google Spanner

Spanning the Globe without Google Spanner

Open Source Geo-Distributed Relational Database on Multi-Cluster Kubernetes

Google Spanner, conceived in 2007 for internal use in Google AdWords, has been rightly considered a marvel of modern software engineering. This is because it is the world’s first horizontally-scalable relational database that can be stretched not only across multiple nodes in a single data center but also across multiple geo-distributed data centers, without compromising ACID transactional guarantees. In 2012,

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Bringing Distributed SQL to VMware Tanzu

Bringing Distributed SQL to VMware Tanzu

VMware Tanzu, the newest offering from VMware’s stable of proven enterprise products, brings together a portfolio of open source projects for modernizing applications and automating infrastructure management. VMware Tanzu provides a managed Kubernetes environment on VMware vSphere or any public cloud of choice that allows a consistent way to provision and deploy the code for application developers.

The Yugabyte team collaborated with VMware to certify YugabyteDB for Tanzu Kubernetes environments,

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Automating YugabyteDB Deployments with Google Cloud Deployment Manager

Automating YugabyteDB Deployments with Google Cloud Deployment Manager

This is the second post in the Getting Started with YugabyteDB on Public Cloud series. In our first post, we covered Automating YugabyteDB Deployments with AWS CloudFormation templates. In this post we will show you how to achieve the same with Cloud Deployment Manager templates when using Google Cloud.

For redundancy across multiple fault domains inside a single region, Google Cloud uses the concept of zones,

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