Whether your data is streaming or batch, and has Geospatial or Time Series attributes, DeepIQ simplifies building and scaling your data workflowsRequest a Demo
DeepIQ’s edge software supports native connectivity to all leading historians and industry standard PLCs. Scale to millions of messages per second and support transmission using secure, encrypted connections.
Infer your source to sink schema mapping automatically across your sources. Capture data schema at any stage of your workflow
Track changes to your data and capture slow changing dimensions with automated audit columns including dates and user details
Gain 50x execution speeds with Apache Spark based implicit computation and data parallelism
Visualize your data at any stage of your workflow and explore your data with statistics, samples and quality metrics
Remove outliers and smooth out noise using statistical Time Series data cleansing operations
Unify your Time Series data sources to a single frequency using interpolation techniques
Analyze millions of streaming tags or TBs of Time Series data without a single line of code
Create Time Series data models and persist into your favorite Time Series database including Delta Lake, Hive, Kudu, AWS Timestream, Azure TimeInsights or Google BigTable
Use geospatial queries to calculate containment, intersection, overlap and many other properties between your data sources
Fix bad quality or missing data issues with spatial interpolation techniques
Fine tune your geospatial workflow parameters by iterating your results with map based visualizations
Run laser fast joins against polygon and point datasets with our proprietary and automated geospatial indexing
In this whitepaper, we first explain the challenges in geospatial analytics. Then, we explain how DeepIQ addresses these challenges leveraging your execution environment for scalability and customizability.