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Thursday, December 28, 2017

My comments on "The End of #CloudComputing"

Source: https://a16z.com/2016/12/16/the-end-of-cloud-computing/

Here is my comments:
- IoT replaces the Cloud with Fog computing. It is wise to process data just where it is captured like the human nervous system does a lot of processing before sending information to the brain.
- So centralized cloud would still be needed as a background ML engine to process big data, maybe....
- Data should be decentralized too and blockchain technology is the way to do that.
- Buttomeline is the optimal structure should be combination of centralized and decentralized computing/data processing. Again like human synergistic loop concept: smart sensors -  spine brain -  brain - back to muscles,

R or Python? Six Reasons To Learn R For Business

DS4B Tool Ratings

About Python

Python is a general service programming language developed by software engineers that has solid programming libraries for math, statistics and machine learning. Python has best-in-class tools for pure machine learning and deep learning, but lacks much of the infrastructure for subjects like econometrics and communication tools such as reporting. Because of this, Python is well-suited for computer scientists and software engineers.

About R

R is a statistical programming language developed by scientists that has open source libraries for statistics, machine learning, and data science. R lends itself well to business because of its depth of topic-specific packages and its communciation infrastructure. R has packages covering a wide range of topics such as econometrics, finance, and time series. R has best-in-class tools for visualization, reporting, and interactivity, which are as important to business as they are to science. Because of this, R is well-suited for scientists, engineers and business professionals.

What Should You Do?

Don’t make the decision tougher than what it is. Think about where you are coming from:
  • Are you a computer scientist or software engineer? If yes, choose Python.
  • Are you an analytics professional or mechanical/industrial/chemical engineer looking to get into data science? If yes, choose R.
Think about what you are trying to do:
  • Are you trying to build a self-driving car? If yes, choose Python.
  • Are you trying to communicate business analytics throughout your organization? If yes, choose R.

Saturday, December 16, 2017

How to do Capacity Management in the Cloud

Speaking about System (on Cloud)  Management by Exception - the main topic of this blog - the  anomaly (exception) detection become extremely important technique as based on that video there is apparent shift from protectiveness (classical capacity management) to reactivates. So  the SETDS method works  well applying now to cloud capacity/performance data.   

Thursday, December 7, 2017

NCA CMG meetup meeting 12/6/2017 (Capacity Management on Cloud vs. on-Prem and z/OS Performance HOT Topics)

  • (0:02:17) Capacity Planning for Pervasive Encryption with zBNA - Kathy Walsh, IBM Distinguished Engineer - Abstract: This session will discuss the capacity planning methods and tools available to gain a better understanding of the impact of a pervasive encryption implementation strategy. The session will discuss the system requirements necessary to build a capacity plan and will review the steps necessary to complete the planning. Information on both DFSMS data set encryption and CF structure encryption will be shared. The capability to estimate the capacity impact on current and future technology such as a z14 processor will be covered.
  • (01:08:00) Sponsor presentation: Talend | Open Source Cloud Integration Abstract: Talend is a next-generation leader in cloud and big data integration software. With Talend, you can spin up and spin down EMR clusters so you never waste resources and add unneeded expenses to your AWS account. At scale, this can save organizations millions. 
    Presenters: Karim Sharara (Account Executive)  and Vincent Wilmot (Senior Solutions Engineer)
  • (02:09:00 - Partial Capture) Is Capacity Management Needed in the Cloud? - Kevin McLaughlin (https://cmgimpact.com/speakers/kevin-mclaughlin/) Capital One director (he will repeat his Velocity and CMG imPACT’17 conferences presentation) - Abstract: The cloud holds the promise of bottomless capacity, available instantly. Recently, Capital One has been shifting a significant portion of its workload to the public cloud. Kevin McLaughlin explores what capacity management looks like in the cloud, which old concepts still apply, which should be retired, and what new metrics become important in the process. Kevin also covers the importance of performance management and outlines what needs to be monitored as workloads transition to the cloud and what to monitor once a workload is fully in the cloud, as well as considerations for ensuring the legacy environment maintains sufficient capacity during the transition. (see more details HERE (http://www.trub.in/2016/08/cmg-impact-and-velocity-conferences.html)