Gain new insights to your data with fast, responsive graphics and SQL queries.
Install and configure your OmniSci instance, then load data for analysis.
Extend OmniSci with custom charts and interfaces. Contribute
to the OmniSci Core Open Source project.
- CTAS (CREATE TABLE AS SELECT) on distributed installations.
- Import Parquet format data files.
- Updates on variable length columns.
- Single Sign-on with SAML for compatibility with Okta and
- Extended NULL support for variable length arrays. The full
array can now be null, in addition to individual elements.
- Support for high-precision timestamps, up to nanosecond precision.
- Improved performance loading String Dictionary from storage.
- Significantly more scalable rendering from projection (non-aggregate) queries.
- Immerse Data Manager:
- Delete, Append, and Truncate
(delete all rows from) tables.
- Auto-detect header rows on import, manually
indicate whether the first row is a header row.
- Improved performance for non-aggregated Choropleth/Pointmap/Scatterplot charts.
- Non-aggregated Choropleth now cross-filters on zoom.
- Enterprise trial version is now available.
- Better memory handling through improved estimation of GPU
memory requirements. Automatically run query on CPU if not enough
GPU memory is estimated to be available.
- Better handling of NULL values.
- DECIMAL/NUMERIC fields can be downcast to different scales and precisions.
- Dictionary size increased to 2.15 billion entries.
- Add support for the lasso filter on Linemap chart.
- Added clarity to formatting options and created a new option
to represent billions as B.
- Default ports changed from 9090-9094 to 6273-6280 to avoid collisions.
- Renamed key components from MapD to OmniSci. See the Release Notes.
- Improved geospatial function support
- Support for pct/blend accumulation rendering modes in distributed configurations
- Improved error tracking
- Support for SAML authentication with Okta
- Improved performance on String Dictionary import for multiple
String Dictionary-encoded columns
- More robust joins between different types
- Better compression on decimal/numeric types
- More efficient rendering of lines using the GPU rather than
first copying results to the CPU
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