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5 Super SEO Blogger Tools

In this post, I have explained top blogging tools that need to be considered by every blogger. These tools help you write better SEO friendly blog posts.



1). Headline Analyzer The best tool is the EMV Headline Analyzer. When you enter the headline it analyzes it and gives you EMV ranking. When you get '50' and above it performs better SEO.

2). Headline Length Checker The usual headline length is 50 to 60 characters. Beyond that, the headline will get truncated and looks ugly for search engine users. The tool SERP Snippet Optimization Tool useful to know how it appears in the search results.

3). Free Submission to Search Engines The tool Ping-O-Matic is a nice free submission tool. After your blog post, you can submit your feed to Ping-O-Matic. It submits to search engines freely.

4). Spell and Grammar Check Another free tool is Grammarly, this tool checks your spelling and grammar mistakes. So that you can avoid small mistakes.

5). Keyword AnalyzerWordstream Keyword analyzer i…

The best Multi purpose Language Python, why it is so useful

Python and its powerful uses
#Python and its powerful uses:
I have recently started learning Python. During my learning time my friends have asked since you are interested in analytics why you need to learn Python. I explained below reasons. This is one of the powerful languages after Java.
  • Python is similar to many programming languages that people generally know about: Python is very similar to JavaScript, Ruby, and PHP in many respects. Most programmers have a working knowledge of these programming languages and this makes it easier for programmers to learn Python. The basic features of these languages such as the use of arrays, anonymous functions etc., are also present in Python. 
  • Python has very good machine learning libraries: The variety of machine learning libraries that are available in Python is large. One can choose between Scikitlearn, Keras, Theano and Tensorflow. Many neural network libraries such as Keras, Theano etc., are exclusively available in Python. So, if you want to do cutting edge machine learning work, you must know Python. 
  • Python excels at handling text data: Unlike statistical software environments such as R, Python excels at handling text data. People who know Python can easily mine text corpus for useful insights. Python also provides support for Natural Language Processing through NLTK and sPacy
  • Python makes distributed computing very easy: Apache Spark has a Python API called PySpark. Using this piece of software, one can easily do distributed computing. PySpark has in recent times become the de-facto API for Spark.
  • Extensive support for different data sources: It doesn’t matter if one needs to fetch data from an SQL server, a MongoDB database or JSON data from some web API; Python can easily support all these data sources with a very clean and elegant syntax.
The benefits of Learning Python

Learning Python has many advantages – it gives a user many skills, one can fetch data from different sources, create machine learning models and do distributed computing seamlessly. For any programmer, learning Python will not be a difficult task. One can reap a lot of benefits by devoting time to learning Python.

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Tokenization story you need Vault based Vs Vault-less

The term tokenization refers to create a numeric or alphanumeric number in place of the original card number. It is difficult for hackers to get original card numbers.

Vault-Tokenization is a concept a Vault server create a new Token for each transaction when Customer uses Credit or Debit Card at Merchant outlets 
Let us see an example,  data analysis. Here, card numbers masked with other junk characters for security purpose.

Popular Tokenization ServersThere are two kinds of servers currently popular for implementing tokenization.
Vault-based Vault-less Video Presentation on Tokenization
Vault-based server The term vault based means both card number and token will be stored in a Table usually Teradata tables. During increasing volume of transactions, the handling of Table is a big challenge.
Every time during tokenization it stores a record for each card and its token. When you used a card multiple times, each time it generates multiple tokens. It is a fundamental concept.
So the challe…