AWS : Leader in Gartner's Quadrant 2020

AWS has been named as a Leader in the Magic Quadrant for Cloud Infrastructure and Platform Services - 2020. The AWS is placed highest in Ability to Execute and furthest in Completeness of Vision. As compared to Azure it has 4X market customers base.

Leaders Quadrant

    1) AWS (2006)

    2) Microsoft Azure (2010)

    3) Google (2011)

Challenger's Quadrant - No cloud provider

Nice Player Quadrant -

    1) Alibaba 

    2) Oracle

    3) IBM

    4) Tencent Cloud

Visionaries Quadrant- No cloud provider

 

 

AWS Vs Azure Vs Google Cloud: Market Shares and Growth Rate

In terms of cloud market, AWS has been the market leader since beginning. If you take a look at the following graph, it clearly depicts that:

• AWS is leading with around 30 percent of public cloud share and rest i
  
• Microsoft Azure is on the second place, owning around 16 percent of the worldwide market share.
• Google, on the third place, owns up to 10 percent of the market share worldwide.

 

 

 

• AWS has 5 years advantage over GCP & Azure.
• AWS is has better reach and available across more zones than GCP and Azure
• AWS has one-third of market shares in its name and is clear leader
• AWS has good growth rate but GCP is closing on with 100 percent growth
• Services offering of AWS, Azure, GCP :
  • When it comes to the number of services, the winner is AWS.
  • Regarding the integration with open-source and on-premise systems, such as MS tools, that are mostly used in almost all organizations, the winner is Azure.
• Pricing Models: Google Cloud has most economical pricing but Azure and AWS are quite close and AWS has more features and services as compared to GCP.
  

 

Data Preprocessing - Binning ( Data Science and stuff)

A simple, yet powerful concept used during data analysis that categorizes the data into different buckets/bins. One important point to remember is that “binning” and “clustering” are not the same and vary considerably in logic and implementation. Binning as a method of data pre-processing. Data binning (or bucketing) groups data in bins (or buckets), in the sense that it replaces values contained into a small interval with a single representative value for that interval. Sometimes binning improves accuracy in predictive models.

For example, if you have census data, you can bin “age” into [0 to 5], [5 to 9], [10 to 14] and so on.

Image of binning census data by age group -

Sometimes, binning can improve accuracy of the predictive models. In addition, sometimes we use data binning to group a set of numerical values into a smaller number of bins to have a better understanding of the data distribution. 

As example, “a automobile price” is an attribute range from 5,000 to 45,500.
Using binning, we categorize the price into three bins: low price, medium price, and high
prices. In the actual automobile dataset, ”price" is a numerical variable ranging from 5188 to 45400,
it has 201 unique values. We can categorize them into 3 bins: low, medium, and high-priced cars.
In Python we can easily implement the binning: We would like 3 bins of equal binwidth, so
we need 4 numbers as dividers that are equal distance apart.
1) First we use the numpy function “linspace” to return the array “bins” that contains
4 equally spaced numbers over the specified interval of the price.                                                       2)We create a list “group_names “ that contains the different bin names.
3) We use the pandas function ”cut” to segment and sort the data values into bins.
We can then use histograms to visualize the distribution of the data after they’ve been
divided into bins. This is sample histogram that we plotted based on the binning that we applied in the price feature. From the histogram plot, it is clear that most cars have a low price, and only very few cars have high price.

Another example of binning employee data by salary.