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Link to Best Practices for Deploying and Scaling Industrial AI

Best Practices for Deploying and Scaling Industrial AI

Artificial Intelligence (AI) is transforming industrial operations, helping organizations optimize workflows, reduce downtime, and enhance productivity. Different industry verticals leverage AI in unique ways.

Link to The Center for Adaptive Edge Intelligence

The Center for Adaptive Edge Intelligence

Adaptive edge intelligence brings real-time decision-making to the point of data creation, whether from sensors, machines, or cameras.

Link to The Value of Vehicle Electrification

The Value of Vehicle Electrification

Electric vehicles (EVs) present automakers with many design, engineering, and manufactu ring challenges.

Link to Accelerating Manufacturing Digital Transformation with Industrial Connectivity and IoT

Accelerating Manufacturing Digital Transformation with Industrial Connectivity and IoT

Digital transformation is empowering industrial organizations to deliver sustainable innovation, disruption-proof products and services, and continuous operational improvement.

Link to Smart Manufacturing for Automotive

Smart Manufacturing for Automotive

Leading a transportation revolution in autonomous, electric, shared mobility and connectivity with the next generation of design and development tools.

Link to Center for Data Pipeline Automation

Center for Data Pipeline Automation

As businesses become data-driven and rely more heavily on analytics to operate, getting high-quality, trusted data to the right data user at the right time is essential.

Link to Center for Automated Integration

Center for Automated Integration

The goal of automated integration is to enable applications and systems that were built separately to easily share data and work together, resulting in new capabilities and efficiencies that cut costs, uncover insights, and much more.

Link to Continuous Intelligence: Insights

Continuous Intelligence: Insights

Digital transformation requires continuous intelligence (CI). Today's digital businesses are leveraging this new category of software which includes real-time analytics and insights from a single, cloud-native platform across multiple use cases to speed decision-making, and drive world-class customer experiences.
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7 Essential Elements in a Real-Time Streaming Analytics Platform

7 Essential Elements in a Real-Time Streaming Analytics Platform

streaming analytics

What factors are crucial for a streaming analytics platform?

Written By

RTInsights Team

Feb 18, 2016

3 minute read

Sources of real-time data are exploding. For starters, there’s the IoT: Gartner estimates there will be 25 billion things connected to the Internet by 2025, and that includes everything from industrial machinery to mobile phones, smart thermostats, and wearable fitness trackers. There’s also growing amounts of data from clickstreams and social media, as well as e-commerce. Download report.

To get a handle on such data, businesses often turn to streaming analytics software. But these platforms face several challenges, including processing massive amounts of data, scaling, integration with existing IT infrastructure, and data visualization.

In this white paper, you will learn what to look for in a streaming analytics solution, including:

Open-source vs. proprietary frameworks.
How to ensure integration with a Big Data stack,
Considerations for scaling out, data visualization, and application development.

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Want more? Check out our most-read content:

E-Book: How to Move to a Fast Data Architecture
IoT Architectures for Edge Analytics
Operational Analytics: Five Tips for Better Decisions

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Leveraging Connectivity and Predictive Analytics to Proactively Service Products in the Field (Special Report)

Leveraging Connectivity and Predictive Analytics to Proactively Service Products in the Field (Special Report)

Companies that develop or support products in the field need to transition from reactive to predictive maintenance and service. Such a transition is possible using ubiquitous connectivity and predictive analytics on machine data. Here’s how.

RTInsights Team

May 17, 2021

The Four Key Benefits of Edge Computing (eBook)

The Four Key Benefits of Edge Computing (eBook)

Although edge architectures differ fundamentally from conventional cloud and on-premises environments, edge isn’t an alternative to traditional infrastructure strategies as much as it is an augmentation of them.

RTInsights Team

Apr 27, 2021

The Power of Personalization: Driving Digital Banking Success (White Paper)

The Power of Personalization: Driving Digital Banking Success (White Paper)

The relationships banks have with their customers can last for decades, but too many banks fail to use the data they have to provide personalized digital banking services.

RTInsights Team

Mar 10, 2021

Modernizing Your Decision Automation Strategy

Decision Automation

Modernizing Your Decision Automation Strategy

Every transaction, order, customer interaction, and internal operation depends on decisions. At a time when businesses are under pressure to improve efficiencies, cut costs, and be more responsive, automating decisions becomes essential. While many businesses have picked off the low-hanging fruit when automating, most need to add more. Specifically, businesses want to automate complex decisions, involving multiple steps, multiple data sources and many rules.

RTInsights Team

Mar 2, 2021

Featured Resources from Cloud Data Insights

Real-time Analytics News for the Week Ending May 30

Real-time Analytics News for the Week Ending May 30

In this week’s real-time analytics news: The Linux Foundation announced the launch of the DNS-AID project, an open-source project enabling AI agents to discover and communicate with one another in a standard way.

Salvatore Salamone

May 31, 2026

The Data Integrity Blind Spot in Real-Time AI Systems

The Data Integrity Blind Spot in Real-Time AI Systems

The next phase of operational AI will likely focus less on analytics speed and more on measurement fidelity. Because in real-time systems, intelligence is only as reliable as the observations behind it.

Aity Ritesh Raj

May 30, 2026

You Don’t Own Your Observability Data. And That’s About to Kill Your AI Strategy.

Data management

You Don’t Own Your Observability Data. And That’s About to Kill Your AI Strategy.

As AI platforms release new capabilities on a weekly cycle, the enterprises locked into vendor-specific collection mechanisms face brutal switching costs every time something better emerges. Those standardized on OpenTelemetry change a routing rule and can benefit from huge time savings.

May 29, 2026

The Four Core Principles of Controlling the AI Agents You Can’t See

Artificial intelligence technologies

The Four Core Principles of Controlling the AI Agents You Can’t See

Identity is the prerequisite for controlling, monitoring, and holding agents accountable. With most organizations lacking full visibility into the agents operating across their environments, identity must be in place before those agents are granted access to data or the ability to act.

May 28, 2026

Analysis and market insights on real-time analytics including Big Data, the IoT, and cognitive computing. Business use cases and technologies are discussed.

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