With massive amounts of data being generated by devices and humans, thanks to the evolution of mobile and IoT, there is a need to structure the data, store and process efficiently to deliver useful insights. A solution that worked for thousands of records does not automatically scale for millions. Every incremental benefit results in huge gains due to sheer scale.
Real time processing
Ingest and correlate continuous streams of structured data in real time to detect anomalies, raise actionable alerts, build learning models, etc. Enrich, aggregate and stream the real time data into data stores while ensuring that data is usable with lowest latency.
Data store management
Improve query responsiveness by devising effective data sharding and replication strategies that also bring about better availability to the active data set. Identify and stream out inactive data onto secondary storages so that queries execute on a focussed data set.
Structure the application data to derive insights in the form of counters, trends, ranking, comparison graphs, etc. Devise aggregations and correlations along different dimensions to help in analyzing from different business perspectives.
Reliable data processing
Design workflows for reliable transfer of data from ingestion systems to data stores. Workflows that are inherently designed to scale and be resilient to failures.
Real time predictions
Build predictive machine learning models offline based on the past events. Apply the models on a stream of events in realtime to predict the next event.
User behaviour analysis
Analyze behavioral patterns from large data sets and build learning models. Use these learning models to predict and influence the user’s actions.
Time series data analysis
Detect the patterns by analysing the time series data. Derive insights based on these patterns and predict trends.