Today’s most pressing data challenges center around connections, not just tabulating discrete data. Graph analytics accelerate breakthroughs across industries with more intelligent solutions.
This article series is designed to help you better leverage graph analytics so you can effectively innovate and develop intelligent solutions faster.
This article is a comprehensive guide, from start to finish, on how to deploy a Java library to Maven Central so everyone can use it by including the dependency in their project(s).
It goes without saying that there has to be a Java library for it to be uploaded. Hence, the first thing is creating a Java library that’s unique, of quality code standard and will be beneficial to the developer community. Of course, you’ve got that already — that’s why you’re reading this after all (or maybe you’re planning on creating one).
It’s been about a month since my last research post, and I’ve been musing about the next topic. What should it be? Well, I’ve decided. Since I love nothing more than throwing the gates wide for everyone’s internet anger, I thought I’d weigh in on the subject of self-documenting code vs. comments.
I’ll be awaiting your rage below, in the comments.
At Devada, we’ve sent literally millions of emails to developers. Getting their attention can be challenging, as they tend to be skeptical of traditional marketing tactics. Email, however, is one of their preferred communication channels. If you can make your messages stand out in their inboxes, you’ll be on your way to reaching your target open rates and CTRs.
Most marketers can’t resist a witty subject line. Devs, however, aren’t always the biggest fans. It’s not that they don’t have a sense of humor – they’re just wary of clickbait and clever advertising ploys.
So, I’ve been looking a little deeper into what I have running on my network over the past day or so, and I found a few interesting things. I covered the most interesting things I found in my last piece, as well as some initial scans of the equipment I found. I had a couple of interesting addresses on my NAT LAN, at .128, .131, and .147.
I have no idea what’s running at .128. Whatever it is, it has absolutely no ports open, and believe me, I’ve looked over them all (nmap … -p-65535 is your friend). I ran all the usual suspects from the script library too, and it doesn’t register any vulnerabilities nor has it been hooked by any known malware. I’m going to need to figure out another way to get to whatever that is.
So your JUnit 5 tests are not running under Maven? Do you have JUnit 5 tests that run fine from your IDE but fail to run under Maven?
Your test output looks like this:
I have worked for 14 years in service and consulting companies, for various and varied clients.
I’ve had to work for nearly twenty clients, most of the time for large companies well known to everyone. I have been a developer, project manager, architect, and I have been able to work in contact with operationals, ops and some of the managers of these companies. It is following various recent exchanges that I wanted to share my personal observation, on some fundamental differences between startups and a number of large companies.
As an application architect, eventually, you’d choose the database or database service to power your newest application or a microservice. Selecting one of the databases among relational databases was easier. The use cases were roughly divided into OLTP and OLAP (decision support). The workload differences between OLTP and OLAP were well known. OLTP workloads consist of short transactions on few random rows, expecting millisecond responses on pre-compiled queries; OLAP workloads consist of data loads, long-running queries scanning millions of rows of a fact table of a star/snowflake schema. Each had the performance benchmark and TCO well defined, measured and audited via TPC benchmarks. You can make use of these numbers, approximate your workload, understand the needs and capabilities match on other fronts like administration.
Then, there are NoSQL databases. NoSQL databases were invented to handle the webscale performance of operational applications. It had to be elastic to handle the scale and tolerate nodes going down (aka partition tolerance). That sparked the innovation to create databases on a variety of data models and use cases. There are databases for JSON, graphs, time-series and more. From Azure databases to ZODB, from Couchbase to Cassandra. MongoDB to TiDB, spatial to JSON databases — so many different kinds of databases. In fact, NoSQL-databases.org lists 225 databases as of November 2018.
As a project manager, credible sources of information hold significant value in terms of knowledge acquisition. Aside from all the books and certifications, social media platforms are equally beneficial. One such platform to gain fantastic insights is Twitter. We’ve hunted down some of the top project managers that you need to follow on Twitter.
We’re all well aware of Twitter as a popular medium of gaining and sharing information. As we’re speaking, there are around 335 million active users utilizing the platform for exchanging information according to their interests.
This is a primer on time-series data, and why you may not want to use a “normal” database to store it.
Here’s a riddle: what do self-driving Teslas, autonomous Wall Street trading algorithms, smart homes, transportation networks that fulfill lightning-fast same-day deliveries, and an open-data-publishing NYPD have in common?
