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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…

5 top Data Storage Patterns to handle variety of data

Data is now a variety of patterns. Data is now more than just plain text, it can exist in various persistence-storage mechanisms, with Hadoop distributed file system (HDFS) being one of them.

The way data is ingested or the sources from which data is ingested affects the way data is stored. On the other hand, how the data is pushed further into the downstream systems or accessed by the data access layer decides how the data is to be stored.

Role of RDBMS

The need to store huge volumes of data has forced databases to follow new rules of data relationships and integrity that are different from those of relational database management systems (RDBMS). RDBMS follow the ACID rules of atomicity, consistency, isolation, and durability.

These rules make the database reliable to any user of the database. However, searching huge volumes of big data and retrieving data from them would take large amounts of time if all the ACID rules were enforced.
A typical scenario is when we search for a certain topic using Google. The search returns innumerable pages of data; however, only one page is visible or basically available (BA). 
The rest of the data is in a soft state (S) and is still being assembled by Google, though the user is not aware of it. By the time the user looks at the data on the first page, the rest of the data becomes eventually consistent (E). This phenomenon—basically available soft state and eventually consistent—is the rule followed by the big data databases, which are generally NoSQL databases following BASE properties.

Database theory suggests that any distributed NoSQL big database can satisfy only two properties predominantly and will have to relax standards on the third. 

The three properties are consistency, availability, and partition tolerance (CAP). This is the CAP theorem. 

Polyglot pattern: Multiple types of storage mechanisms—like RDBMS, file storage, CMS, OODBMS, NoSQL, and HDFS—co-exist in an enterprise to solve the big data problem.

The aforementioned paradigms of ACID, BASE, and CAP give rise to new big data storage patterns like below:
  • Façade pattern: HDFS serves as the intermittent Façade for the traditional DW systems. 
  • Lean pattern: HBase is indexed using only one column-family and only one column and unique row-key. 
  • NoSQL pattern: Traditional RDBMS systems are replaced by NoSQL alternatives to facilitate faster access and querying of big data.

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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…