Last month, I reported on a leaked announcement that Google had proved “quantum supremacy”, which is a concrete illustration of a use case where a quantum computer succeeds in a computation that a classical supercomputer cannot achieve in any reasonable timescale.
DISCLAIMER: This post in based on personal experiences and the situations explained here may not apply in other context.Figures displayed on the examples are just samples for demo purposes, not actual data.
In every company in the world, employees need access to information. Most companies purchase and install expensive software solutions or even spend years developing complex reporting systems on-site.
However, they all fall short satisfying user needs. They are either too complex, and non-technical people can’t understand how to use those tools, or they are too user-friendly and they lack the flexibility these users need.
Monitoring metrics is highly important to operate distributed systems in production. Alluxio collects metrics using the Codahale Metrics Library on I/O throughput, RPC throughput, and resource usage. Alluxio metrics are shown in its webUI but are also available through a REST endpoint or exportable to several third-party sinks in a time-series manner (see docs).
Grafana, a comprehensive metrics visualization software, ties into this process by pulling the metrics that systems like Alluxio collect through a sink and visualizes them in a more helpful fashion. This guide will cover how to set up Grafana and Graphite, a supported sink for Alluxio, which will put metrics in a time-series database, along with exploring some of the possibilities that the combination offers.
The past several years have seen increasing adoption for PostgreSQL. PostgreSQL is an amazing relational database. Feature-wise, it is up there with the best, if not the best. There are many things I love about it — PL/ PG SQL, smart defaults, replication (that actually works out of the box), and an active and vibrant open source community. However, beyond just the features, there are other important aspects of a database that need to be considered.
If you are planning to build a large 24/7 operation, the ability to easily operate the database once it is in production becomes a very important factor. In this aspect, PostgreSQL does not hold up very well. In this blog post, we will detail some of these operational challenges with PostgreSQL. There is nothing fundamentally unfixable here, just a question of prioritization. Hopefully, we can generate enough interest in the community to prioritize these features.
The impact of AI on human lives can be felt the most in the healthcare industry. AI-powered computer vision technology can help bring affordable healthcare to millions of people. Computer vision practices are already in place for sorting and finding images in blogs and retail websites. It also has applications in medicine.
You may be interested in: Computer Vision: Overview of a Cutting Edge AI Technology
Medical diagnosis depends on medical images such as CAT scans, MRI images, X-rays, sonograms, and other images.
As Josh Wills once said,
“Data Scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”
Math and Statistics for Data Science are essential because these disciples form the basic foundation of all the Machine Learning Algorithms. In fact, Mathematics is behind everything around us, from shapes, patterns, and colors, to the count of petals in a flower. Mathematics is embedded in each and every aspect of our lives.
You may also like: Service Discovery and Client-Side Load Balancing With Eureka and Ribbon
Almost a year ago Spring Cloud has announced that most of Spring Cloud Netflix OSS projects will be moved to the maintenance mode starting from Spring Cloud Greenwich Release Train. The maintenance mode only does not include Eureka, which still will be supported. I referred to that information in one of my previous articles The Future of Spring Cloud Microservices After Netflix Era.
Startups face extreme amounts of uncertainty and challenges when their web applications’ user-base multiplies. They need to be flexible in their choices, such as choosing the right framework, database, and architecture besides building the right team. What’s more, unlike enterprises, startups have only limited time to scale. Even worse, if they are fortunate enough, they might need to scale their capacity tenfold in a few months.
To understand the nuts and bolts of web scalability, we spoke to some experts and asked for their tips on improving web scalability. Here’s what they said…
REST is an API design architecture, which, in the last few years, has become the norm for implementing web services. It uses HTTP to get data and perform various operations (POST, GET, PUT, and DELETE) in JSON format, allowing better and faster parsing of data.
However, like all great technologies, REST API comes with some downsides. Here are some of the most common ones:
Within the scope of this article, the Batch Ab Initio graph will be triggered via the scheduler and the files in the destination folder will be retrieved and the data in it will be parsed according to certain criteria.
The parsed data will be written to the database.
