In this example, you will learn how to map one-to-many relationship using Hibernate Annotations. Consider the following relationship between Student and Phone entity.
According to the relationship, a student can have any number of phone numbers.
Innovation thrives on openness. While it’s common to think that innovation largely consists of revolutionary breakthroughs, in reality, it is much more common for it to be a slow and iterative process of gradual improvements and remixing of existing technologies in new and novel ways.
You may also like: How to Shift Your Internal Culture Towards Innovation
As such, being open with your own insights, and others doing likewise, is crucial to the innovation process.
Before beginning a feature comparison between TensorFlow, PyTorch, and Keras, let’s cover some soft, non-competitive differences between them.
Non-competitive facts:
Below, we present some differences between the 3 that should serve as an introduction to TensorFlow, PyTorch, and Keras. These differences aren’t written in the spirit of comparing one with the other but with a spirit of introducing the subject of our discussion in this article.
The healthcare industry has generated plenty of data. The new method of data collection, such as sensor-generated data, has helped this industry to find a spot in the top.
What if this data can be used to provide better healthcare services at lower costs and increase patient satisfaction? Yes, you heard it right. It’s actually possible by applying machine learning (ML) techniques in the healthcare industry.
Today, I’m pleased to announce a new way to work with the OmniSci platform: OmniSci.jl, a Julia client for OmniSci! This Apache Thrift-based client is the result of a passion project I started when I arrived at OmniSci in March 2018 to complement our other open-source libraries for accessing data: pymapd, mapd-connector, and JDBC.
Julia and OmniSci: Similar in Spirit and Outcomes
If you’re not familiar with the Julia programming language, the language is a dynamically-typed, just-in-time compiled language built on LLVM that can achieve or beat the performance of high-performance, compiled languages such as C/C++ and FORTRAN. With the performance of C++ and the convenience of writing Python, Julia quickly became my favorite programming language when I started using it around 2013.
The fourth industrial revolution, or Industry 4.0, dawned upon the enterprise sector faster than any of the prior industrial eras (steam, electricity, and computers). Yet, excess wastes haven’t escaped the balance sheets. The zest to predict industrial waste and keep it to a minimum is one of the promises that industry 4.0 — driven by predictive analytics — holds.
Despite the automation hangover (Industry 3.0), manufacturers are proactively attuning their processes into smarter ecosystems. According to the PWC Global Industry 4.0 Survey, industry 4.0 could potentially bring cost reduction up to 3.6% p.a., amounting to USD 421 billion globally.
The Complexity of API Discovery
I can’t get API discovery out of my mind. Partly because I am investing significant cycles in this area at work, but it is also something I have been thinking about for so long that it is difficult to move on. It remains one of the most complex, challenging, and un-addressed aspects of the way the web is working (or not working) online today. I feel pretty strongly that there hasn’t been an investment in the area of API discovery because most technology companies providing and consuming APIs prefer things to be un-discoverable, for a variety of conscious and unconscious reasons behind these belief systems.
What Does API Discovery Mean? Depends on Who You Are…
One of the reasons that API discovery does not evolve in any significant way is because there is not any real clarity on what API discovery is. Depending on who you are and what your role in the technology sector is, you’ll define API discovery in a variety of ways. There are a handful of key actors that contribute to the complexity of defining and optimizing in the area of API discovery.
Hey guys, I hope you all are doing well. I am back with another article on custom docker instances for databases. In my last post, we saw how we could have our custom docker instance for MySQL. Similarly, in this post, we will see how we can do the same with DynamoDB, so let’s get started.
Just like the scenario in the previous article, I was working on a project with DynamoDB as the database due to its many features like scalability, cloud storage, etc. And I wanted to test some things and did not want to mess with the cloud instance, so I thought to make an instance of my own, so what to do?
How NOT to be API-misled for connectivity!
It was a few years back when API-led connectivity was getting popular and our customers wanted to see it being brought into practice. Some of them also began to choose their hybrid integration products based on the product’s inherent support for the approach. After having implemented two large hybrid integration programs for two different industries (Banking and Manufacturing), I thought I should share my experience as do’s and don’ts for API-led connectivity and Hybrid Integration.
API-led connectivity
The approach was termed as the next step in the evolution of SOA, which is why its principles and the very fundamental concepts will remain timeless, at least in the context of software architecture.
