Big data technology

 Big data technology

Big Data Technology

What is

Big Data Technology?

In the midst of a revolution driven by data engineering, researchers estimate that every person on earth is generating approximately 1.7 MB of data per second! Today, only 0.5% of accessible data is analyzed and used, as per the MIT Technology Review. This tremendous gap is a boon in disguise for big data technology, services, and advanced data analytics.

Moreover, the Big Data market revenues for software and services are expected to reach $103 billion in 2027. It is no surprise that a survey found that 79% of executives think that companies that do not embrace Big Data analytics, solutions, and strategies may face extinction.

Nitor is proud to capitalize on the tremendous potential that Big Data engineering tools and technology can offer.

Big Data &
Advanced Analytics
at Nitor

Our experts use robust conceptual models to extract insights and answer four key questions

  •    How to adopt Big Data strategy?
  • Derive important insights using advanced statistical analysis or data processing

  •    How to build customer satisfaction with Big Data?
  • Personalize your customer experience with fruitful and accurate insights to enhance customer satisfaction

  •    How to leverage Serverless Big Data application on AWS?
  • Perform real-time stream processing of multiple data types without needing to spin up servers or install software, via big data applications

  •    Why do you need Managed Big Data services?
  • Increase business efficiency and reduce risk by harnessing the power of data with Managed Big Data

Big Data Technology Offerings for your Business Needs

Our experts work with you during your entire Cloud journey to offer Big Data engineering services and build a customized roadmap so you can reap the benefits of our data and analytics solutions. Following is our proposed architecture, along with technology options, that provides a basis for the deployment of Big Data engineering services:

Data Extraction

Retrieve data from disparate data sources (database/SaaS platforms) in order to replicate it to a set destination or data warehouse

Data Ingestion

Ingest and enrich constant streams of data from heterogeneous sources like web/mobile apps and wearables, for a unified view of your data

Data Storage

Store raw data in a cost-effective data lake. Refine and transfer it to a secure, optimized data warehouse

Data Cleansing

Refine raw data in data lakes before transferring to data warehouses. Conduct data cleansing procedures with our Data LEGO framework

Database Modelling

Organize data in your optimized database. Eliminate data siloes and resolve reporting challenges. Create models for business problems. Decide schema on write/schema on reading strategy across various data sources/sinks

BI Analytics

Process data quickly and deliver automated, intelligent insights with sophisticated algorithms using the refined warehouse data

Advanced Analytics

Employ proven ML-based techniques to predict trends and detect potential threats, with the freedom to scale as you grow

Business Rule Configuration

Configure unlimited, customized business rules through a data pipeline, and transform the data for your required business rule configuration

Data Access Layer

Build an API layer to securely access data which acts as a decoupled, language-agnostic data/functionality interface to applications

Data Science

Write sophisticated algorithms that extract the maximum possible meaning from data and are critical to solving business problems

Data Engineering

Address the volume, velocity, value, variety, and veracity of data to derive utility from the insights provided by data scientists

Comments