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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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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.

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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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Basic CRUD Operations Using Hasura GraphQL with Distributed SQL on GKE

Editor’s note: This post was updated July 20, 2020 with new Helm and YugabyteDB versions

GraphQL is an MIT-licensed project originally developed at Facebook in 2012 and open-sourced a few years later. Two popular GraphQL projects, Hasura and Apollo, have reported download numbers of 29 and 33 million, respectively. Why? Think of GraphQL as a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API,

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Getting Started with pgbench and Distributed SQL on GKE

pgbench is a simple program for running benchmark tests on PostgreSQL. It runs the same sequence of SQL commands over and over, possibly in multiple concurrent database sessions, and then calculates the average transaction rate (transactions per second). By default, pgbench tests a scenario that is loosely based on TPC-B, involving five SELECT, UPDATE, and INSERT commands per transaction. However, it is easy to test other cases by writing your own transaction script files.

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Getting Started with Falco Runtime Security and Cloud Native Distributed SQL on Google Kubernetes Engine

Falco is an incubating CNCF project that provides cloud native, open source runtime security for applications running in Kubernetes environments. Falco monitors process behaviors to detect anomalous activity and help administrators gain deeper insights into process execution.  Behind the scenes, Falco leverages the Linux-native extended Berkeley Packet Filter (eBPF) technology to analyze network traffic and audits a system at the most fundamental level, the Linux kernel. Falco then enriches this data with other input streams,

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Cloud Native Meets Distributed SQL: Bringing Microservices, Kubernetes, Istio & YugabyteDB Together with Hipster Shop Demo

Polyglot persistence is the widely accepted database implementation strategy when it comes to decomposing monoliths into microservices. In practice, this requires every microservice to model its data needs independently using a database that is purpose-built for that particular model, and thereafter store the data in an independent database instance. While independent database instances as a deployment paradigm makes sense from an decoupled microservices architecture standpoint, choosing multiple different databases each with a specialized data model is usually justified in the context of performance,

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Getting Started with Distributed SQL on Yugabyte Cloud

With the release of YugabyteDB 2.1 earlier this week, we introduced many exciting new features: performance optimizations, 2DC and read replica support, and more. We also announced that Yugabyte Cloud is now officially in Beta! In this blog post we’ll show you how to quickly get up and running with a YugabyteDB cluster on Yugabyte Cloud, build a sample database, and connect to it with JetBrain’s DataGrip database administration tool.

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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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YugaByte Company and Database Update – Aug 3, 2018

$16 Million Funding Round

In case you missed the news earlier this Summer, YugaByte raised an additional $16M of funding from Dell Technologies Capital and our previous investor Lightspeed Venture Partners. With the additional funding, we are accelerating investments in engineering, sales, and customer success to scale our support for enterprises building business-critical applications in the cloud. So, as you’d expect…

We are Hiring!

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