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Data Operations

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We cater to this need via a focused data operations offering.

Data Operations

Data Operations is a responsive, process-oriented methodology for creating and delivering analytics. Itelenet connects the data operation team with data engineers and data scientists to deliver the tools, procedures, and organizational structures and support the data-focused company.

Data is increasingly recognised as one of the most important assets of a business, how the business then uses this data will have a direct impact on its ability to compete in the market. As such, it’s vital that the databases which are underpinning the business-critical applications are kept highly tuned and are available at all times.
 
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Create business value from Data Operations

From groundwork to experience, data operations have the skill to enable the solutions, cultivate data products and initiate data for business value across all technology. These occurrences demand an entirely new enterprise platform that is scalable, dynamic, data formats agnostic, tremendously cost-efficient for the build as well as operations.

Data operation is a method of designing, implementing and maintaining a distributed data architecture.

Data operation implementation:

Add data and logic tests

Add data and logic tests are important insures that a feature release is of high quality without requiring time. We are experts in practice logical reasoning tests and deductive data as well as identifying flaws in a piece of information.

Use a version control system

With version control software, you can keep track of every alteration to the code in a exceptional kind of database. Developers can easily turn back the clock and compare earlier versions of the code to fix the error while minimizing disruptions.

Branch and merge

To merge branches locally, we let you switch to the branch you want to merge into. It lets you take the autonomous lines of development made by the git branch and incorporate them into a single branch.

Use multiple environments

Use configures app performance based on the runtime environment using an environment variable. Using multiple environments keeps a team productive. Having a selection of environments enables a team to work on parallel development efforts.

Reuse and containerize

Reuse code as configuration. With data operations, the data analytics team moves at a great speed using highly optimized. When we perform reusing code, we reuse data analytics components.

Parameterize your processing

Parameterization is required when virtual users will make a POST, PUT, or PATCH request in a test. Parameterizing your processing can help in preventing server-side caching from affecting the load test.