DeepIQ

Engineer

Whether your data is streaming or batch, and has Geospatial or Time Series attributes, DeepIQ simplifies building and scaling your data workflows.

Engineer

Transform

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.

Schema Mapping

Infer your source to sink schema mapping automatically across your sources. Capture data schema at any stage of your workflow.

Change Data Capture

Track changes to your data and capture slow changing dimensions with automated audit columns including dates and user details.

Speed

Gain 50x execution speeds with Apache Spark based implicit computation and data parallelism.

Monitor

Visualize your data at any stage of your workflow and explore your data with statistics, samples and quality metrics.

Engineer

Time Series processing

Clean

Remove outliers and smooth out noise using statistical Time Series data cleansing operations.

Interpolate

Unify your Time Series data sources to a single frequency using interpolation techniques.

Scale

Analyze millions of streaming tags or TBs of Time Series data without a single line of code.

Persist

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.

Engineer

Geospatial Data Processing

Query

Use geospatial queries to calculate containment, intersection, overlap and many other properties between your data sources.

Clean

Fix bad quality or missing data issues with spatial interpolation techniques.

Visualize

Fine tune your geospatial workflow parameters by iterating your results with map based visualizations.

Join

Run laser fast joins against polygon and point datasets with our proprietary and automated geospatial indexing.

Whitepaper - Geospatial Analytics at Scale

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.

Engineer

Reconcile

Use DeepIQ’s proprietary distributed pattern matching and machine learning algorithms to seamlessly match and reconcile multiple datasets with millions of entities.

Match Data

Match your entities or events on text, dates, numbers, and geospatial attributes using sophisticated pattern matching algorithms.

Create Golden Records

Bring together data from multiple data sources to create golden records using sophisticated statistical algorithms.

Identify millions of Entities

Scale to millions of entities using intelligent computational parallelization algorithms.

Automate with AI

Use AI to outperform traditional, human intensive data mastering approaches.

Whitepaper - Master Data

In this whitepaper, we will explain the challenges in Master Data Management. We will dive into the traditional approaches to manage master data, and then show how DataStudio solves these challenges.

Engineer

Model

Build analytic ready datasets integrating IoT, transactional and geospatial sources and implement industry leading machine learning models that are trained on your integrated datasets.

Machine Learning Pipelines

Use a simple drag and drop interface to normalize data, reduce dimensions and build deep learning/machine learning models.

Model Optimization

Find the best model for your problem by scaling your hyperparameter search to 100s of nodes if desired.

Structured Learning

Use our patent pending structured learning algorithms to model relationships between predicted outputs even for sparse time-series, geospatial and combinatorial datasets.

Model Lifecycle Management

Maintain a central model registry and manage the ML lifecycle, including experimentation and deployment.

Data science for industrial digital transformation - Hype or Hallelujah?

In the digital world, data scientists have a linear journey to ROI. For them, building a machine learning model, graduating it to an A/B test environment and pushing it to production when proven is a streamlined process. We, the industrial data science practitioners, have a much more convoluted job. In a three-part article series, we delve on the topic of how to tackle these challenges head on.

Explore the World with Generative AI Models: Analyzing Geospatial Data Using Natural Language

DeepIQ’s Generative AI capabilities are a game changer for geoscientists and data engineers alike.

Take your data engineering and exploration to the next level with our comprehensive platform, starting with seamless ingestion of disparate geospatial sources, preparing high-quality data lakes, and unlocking valuable insights.