- Awesome Awesomeness — list of curated collections of frameworks and libraries in various languages that do not suck. They solve the problem of “so, I’m new to (language) and don’t want to kiss a lot of frogs before I find the right tool for a particular task”.
- Breach — a hackable, modular web browser.
- The CompuServe of Things (Phil Windley) — How we build the Internet of Things has far-reaching consequences for the humans who will use—or be used by—it. Will we push forward, connecting things using forests of silos that are reminiscent the online services of the 1980′s, or will we learn the lessons of the Internet and build a true Internet of Things? (via Cory Doctorow)
Curated Code, Hackable Browser, IoT Should Be Open, and Better Treemaps
Business users are becoming more comfortable with graph analytics.
The rise of sensors and connected devices will lead to applications that draw from network/graph data management and analytics. As the number of devices surpasses the number of people — Cisco estimates 50 billion connected devices by 2020 — one can imagine applications that depend on data stored in graphs with many more nodes and edges than the ones currently maintained by social media companies.
This means that researchers and companies will need to produce real-time tools and techniques that scale to much larger graphs (measured in terms of nodes & edges). I previously listed tools for tapping into graph data, and I continue to track improvements in accessibility, scalability, and performance. For example, at the just-concluded Spark Summit, it was apparent that GraphX remains a high-priority project within the Spark1 ecosystem.
The Lambda Architecture has its merits, but alternatives are worth exploring.
Nathan Marz wrote a popular blog post describing an idea he called the Lambda Architecture (“How to beat the CAP theorem“). The Lambda Architecture is an approach to building stream processing applications on top of MapReduce and Storm or similar systems. This has proven to be a surprisingly popular idea, with a dedicated website and an upcoming book. Since I’ve been involved in building out the real-time data processing infrastructure at LinkedIn using Kafka and Samza, I often get asked about the Lambda Architecture. I thought I would describe my thoughts and experiences.
What is a Lambda Architecture and how do I become one?
The Lambda Architecture looks something like this:
If all companies are software companies, then all companies must learn to manage their online operations.
Two years ago, I wrote What is DevOps. Although that article was good for its time, our understanding of organizational behavior, and its relationship to the operation of complex systems, has grown.
A few themes have become apparent in the two years since that last article. They were latent in that article, I think, but now we’re in a position to call them out explicitly. It’s always easy to think of DevOps (or of any software industry paradigm) in terms of the tools you use; in particular, it’s very easy to think that if you use Chef or Puppet for automated configuration, Jenkins for continuous integration, and some cloud provider for on-demand server power, that you’re doing DevOps. But DevOps isn’t about tools; it’s about culture, and it extends far beyond the cubicles of developers and operators. As Jeff Sussna says in Empathy: The Essence of DevOps:
…it’s not about making developers and sysadmins report to the same VP. It’s not about automating all your configuration procedures. It’s not about tipping up a Jenkins server, or running your applications in the cloud, or releasing your code on Github. It’s not even about letting your developers deploy their code to a PaaS. The true essence of DevOps is empathy.
Think your IT staff can protect you better than major cloud providers? Think again.
I just ran across Katie Fehrenbacher’s article in GigaOm that made a point I’ve been arguing (perhaps not strongly enough) for years. When you start talking to people about “the cloud,” you frequently run into a knee-jerk reaction: “Of course, the cloud isn’t secure.”
I have no idea what IT professionals who say stuff like this mean. Are they thinking about the stuff they post on Facebook? Or are they thinking about the data they’ve stored on Amazon? For me, the bottom line is: would I rather trust Amazon’s security staff, or would I rather trust some guy with some security cert that I’ve never heard of, but whom the HR department says is “qualified”? Read more…
Why my understanding of AI is different from yours.
Let me start with a secret: I feel self-conscious when I use the terms “AI” and “artificial intelligence.” Sometimes, I’m downright embarrassed by them.
Before I get into why, though, answer this question: what pops into your head when you hear the phrase artificial intelligence?
For the layperson, AI might still conjure HAL’s unblinking red eye, and all the misfortune that ensued when he became so tragically confused. Others jump to the replicants of Blade Runner or more recent movie robots. Those who have been around the field for some time, though, might instead remember the “old days” of AI — whether with nostalgia or a shudder — when intelligence was thought to primarily involve logical reasoning, and truly intelligent machines seemed just a summer’s work away. And for those steeped in today’s big-data-obsessed tech industry, “AI” can seem like nothing more than a high-falutin’ synonym for the machine-learning and predictive-analytics algorithms that are already hard at work optimizing and personalizing the ads we see and the offers we get — it’s the term that gets trotted out when we want to put a high sheen on things. Read more…