Harness Complex Data Types with Ease
Ocient is engineered to natively load, transform, store, and analyze semi-structured data, so you can deliver critical insights across more data types faster than ever before.
Including strong support for nulls within arrays, jagged arrays of arrays, and for_some and for_all operations
Including the ability to perform linear algebra natively in SQL for scientific math and machine learning
Including arrays of tuples, tuples of arrays, and tuples of tuples, nested to an arbitrary depth to handle deeply nested structures
With constant streams of semi-structured data coming from a growing number of sensors, devices, and more, Ocient delivers important performance gains for semi-structured data analysis.
Place secondary indexes on massive structured and semi-structured datasets and their corresponding nested structure to accelerate performance without impacting cost.
Add new data without complex index maintenance. With no global indexes, each segment contains its own index that resides alongside the data. You get the performance-boosting power of secondary indexes without any of the hassle.
Ocient goes beyond support of nest, unnest, and array_agg functions to provide deeper analysis with native functions and operations directly on arrays, tuples, and matrices, resulting in extremely high-performance SQL.
- Convert nested, structured objects from source data into desired relational format
- Select arbitrarily complex nested arrays, objects, and values
- Gracefully handle missing values
- Easily normalize or denormalize data via array and tuple manipulation, filtering, and pivoting
- Apply transformations over arrays and structured objects to create derived fields during loading
With the ability to execute on an advanced class of SQL queries faster than any competitor at hyperscale, Ocient puts you ahead of the curve and opens up new possibilities for relational analytics on everything from complex adtech datasets to financial services and IoT data.
Learn how you can deploy Ocient and save on always-on analysis for complex data types.