DeepIQ DataStudio is an AI-powered Industrial DataOps platform purpose-built to manage time series, geospatial, and business data across complex operational environments. Built for scale, DataStudio enables seamless collaboration between IT and OT teams—accelerating data-driven decisions and industrial AI initiatives on AWS.
With DataStudio, you can:
- Ingest at Scale: Stream and batch-ingest high-frequency operational and geospatial data into your cloud data lake with enterprise-grade performance and security
- Engineer Industrial-Grade Data Lakes: Build and orchestrate multi-stage AWS data lakes with integrated IT/OT datasets, ensuring data quality, lineage, and contextual integrity
- Model Asset Hierarchies: Define and manage complex asset hierarchies directly within the data lake to enable semantic search, analytics, and model deployment
- Accelerate Predictive Intelligence: Develop and deploy hybrid physics-based and machine-learning models for predictive maintenance, equipment health monitoring, and production optimization.
DataStudio unifies the entire data lifecycle—from ingestion to modeling—into a single, intuitive platform designed for industrial scale. It empowplalatttiners asset-heavy organizations to unlock actionable insights faster, with less effort, on AWS.
In this section, you will explore various real-world applications of DataStudio across different industries. Detailed examples illustrate how companies have utilized DataStudio to overcome specific challenges and achieve significant results. By delving into these use cases, you will gain a deeper understanding of the practical benefits and versatility of DataStudio in enhancing data management, analytics, and operational efficiency.
DataStudio specializes in {Data+ML} Ops for time series, geospatial, and business data. It can provide solutions to any business that needs to manage and process large amounts of data and build insights into it. The current DeepIQ customer base spans Upstream, Downstream, Midstream, Renewable, Financial, Mining, and Agricultural verticals. With DeepIQ, these customers have built solutions for several high-value use cases, including:
- Equipment Predictive Health
- Production and Drilling Optimization
- Real-Time Operations Monitoring
- Mineral Prospectivity Analysis
- IT-OT Convergence Reports
- Carbon Emissions Monitoring
When the deployment is complete, the following resources are set up on AWS:
- EFS (Elastic File System)
- EKS (Elastic Kubernetes Service)
- VPC (Virtual Private Cloud)
- ECR (Elastic Container Registry)
DeepIQ supports the ingestion of data from the following sources.
- Time series: WITSML 1.4, WTIMSL 1.3, OSI PI, Aspen Tech IP21, Historians/SCADA/PLC systems that support OPC HDA/OPC UA/MQTT (Examples include Honeywell PhD, GE Proficy, Cygnet)
- Geospatial Sources: Different types of Map formats and satellite imagery
In addition, DeepIQ supports read/write access to the following sources:
- Cloud Databases: AWS Databases (Redshift/Timestream), Snowflake, Delta Lake,
- Streaming Sources: AWS Kinesis, Confluent Kafka, Azure Event Hub
- On-Premise Databases: All data sources that support JDBC/ODBC, Hive, HBase, MongoDB
DeepIQ supports 250+ inbuilt transformations for extensive scale engineering of time-series, geospatial, and structured data sources.
DeepIQ provides a no-code app to build machine learning pipelines spanning supervised/unsupervised/deep learning use cases. It also offers end-to-end machine learning features. Special-purpose pipeline features are provided to address challenges in time series and geospatial data sources.
DeepIQ has patent-pending algorithms that allow users to build knowledge models and use AI to optimize their performance.
The current DeepIQ customer base spans Upstream, Downstream, Midstream, Renewable, Financial, Mining, and Agricultural verticals. With DeepIQ, these Customers have built solutions for several high-value use cases, including:
- Equipment Predictive Health
- Production and Drilling Optimization
- Real-Time Operations Monitoring Mineral Prospectivity Analysis
- IT-OT convergence reports
- Quality Monitoring
- Carbon Emissions Monitoring
The introductory material describes all deployment options discussed in the user guide, including single-AZ, multi-AZ, and multi-region deployments. These options provide flexibility and scalability to meet the diverse needs of different organizations.
Managed Service: This option involves installing the application on the customer’s infrastructure. It allows organizations to maintain control over their environment while leveraging the capabilities of DataStudio. The managed service deployment ensures that the application is tailored to the specific requirements and configurations of the customer’s existing infrastructure.
- SaaS Offering: The publisher hosts the applications on a dedicated infrastructure in this option. This Software-as-a-Service (SaaS) model provides a hassle-free deployment experience as the publisher manages the infrastructure, maintenance, and updates. Organizations can benefit from the scalability and reliability of the publisher’s infrastructure while focusing on their core business operations.
- Single-AZ Deployment: This deployment option involves setting up the application within a single Availability Zone (AZ). It suits organizations with lower availability requirements and can be a cost-effective solution for smaller-scale deployments.
- Multi-AZ Deployment: This option involves deploying the application across multiple Availability Zones within a single region. It enhances the availability and fault tolerance of the application by distributing resources across different AZs. Multi-AZ deployment is ideal for organizations that require higher availability and resilience.
- Multi-Region Deployment: This deployment option involves setting up the application across multiple regions. It provides the highest availability and disaster recovery capabilities by distributing resources across different geographic locations. Multi-region deployment is suitable for organizations with global operations and stringent availability requirements.
By offering these deployment options, DataStudio ensures that organizations can choose the most suitable configuration to meet their needs and operational requirements.
