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How to Work With Tuple in Python

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Tuple in python is one of the streaming datasets. The other streaming datasets are List and Dictionary. Operations that you can perform on it are shown here for your reference. Writing tuple is easy. It has values of comma separated, and enclosed with parenthesis '()'. The values in the tuple are immutable, which means you cannot replace with new values. #1. How to create a tuple Code: Tuple example my_tuple=(1,2,3,4,5) print(my_tuple) Output: (1, 2, 3, 4, 5) ** Process exited - Return Code: 0 ** Press Enter to exit terminal #2. How to read tuple values Code: print(my_tuple[0]) Output: 1 ** Process exited - Return Code: 0 ** Press Enter to exit terminal #3. How to add two tuples Code: a=(1,6,7,8) c=(3,4,5,6,7,8) d=print(a+c) Output: (1, 6, 7, 8, 3, 4, 5, 6, 7, 8) ** Process exited - Return Code: 0 ** Press Enter to exit terminal #4.  How to count tuple values Here the count is not counting values; count the repetition of a given value. Code: sample=(1, 6, 7, 8, 3, 4, 5, 6, 7, 8

Data Vault Top benefits Useful to Your Project

Data Vault 2.0 (DV2) is a system of business intelligence that includes: modeling, methodology, architecture, and implementation best practices.
The benefits of Data Vault
The components, also known as the pillars of DV2 are identified as follows:
data vault
  • DV2 Modeling (changes to the model for performance and scalability)
  • DV2 Methodology (following Scrum and agile best practices)
  • DV2 Architecture (including NoSQL systems and Big Data systems)
  • DV2 Implementation (pattern-based, automation, generation Capability Maturity Model Integration [CMMI] level 5)
There are many special aspects of Data Vault, including the modeling style for the enterprise data warehouse. The methodology takes commonsense lessons from software development best practices such as CMMI, Six Sigma, total quality management (TQM), Lean initiatives, and cycle-time reduction and applies these notions for repeatability, consistency, automation, and error reduction.

Each of these components plays a key role in the overall success of an enterprise data warehousing project. These components are combined with industry-known and time-tested best practices ranging from CMMI to Six Sigma, TQM (total quality management) to Project Management Professional (PMP).

Data Vault 1.0

Data Vault 1.0 is highly focused on just the data modeling section, while DV2 encompasses the effort of business intelligence. The evolution of Data Vault extends beyond the data model and enables teams to execute in parallel while leveraging Scrum agile best practices.

Data Vault 2.0

DV2 architecture is designed to include NoSQL (think: Big Data, unstructured, multistructured, and structured data sets). Seamless integration points in the model and well-defined standards for implementation offer guidance to the project teams.

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