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Showing posts with label MySQL. Show all posts
Showing posts with label MySQL. Show all posts

Tuesday, March 20, 2018

Announcing general availability of Azure database services for MySQL and PostgreSQL

Today, I am excited to announce the general availability (GA) of @Azure Database for @MySQL and Azure Database for @PostgreSQL. The GA milestone means that, starting today, these services are bringing the community versions of MySQL and PostgreSQL with built-in high availability, a 99.99% availability SLA, elastic scaling for performance, and industry leading security and compliance to Azure. Since we started the preview of MySQL and PostgreSQL on Azure in May 2017, we have accomplished a lot, increasing compute scale up to 32 vCores, offering a new Memory Optimized tier, ability to scale storage on-line independent of compute without impact to application performance, allowing greater flexibility in backup storage options, and achieving industry compliance with ISO, SOC, and HIPAA. We will be compliant with the General Data Protection Regulation (GDPR) when enforcement begins on May 25, 2018. In addition, with availability in 22 regions worldwide, these services are truly global. The reach of the services remains a key focus for us, and we continue to work on providing availability across all 40+ Azure regions, which we expect to deliver in coming months. In the video below, my colleague Sunil Kamath shares some of the key benefits of adopting Azure database services for MySQL and PostgreSQL

https://azure.microsoft.com/en-us/blog/announcing-general-availability-of-azure-database-services-for-mysql-and-postgresql/

Thursday, July 20, 2017

The 7 Best Data Visualization Tools In 2017

#BigData and the ever-growing access we have to more information is the driving force behind artificial intelligence and the wave of technological change sweeping across all industries. But all the data in the world is useless – in fact it can become a liability – if you can’t understand it. Data visualization is about how to present your data, to the right people, at the right time, in order to enable them to gain insights most effectively.

Luckily visualization solutions are evolving as rapidly as the rest of the tech stack. Charts, videos, infographics and at the cutting edge even virtual reality and augmented reality (VR & AR) presentations offer increasingly engaging and intuitive channels of communication.

Here’s my run-down of some of the best, most popular or most innovative data visualization tools available today. These are all paid-for (although they all offer free trials or personal-use licences). Look out for another post soon on completely free and open source alternatives.

Tableau

Big Data and the ever-growing access we have to more information is the driving force behind artificial intelligence and the wave of technological change sweeping across all industries.

But all the data in the world is useless – in fact it can become a liability – if you can’t understand it. Data visualization is about how to present your data, to the right people, at the right time, in order to enable them to gain insights most effectively.

Shutterstock

Luckily visualization solutions are evolving as rapidly as the rest of the tech stack. Charts, videos, infographics and at the cutting edge even virtual reality and augmented reality (VR & AR) presentations offer increasingly engaging and intuitive channels of communication.

Here’s my run-down of some of the best, most popular or most innovative data visualization tools available today. These are all paid-for (although they all offer free trials or personal-use licences). Look out for another post soon on completely free and open source alternatives.

Tableau

Tableau is often regarded as the grand master of data visualization software and for good reason. Tableau has a very large customer base of 57,000+ accounts across many industries due to its simplicity of use and ability to produce interactive visualizations far beyond those provided by general BI solutions. It is particularly well suited to handling the huge and very fast-changing datasets which are used in #BigData operations, including artificial intelligence and machine learning applications, thanks to integration with a large number of advanced database solutions including #Hadoop, #Amazon #AWS, #MySQL, #SAP and #Teradata. Extensive research and testing has gone into enabling Tableau to create graphics and visualizations as efficiently as possible, and to make them easy for humans to understand.

great deal of flexibility. One feature that has helped make FusionCharts very popular is that rather than having to start each new visualization from scratch, users can pick from a range of “live” example templates, simply plugging in their own data sources as needed.

Highcharts

Like FusionCharts this also requires a licence for commercial use, although it can be used freely as a trial, non-commercial or for personal use. Its website claims that it is used by 72 of the world’s 100 largest companies and it is often chosen when a fast and flexible solution must be rolled out, with a minimum need for specialist data visualization training before it can be put to work. A key to its success has been its focus on cross-browser support, meaning anyone can view and run its interactive visualizations, which is not always true with newer platforms.

https://www.forbes.com/sites/bernardmarr/2017/07/20/the-7-best-data-visualization-tools-in-2017/?c=0&s=BigData#618ef1681365

Wednesday, August 24, 2016

How to Implement Docker Swarm with PostgreSQL and Web-Services on Nutanix ACS Shared Volumes

In my previous article we deployed an open-source PACS solution on #Nutanix using Acropolis Container Services (ACS) and created a persistent volume for the application and the database – a native MySQL. While #MySQL is the simplest solution as it is already backed into the application container, it has its limitations and is not scalable enough for enterprise deployments. This article focus on deploying the same application using a container with #PostgreSQL database hosted with a persist volume for database files; while having #Orthanc running on non-persistent containers in a #Docker swarm cluster. Docker swarm enable the application to scale-out and also manages a built-in load balancer across container instances in the cluster. The first step is to create a Host VM for PostgreSQL. That can also be the same VM where you host one of the Orthanc instances, but in my case I’m placing PostgreSQL in a dedicated Host VM. Since I’m using persistent volume with ACS for the database I can blow PostgreSQL off and easily recreate the service in a new Host VM.

http://www.dabcc.com/how-to-implement-docker-swarm-with-postgresql-and-web-services-on-nutanix-acs-shared-volumes/