Business Insights
at the Speed of Data

Machine learning based cloud Data supplychain and decisioning solution to derive insights

What we do

Our data expert helps businesses become data-driven by transforming data into valuable knowledge to anticipate market change, introduce new products and optimize cost

Data Infrastructure Modernization

Migrate your existing data pipeline to a modern and secure big data pipeline powered by major cloud providers. This ensures scalability, reduced maintenance, security, 24×7 uptime and reduced latency anywhere in the world.

Key Features

  • Real time data analysis
  • Optimize cost per KPI
  • Faster time to actionable insight
  • Connect and govern data from multiple data sources
  • Leverage multiple cloud platform (AWS,Azure, GCP)

Demand forecasting for retail

Advanced machine learning makes it easier for retailers to anticipate even the slightest shifts in demand which could cause issues for the retailer and their entire supply chain.

ETL as a Service

Collect your raw data scattered across multiple sources and give it a cleaner, structured and meaningful representation. This will help in saving storage and DB space and get all the data in one place for convenience.

Key Features

  • Collect raw or structured streaming data
  • Manage schema and mapping
  • Cleaning data from unstructured format
  • Auditing and logging
  • Scalable architecture

Research and report generation for media platform

Automate insight-mining from enormous amounts of data collected from various disparate sources can speed up generation of useful reports on key societal trends

Cloud Data Analytics

Utilize cloud based industry leading technologies to get real time business value insights and optimized cost per KPI.

Key Features

  • Machine learning based analytics model
  • Custom analytics as per the business demand
  • Leverage leading platforms like tableau, powerbi, quicksight

Research and report generation for media platform

Automate insight-mining from enormous amounts of data collected from various disparate sources can speed up generation of useful reports on key societal trends

Technology Stacks

Components of Data Engineering Pipeline

Our data specialists support enterprises with data-driven solutions that reduce time-to-value and optimize costs.

Data collection

Data collection is the process of obtaining data from a database or SaaS platform so that it can be replicated to a destination — such as a data warehouse.

Data transformation

Data transformation is the process of converting data from one format to another, typically from the format of a source system into the required format of a destination system.

Data pipelines

A data pipeline essentially is the steps involved in aggregating, organizing, and moving data. Modern data pipelines automate many of the manual steps involved in transforming and optimizing continuous data loads.

Data science and ML

Data science automates the process of Data Analysis and makes data-informed predictions in real-time without any human intervention.

Data visualization

Data visualization is the process of giving a clear idea of what the information means by giving it a visual context through charts, graphs and numbers.

Why Choose our Data Engineering Solutions?

Leverage our expertise in multiple major cloud solution provider like Azure, AWS and GCP, and optimize your data platform based on the best practices of that cloud provider.

Use cloud based data streaming services to transfer data in real-time to get faster insights.

Leverage cloud based machine learning models to perform analytics on your data.

Leverage an event-based serverless architecture for your data pipeline which can be scaled as and when required in a cost-optimized fashion.

Data Engineering Frequently Asked Questions

Data drives the operations of businesses, small and large. Businesses use data to provide answers to relevant inquiries that range from consumer interest to product viability etc.
1) Designing a highly scalable and efficient data solution
2) Implementing the ETL processes
3) Validating and verifying data quality
4) Delivering analytics and KPIs based on the business need
Data science projects often referred to as the projects which involve performing analysis and machine learning on a large set of already available data.
Data engineering on the other hand is a process where you collect, transform and store the large set of data from different sources via a data pipeline.
Snowflake, Amazon RedShift, Azure data lake, AWS S3

Case Studies

Our team has worked on projects across domains

Industry - Fin-tech

We built an independent, fully integrated app that automated the process of identifying mismatches between large ...

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Industry - Media

We built a platform that automated data collection and segregation, harmonized different file types ...

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Industry - Technology

We developed a connector engine, making it possible to extract customizable user-defined data from any ...

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Industry: Financial news

Intel chose TechVariable to develop the platform because of its technical expertise, offshore development ...

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Need a custom software application for your business?

We at TechVarible do acknowledge that one size will not fit all. Hence, we work in collaboration with you to identidy, analyze & then develop a solution that fulfils your needs.

Either we will define the functional scope of your project to estimate the timeline and budget or you can create your own agile team from among our recources.

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Blogs & Articles

Check out the new stories published by our team

Why and when to use D3 for data visualization May 3, 2022 by Ratnadeep Bhattacharjee Posted in: Data Analytics
The rise of data apps & how to build app in a blazing-fast way May 5, 2022 by Ratnadeep Bhattacharjee Posted in: Data Analytics

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Give us a call or fill in the form below and we will contact you. We endeavor to answer all inquiries within 24 hours on business days.

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