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.
- Added support to log the user roles during login, and the
ability to use encrypted communication among distributed system
- SpatioTemporal enhancements, including support for the ST_POINT
geo constructor and performance improvements for ST_CONTAINS.
- Performance and memory management improvements including
certain high-cardinality GROUP-BY queries, and
support for the OPTIMIZE command for sharded tables.
- Immerse now integrates with JupyterLab. You can
send SQL queries to Jupyter from SQL Editor, or access it
directly from top level navigation. See
Jupyter Integration for setup instructions.
- Combo chart now supports zooming and panning on large time
series measures down to millisecond-level granularity.
- New Immerse features include the new Dark Mode theme, the
ability to duplicate charts within dashboards, and export and
import of dashboard metadata.
- Added support for Window functions in OmniSciDB. This is an
early iteration of an often-requested SQL feature allowing for
analytic computations over rolling windows. See the release notes
and documentation for details and known limitations.
- Added support for Visual Data Fusion, the ability to add
measures on a combo chart from more than one source table,
and also crossfilter across these sources. This is a natural
complement to multi-layer geospatial chart.
- Distributed Rendering support for polygons supporting much
larger shape datasets within analytical workflows.
- Filters on aggregates in Pointmap and Scatter Plot, equivalent
to applying a HAVING filter on SQL aggregate queries.
- INSERT TABLE AS SELECT (ITAS) support in both single-node and
Distributed modes, allowing for easier ELT (Extract/Load/Transform)
type workflows in OmniSciDB, typically combined with CTAS
(CREATE TABLE AS SELECT).
- Data import status within Immerse for time estimation and import
Read more at the OmniSci Blog
- 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.
Read more at the OmniSci Blog
- 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.
Read more at the OmniSci Blog.
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