In the era of Industry 4.0, data is the key to unlocking the potential of digital transformation for industrial operations. However, data integration and management can be challenging, especially when dealing with different types of data from IT and OT systems. In this article, we deep dive into the similarities and differences between Rockwell’s FactoryTalk DataMosaix and DeepIQ’s DataStudio, both of which aim to simplify and accelerate data-driven decision-making for industrial companiesView
Operational technology (OT) data refers to the data generated by sensors and other devices used in industrial settings to monitor and control physical processes. Supervisory control and data acquisition (SCADA) systems, data historians, and programmable logic controllers (PLCs) are examples of technologies that generate OT data. SCADA systems are used to monitor and control industrial processes, while data historians are used to collect and store historical data from industrial systemsView
Ignition SCADA by Inductive Automation is a comprehensive, industrial-grade software platform for building and deploying powerful HMI, SCADA, and IIoT solutions.
In our latest whitepaper, we explain how DeepIQ enables you to establish secure and scalable data pipelines between different cloud data lake platforms and your Ignition SCADA without writing a single line of code.View
Building Deep Learning models on large datasets can increase your cloud costs significantly. Find out how DeepIQ can help you cut down these costs by up to six times on your favorite cloud platform in our latest whitepaperView
Valaris IT chose to leverage DeepIQ software to ingest all business system data into their data lake and deliver integration reports using this capability.View
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 into how to tackle these challenges head-on.Part 1 Part 2 Part 3
In this whitepaper, we first explain the challenges in geospatial analytics. Then, we explain how DeepIQ addresses these challenges by leveraging your execution environment for scalability and customizability.View
In this whitepaper, we will explain the challenges in Master Data Management. We will dive into the traditional approaches to managing master data and then show how DataStudio solves these challenges.View
DataStudio's built-in connectors for industry-standard data historians (e.g., Pi System from OSIsoft) enable seamless fetching of time series data, data cleansing, and data preparation for ML model-building exercises and real-time execution of validated models.View
This latest whitepaper will explain how you can use DeepIQ's patent-pending hybrid Knowledge-AI approach to building high-quality, interpretive predictive maintenance models, even without significant failure data.View
With DeepIQ's DataStudio, moving your IoT data into AWS from your diverse
industrial technology and network landscape is a simple task with no code
In our latest whitepaper, we will use a real-world use case to show you how to build a production-grade data pipeline with real-time streaming data from your industrial systems into your AWS Timestream database in less than 60 minutes.
This whitepaper explains how DeepIQ's DataStudio enables cross-functional analytics by simplifying the integration of your SAP data with the rest of your data ecosystem.View
Snowflake has several inbuilt capabilities that are prerequisites to a good time series database. These include infinite scalability, cost-effective storage, and fast response times for point reads and analytical queries. With DeepIQ's DataStudio, you can realize the full potential of your Snowflake Data Warehouse by ingesting your operational and IT data at scale and developing innovative analytics that maximizes the value of this data.View