Combining streaming and historical data in one platform enables trend analysis that adds context to real-time updates for rights insights, and eliminates the development and maintenance overhead of replicated queries and analytics on separate systems.

An intuitive user interface supports the creation and deployment of data pipelines for defining ingestion, transformation, analytics, ML models, and storage that can be reused across multiple environments, whether in the cloud, on-prem or at the edge. The interface then enables data scientists to easily load, transform, query, and visualize massive datasets in near real-time.

Functionality includes:

• Creating and deploying data pipelines that include ingestion, transformation, analytics, ML models, and storage; and exporting them to other deployments for reuse and consistency
• Library of mathematical and statistical analytics functions
• Date validation, cleansing, and normalization
• Querying, sorting and bucketing techniques optimized for time-series data with columnar storage
• Data import & export UI for easier data capture, transformation and enrichment
• A development environment with additional tools for creating, editing, debugging, testing, and versioning applications
• An integrated dashboards visualization layer for exploring and presenting data