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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…

Hadoop real story to process unstructured data

Hadoop real process
Hadoop Real Process
Hadoop comes into picture to process large volume of Unstructured data. The structured data is already taken care by traditional databases.

Role of traditional databases

Traditional relational databases have been able to store massive data sets for a long time. An Oracle 10g database can store over 8 Petabytes while for many years DB2 databases have been capable of storing well over 500 Petabytes. Of course, this is all theoretical. 

  • No customer has an Oracle or DB2 database that approaches sizes even close to that. Why? Because the speed, or velocity, at which data can be loaded and queries can be executed approaches zero well before then.
  • Similarly, all traditional relational databases can store any variety of data as text or binary large objects. The problem is that large volumes of unstructured data cannot be moved fast enough to enable rapid search and retrieval.

ETL role in the age of Hadoop

Running constant and predictable workloads is what your existing data warehouse has been all about. And as a solution for meeting the demands of structured data—data that can be entered, stored, queried, and analyzed in a simple and straightforward manner—the data warehouse will continue to be a viable solution. Storing, managing and analyzing massive volumes of semi-structured and unstructured data is what Hadoop was purpose-built to do.

  • Unlike structured data, found within the tidy confines of records, spreadsheets and files, semi-structured and unstructured data is raw, complex, and pours in from multiple sources such as emails, text documents, videos, photos, social media posts, Twitter feeds, sensors and clickstreams.
  •  Hadoop and MapReduce enable organizations to distribute the search simultaneously across many machines, reducing the time to find relevant nuggets of information in large volumes of data in a scalable way. That’s why Hadoop is being adopted by bleeding edge enterprises moving into the multi-petabyte club. There are already some environments that break the 100 Petabyte level, and theoretically can continue to scale.
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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…