Our big data consulting service begins with understanding your data sources across various domains, building suitable data models to conceptualize a practical and efficient big data solution. Infrastructure plays a key role in the process and our skilled systems engineers design the right cloud or in-house models. Our QuickData methodology focuses on mapping out a clear road map to optimize efficiency and maximize client revenue.
For storage, we use elastic cloud environment to handle massive data and NoSQL database to store big data. We have created a BUCKET based data structure for easy and fast retrieval, and use a powerful algorithm for crunching data.
One of our key strategies to manage the data for real-time analytics is to preprocess it periodically, thus shortening the time cycle. We’ve created powerful APIs in Amazon Web Services to receive the data stream from data sources
Apart from using visualization tools that have good data management like Qlik, PowerBI & IBM Watson, we also build custom visualization solutions using tools like D3.js to suit the unique needs of our customers.
Whirldata Value Add
Our BigData analytics solutions help in reducing production cost as we use reliable Opensource applications from market leaders such as Apache. We ensure quality as we work closely with the client’s team to ensure the right data is taken for achieving their stated goal. We create customized algorithm to provide insights into the data, predict future trends and provide vision that helps in better decision making.
Whirldata also provides training for client teams resources in latest Big data and visualization applications to enable them to leverage available data and improve efficiencies. In addition, our domain expertise and understanding helps us recommend the right solution for our clients based on their production environment and the data sources available. Our cross domain expert team works across industry segments creating relevant solutions for big data analytics including:
Financial institutions: Manage big data and analyze the performance to predict future trends, recommend best investment options.
Solar power generation: Real-time monitoring of the solar panels to track impact on environment and damages to improve efficiency.
Manufacturing: Collect data on machine’s performance periodically and create an ALERT system for warning in case of failure.
Customer Relationship Management: Analyze customer behavior and market trends based on sentiment analysis, and work with customer feedback to improve services.
A sample architecture of an implementation of a data collection and reporting solution for industrial IoT built on AWS stack