Big Data

Predictability in much simpler way

Why Is Big Data Important?

Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

  • Determining root causes of failures, issues and defects in near-real time.
  • Generating coupons at the point of sale based on the customer’s buying habits.
  • Recalculating entire risk portfolios in minutes.
  • Detecting fraudulent behavior before it affects your organization.

SenseQue Advantage

Big Data Consulting

Define client's Big Data strategy, suggest and select the suitable technology tools and processes to achieve tactical objectives

Big Data Integration, Migration, Development and Implementation

Fast and reliable Big Data solutions to serve the growing needs of enterprises in the competitive environment

Data Lake Handling and Processing NoSQL data

Ability to harness more data, from more sources, in less time, and empowering users to collaborate and analyze data in various ways leads to improved, quicker decision making

Big Data Architecture and Solution Design

Our precisely designed big data architecture plays a fundamental role to meet your big data processing needs

5. Big Data Modelling and Algorithm Development

Models and algorithms to build brilliant solutions which make a big difference to our clients' businesses

Big Data Analytics

We provide comprehensive services to assist you in harnessing the power of your big data

7. AI and Machine Learning for Big Data

Big Data, Machine Learning, and AI can transform business processes

Big Data Testing, Provisioning and Automation

Comprehensive services needed for discovery, delivery, implementation, administration, testing, automation and maintenance for every big data solution

The approach

1

Extraction

Analyze the requirement and select the base tools like SQOOP, KAFKA, FLUME, Spark Streaming, Storm

2

Storage

Select data storage tools based on the data complexity. Tools like Microsoft, Casandra, MongoDB and HBase

3

Cleaning

Cleaning of the data with the help of Scala, Python, R, Park-SQL

4

Mining

Perform data Mining with Python, Spark, etc

5

Visualization

Visualize the data with the help of Table, PowerBI, Talend, etc.