Big Data Analytics

We empower companies to discover the full potential  of their data and deliver meaningful insights
By working with Kyniska for your Big Data Analytics requirements, you obtain access to our expertise, skills, and dedication to delivering creative solutions that produce corporate value.

Our Big Data Analytics Services

Data Engineering Solutions

With a contemporary design, you can guarantee access to accurate data by collecting, transforming, and migrating it efficiently.

AI/ML Solutions

Use artificial intelligence and machine learning to automate and improve operations for higher productivity.

BI Solutions

Use intuitive knowledge and rapid modeling to take a conservative approach to operational efficiency and development.

MLOps Solutions

We focus on developing computer vision solutions that evaluate and comprehend visual data using innovative methodologies and algorithms.

What distinguishes Kyniska from other Big Data Analytics providers?

Our data analytics solutions provide numerous benefits, including better operational effectiveness, customized service to clients, improved risk management, and flawless data protection. We guarantee your data is used to its best capacity and adds the most value to your firm.

Quick Results

Goal Oriented

24/7 Support

Tailored Solutions

Streamline Workflows

Strategic Planning

Improve Data Security

Effortless Collaboration

Unlock the Power of Business Data Analytics Today!

Data Analytics in Action

Data Lake
End User

Tech Stacks












Ant Design

Frequently Asked Questions

Big data is defined as huge and complicated datasets that cannot be efficiently processed or evaluated using typical data processing techniques. Big data differs from traditional data in that it is larger, faster, more diverse, and more accurate.

Big data analytics uses a wide range of technologies and tools, including distributed computing frameworks (e.g., Hadoop, Spark), NoSQL databases (e.g., MongoDB, Cassandra), data warehouses (e.g., Amazon Redshift, Google BigQuery), machine learning libraries (e.g., TensorFlow, sci-kit-learn), and data visualization tools (e.g., Tableau, Power BI).

Big data analytics is commonly used in analytics for clients, analytics for marketing, risk analytics, fraud detection, supply chain improvement, predictive maintenance, sentiment analysis, recommendation systems, and Internet of Things (IoT) analytics.