If you’re not facing some kind of problem, you don’t need a new tool. Full stop. Don’t look for solutions and then back into problems. If you’re not facing a problem that a new technology doesn’t solve significantly better than your existing technology, then your decision is over. If you’re considering using this technology because you’ve seen others using it, it might be useful to think about what problems they are facing, and ask yourself if you’re facing the same problems. It is often easy to reach for a technology because you see another company using it, the difficulty is in determining whether or not you’re facing the same challenges.
Hyperconvergence of hardware resources is expected to be a fundamental architecture for multiple mini data center i.e. edge nodes. Red Hat team came with an innovative hyperconvergence of OpenStack projects along with Ceph software-defined storage. A solution shows, it is possible to gain better control all edge nodes by reducing control planes and maintain the continuity and sustainability of 5G network along with the performance required by new age applications.
The bottom line is that technology advances have been moving very fast, while public policy has lagged behind. It is time for public policy to catch up with technology. If technology is driving the future, society should do the steering.
On March 19 2019 Norsk Hydro, one of the world’s largest aluminum producers revealed that ransomware had been used in an attack against them. The Norwegian firm was attacked on March 18 and production processes at a number of facilities in Norway, Qatar, Brazil and other countries were affected.
PostgreSQL can scale rather well vertically. The more resources (CPU, memory, disk) that you can make available to your PostgreSQL server, the better it can perform. However, while some parts of Postgres can automatically make use of the increased resources, other parts need configuration changes before improvements can be noticed.
More than 1.55 million room nights are reserved on the Booking.com platform every day. It’s a staggering amount of traffic, and not surprisingly, the Amsterdam-based travel e-commerce company has a lot of knowledge to share about handling metrics at scale.
When you think about how “traditional” enterprise-focused firms – think Microsoft, Oracle and IBM – sell software, it usually follows a well trodden formula: negotiations, long sales cycles and a “checklist” of features demanded by decision makers who seldom use the products they buy.
Sharding, also known as horizontal partitioning, is a popular scale-out approach for relational databases. Amazon Relational Database Service (Amazon RDS) is a managed relational database service that provides great features to make sharding easy to use in the cloud. In this post, I describe how to use Amazon RDS to implement a sharded database architecture to achieve high scalability, high availability, and fault tolerance for data storage. I discuss considerations for schema design and monitoring metrics when deploying Amazon RDS as a database shard. I also outline the challenges for resharding and highlight the push-button scale-up and scale-out solutions in Amazon RDS.