Tips, Expertise, Articles and Advice from the Pro's for Your Website or Blog to Succeed
Microsoft Azure has long proven it’s a force to be reckoned with in the world of cloud computing. Over the past year, Azure has made some significant steps in bridging the gap with AWS by offering new services and capabilities as well as competitive pricing.
A growing number of our users are Azure fans, and so we’re happy to introduce a new Logz.io integration for Azure as well as premade dashboards for monitoring different Azure resources!
Almost three-quarters of businesses will run nearly their entire operations on the cloud by 2020. Organizations are flocking en masse to cloud computing, eager to capitalize on the speed, scale, and flexibility a cloud-based infrastructure can provide. But as cloud computing grows in popularity and transforms how companies collect, use, and share data, it also becomes a more attractive target for would-be attackers and hackers.
Cloud providers have invested time and resources into bolstering cloud security and boosting customer confidence. Solutions that were once believed to be fraught with risk have been strengthened through containerization, encryption, advanced failover, and automated threat detection capabilities.
DevOps has hit the C-level and it's permeating beyond approval for pilot programs. Enterprise executives are now talking about DevOps in terms of scale and ROI. With the key to accelerating large scale DevOps transformations often hinging on flexibility, the cloud has become the de facto solution for businesses to host their apps. But while the cloud offers the framework to reduce costs and consolidate workloads, there are a ton of roadblocks when it comes to migrating massive, diverse application portfolios.
During a recent webinar, Jay Yeras, Partner Solutions Architect at AWS, discussed the various drivers for migrating apps to the cloud, the barriers enterprises might face, and how they can overcome them.
Now that we have shown that it is easy to do standard NLP, next up is Deep Learning. As you can see, NLP, Machine Learning, Deep Learning, and more are all in your reach for building your own AI as a Service using tools from Cloudera. These can run in public or private clouds at scale. Now you can run and integrate machine learning services, computer vision APIs, and anything you have created in-house with your own Data Scientists. The YOLO pre-trained model will download the image to /tmp from the URL to process it. The Python 3 script will also download the GLUONCV model for YOLO3.
Using Pre-trained Model:
ARM is a Microsoft Azure provided managed service that enables an automation designer to define their design intent, expressed as templates, using ARM's automation language, which is currently AzureRM and is transitioning to Az.
ARM enables a designer to express their intent as to the selection, configuration, and assembly sequence of computing resource objects as specified by ARM automation resource templates.
To understand the current and future state of containers, we gathered insights from 33 IT executives who are actively using containers. We asked, "What are the most important elements to orchestrating and deploying containers?"
Here's what they told us:
Updated September 2019
In the last few years, Kubernetes has laid waste to its fellow competitors in the battlefield of container orchestration. Sadly, Docker Swarm hasn’t been a major contender since 2016 and, like AWS, admitted defeat by pledging K8s support and integration. Since Kubernetes has skyrocketed to popularity as the container solution of choice, here’s a comprehensive list of all the tools that complement K8s to further enhance your development work.
We look at a modern approach to securely moving frontend code to the cloud using a serverless approach, walking step-by-step through two examples.
Frontend code is inherently insecure. Yes, you can mangle your code with something like UglifyJS, or use more a more advanced obfuscation tool like Jscrambler, but at the end of the day, the public nature of frontend code means it's accessible to nefarious users.
I was playing with AWS Lambda recently and found it pretty exciting. It's also cheap as serverless applications don’t require provisioning, maintaining, and administering servers, and AWS, in particular, is simple, easy-to-learn, and powerful. AWS Lambda supports Java, Go, PowerShell, Node.js, C#, Python, and Ruby languages. The biggest problem with using Lambda is that it does not allow inline editing for Java. In Java, you need to write the code in an editor and build a .jar or .zip deploy on the console.
Elassandra simplifies your data stack by combining the power of Elasticsearch and Apache Cassandra into a single unique solution.
Elasticsearch is by design a sharded master-slave architecture. The Elasticsearch master node manages the mapping changes and only the primary shards take write operations. The replica shards are read-only and can be promoted to primary shards by the master node in case of failure or maintenance. By relying on Apache Cassandra, Elassandra is master-less and has no single point of write. All nodes can process the search requests, request a mapping update, and depending on the Cassandra replication factor, take write operations.
This article is the fourth in a series on serverless computing. I recommend starting at the beginning of the series, as I introduce concepts incrementally. The links to previous articles on DZone are included here:
In the previous articles, I demonstrated the benefits and challenges of serverless integration for business applications and illustrated them with an example based on the Bonita platform. As you know by now if you have been reading along, the main benefit of serverless is to abstract infrastructure with ephemeral containers using a FaaS service on the cloud.
One of my favorite features that was announced during re:Invent 2018 was DynamoDB On-Demand. With DynamoDB On-Demand, we can use DynamoDB without provisioning capacity. Instead, we pay per request. Sounds amazing, huh? I was excited and re-configured all DynamoDB tables of our SaaS product marbot: cloud-native alerting for CloudWatch via Slack. The result is stunning but misleading.
I shared my excitement on Twitter, and today, I add what we learned in the following weeks.
Great speaking with Brend Greifeneder, CTO at Dynatrace, during Perform 2019 where he announced the next generation of their Artificial Intelligence engine, Davis, which is now powered by new and enhanced algorithms and an ability to ingest data and events from a third-party.
“Four years ago, we pioneered, and continually improve, a unique, deterministic approach to AI that enabled customers to simplify enterprise cloud environments and focus more time on innovation. Because Dynatrace auto-discovers and maps dependencies across the enterprise cloud and analyzes all transactions, our Davis AI engine can truly causate, and drive to the precise root cause of issues versus simple guesses based on the correlation. This concept just got even better through semantically enriching external data and mapping it to our real-time topological models. In addition, unlike other solutions, it doesn't require learning periods, making it effective for highly dynamic clouds,” explained Bernd.
Recently, I had the opportunity to chat with Alan Shimmel from DevOps.com about some of the challenges and tips for cloud-ifying legacy applications and on-prem infrastructure.
For many modern companies, growth often means growing through acquisition. As organizations become larger, it often makes sense to acquire existing companies who are already creating the solutions they need than try to build them from scratch. However, this can often create a variety of conflicts and challenges—one of which is around infrastructure. For other companies that have grown more organically, it often can be difficult to modernize their legacy systems and bring their technology stack into the 21st century.
Gaining visibility into modern IT environments is a challenge that an increasing number of organizations are finding difficult to overcome.
Yes — the advent of cloud computing and virtually “unlimited” storage has made it much easier to solve some of the traditional challenges involved in gaining visibility. However, architecture has evolved into microservices, containers, and scheduling infrastructure. Software stacks and the hardware supporting them are becoming more complex, creating additional and different challenges. These changes have directly impacted the complexity of logging and it takes a very specific set of tools and strategies to be able to solve this visibility challenge.
With the forecast of over IoT devices being deployed globally by 2020, the amount of data stored in the cloud is hard to imagine. Not to mention the processing power needed to derive any tangible value from it.
No wonder business owners are increasingly looking to improve the performance and reduce operational costs of their IoT products. One of the ways to do so is by handling the data outside of the main cloud or at its "edge."
In my previous article from this series, we looked into the details that determine how your integration becomes the key to transforming your customer experience.
It started with laying out the process of how I've approached the use case by researching successful customer portfolio solutions as the basis for a generic architectural blueprint. Now it's time to cover various blueprint details.
The bar for IT support is already high. New-gen startups have made exceptional digital customer experience their central selling point, challenging more traditional companies to keep up the stakes.
However, delivering that support also requires significant capital investments – both financial and human. The wrinkle? Companies now spend billions in recruitment to find the right talent, only to have to replace that talent in a year or so. According to a recent LinkedIn report, the tech sector has the highest turnover rate in every industry sector – 13.2%. Yes, even higher than retail. Even at that giant Amazon, the average tenure of an IT employee is one year.