Thoughts are in it’s way…
MuleSoft (just got listed @ Nasdaq today) founder’s fairy tale
Do believe in Miracles!
From being a frustrated IT guy to an Entrepreneur who went on to have found ‘MuleSoft’ – Cloud Integration via API’s enabler and a well respected player in the market!
Courtesy: MuleSoft, BI, FinYahoo
Google responded to “AWS Reserved Instances” sharply with it’s proposed offering, “Committed Use Discounts”
This covers G-CUD Vs. AWS RI,
Courtesy: Tino Tereshko (source URL above)
Google Cloud Next’17 Conference – Recap with a few snippets!
- Google is trying to stay appeal to Enterprises (incl Legacy ones!)
- Google tried to speak the language Enterprises love to hear – MultiCloud, Partnering with ERP giants like SAP
- Show casing the lineup of Enterprise customers and case studies (HSBC, Colgate, Schlumberger, Disney, The Home Depot, et al..)
- Customers respect the fact, Google got the true global presence with their inter-regions connectivity with well enhanced security is a stand out factor
The centrifugal force for all the Enterprises and shadow customers, Google has got a muscle to push and deliver Machine Learning~AI and Big Data capabilities via Cloud
AI/ML could well be leveraged in Microservices platform where the building block components/services cause a ‘racing condition’ for resources or to predict and suffice a resource management.
The ability to deploy machine-learning applications as containers and to cluster those containers has several advantages, including:
- The ability to make machine learning applications self-contained. They can be mixed and matched on any number of platforms, with virtually no porting or testing required. Because they exist in containers, they can operate in a highly distributed environment, and you can place those containers close to the data the applications are analyzing.
- The ability to expose the services of machine learning systems that exist inside of containers as services or microservices. This allows external applications, container-based or not, to leverage those services at any time, without having to move the code inside the application.
- The ability to cluster and schedule container processing to allow the machine learning application that exists in containers to scale. You can place those applications on cloud-based systems that are more efficient, but it’s best to use container management systems, such as Google’s Kubernetes or Docker’s Swarm.
- The ability to access data using well-defined interfaces that deal with complex data using simplified abstraction layers. Containers have mechanisms built in for external and distributed data access, so you can leverage common data-oriented interfaces that support many data models.
- The ability to create machine learning systems made up of containers functioning as loosely coupled subsystems. This is an easier approach to creating an effective application architecture where you can do things such as put volatility into its own domain by using containers.
AWS introduced the most customer demanded feature “Elastic Volumes” available for all current-generation EBS volumes attached to current-generation EC2 instances.
This new capability allows you to modify configurations of live EBS volumes with a simple few console clicks. You can now dynamically increase volume size, tune performance (change IOPS) or change the volume type of any new or existing current generation volume with no downtime or performance impact.
You can continue to use your application while the change takes effect. This Elastic Volumes feature is available in all AWS regions at no additional cost
Fowler knows everything!
Being an avid follower of Martin Fowler, I could say hands down his predictions and observation would come true
We couldn’t ask for a clear and concise write up on ‘Serverless Architecture’
** MUST Read **