Big Data Infrastructure

Helping clients put big data into efficient use by providing a full range of big data services: big data consulting, implementation, support, and big data managed analytics services.


Big Data Consulting

By rendering big data implementation and improvement consulting services, We helps with:

Designing a roadmap to leveraging big data potential.

  • Recommending on data quality management.
  • Designing big data solution’s architecture, implementation strategy, user adoption strategy, evolution strategy, etc.

Big Data Implementation

VitCamp IT implements big data solutions with some or all of the following architecture components: a data lake, a data warehouse, ETL processes, OLAP cubes, reports, and dashboards. Additionally, We sets up data quality management and data security practices, trains, and applies machine learning models.

Big Data Support

With big data support services, We provide big data solution administration (updating software, adding new users, handling permissions), big data administration (data cleaning, backup, and recovery), a big data solution’s regular health checks and continuous monitoring to identify problems early and effectively troubleshoot them.

Big Data Managed Analytics Services

With managed analytics services, We helps companies who want to quickly derive insights out of their big data and focus on their core business activities without developing and managing a full-scale big data solution.

Enhancing Qualitative Characteristics


Verifiability implies consensus between the different knowledgeable and independent users of information. Such information must be supported by sufficient evidence to follow the principle of objectivity.


Comparability is the uniform application of methods across entities in the same industry. The principle of consistency is under comparability. Consistency is the uniform application across points in time within an entity.


Understandability means that reports should be expressed as clearly as possible and should be understood by those to whom the information is relevant. Timeliness: Timeliness implies that information must be presented to the users before a decision is to be made.