Collect and analyze data to build knowledge. Gather insights and improve decisions.

Real time processing

Collect, clean, enrich, aggregate data in real time. Correlate continuous streams of data to detect anomalies, raise actionable alerts, build learning models.

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. Apply the models in real time on a stream of events to predict the next event.

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.

Time series data analysis

Detect the patterns by analysing the time series data. Derive insights based on these patterns and predict trends.

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.