Importance of Real-Time Data for Networks

Real-time data is just a fad! "I don't see a need for analytics and BI to be real-time"...say every BI user. People said cars were a fad and the horse was just fine. People said email was a fad and postal mail was just fine. People say GenAI/LLMs are a fad and Google search is just fine People say real-time data is a fad and batch processing is just fine Lets be honest, most people aren't good at recognizing the potential of innovation and new technology until much later. Real-time, streaming or continuous data ingestion, processing and insights is not a fad, it's the new reality. According to Confluent's survey of 2,250 companies, 89% say investing in streaming is important and 44% say it's a top priority. Apache Flink is picking up steam. Confluent acquired Immerok's managed Flink showing a strategic investment in the full streaming ecosystem. Spark has been investing more into improving Structured Spark Streaming. Lots of great real-time/streaming databases are popping up to round out the real-time ecosystem. What does that mean to you? Know the terms: - Real-time: Data can be consumed as soon as it's produced - Near real-time: Data is available within a short time after it's produced - Streaming: Data is flowing event by event or in small batches - Micro-batching: Data is combined into smaller batches and processed continuously without external orchestration or scheduling. - Continuous: Data is moving all the time from producers to consumers. It maybe available in real-time or near real-time. It can be streamed or combined into small batches. Know the data stages: - Extraction: In large majority of cases, data is produced in real-time and can be extracted continuously - Ingestion: Continuously ingest and store the extracted data into your lake or warehouse for future processing - Processing: Prepare, transform and enhance data continuously as it streams from the source to the target. - Serving: Make data queryable as soon as it's been processed. Continuously update it as new data becomes available. Know your architecture: - Batch: Keep doing what you're doing with existing batch systems but make your data batches smaller and execute them more frequently - Streaming: Migrate all workloads to use a stream processing engine to continuously transform data before loading it your target serving layer (Snowflake, lakehouse, Clickhouse) - Continuous: Use a combined batch + streaming engine to unify the developer experience and speed up the availability of data for the use cases that demand it. In 2024, you will see more focus by vendors and users on understanding how to incorporate continuous/stream processing into their architecture. I'm hosting a webinar panel on Dec 13 (link in comments) to dive into all of this to help you wrap your heads around it and prepare for the conversations in 2024. #dataengineering #datastreaming

🚫 Disconnected Systems = Delayed Decisions = Missed Opportunities In too many enterprises, best-of-breed has become death-by-integration. But Why? 🤔 Each system from ERP, WMS, TMS, CRM are stitched together with brittle, point-to-point links. Every new supplier, customer, or carrier triggers another IT project. With every decision is delayed waiting for data to sync. Now add AI into that mix. ⚠️ AI + Siloed Data = Suboptimal Decisions… Just Faster Until your AI has near real-time access to both internal and external data, it's just accelerating outdated or incomplete decision-making. ✅ That’s where a multi-party, network-based Control Tower changes the game. A network acts as a System of Engagement over your existing Systems of Record, enabling: -Multi-Party MDM: Unified, authoritative data across your network -Permissioned Data Sharing: One connection per partner, instead of dozens -Cross-Legacy Workflow Orchestration: Order-to-cash, demand-to-fulfill, plan-to-produce, and all capabilities on a single platform. 📊 With a real-time, unified view of demand, capacity, inventory, and logistics, enterprises can: -Detect constraints before they cause disruption -Run what-if scenarios across the network -Launch promotions or new products without blind spots -Optimize decisions as AI executes actions based on complete, near real-time data It’s not about choosing between “best-of-breed” or “monolithic suite.” It’s about connecting once, collaborating always—and empowering AI with the full picture. #SupplyChain #AI #ControlTower #DigitalTransformation #MDM #ERP #SIOP #Logistics

Solving Network Complexity with Graph Neural Networks Network engineers often struggle to troubleshoot today’s complex systems, overwhelmed by fragmented logs, metrics, and traces. Missing a critical data point can delay resolution, while the sheer volume of data leads to alert fatigue. At NetAI, we leverage Graph Neural Networks (GNNs) to transform observability by connecting and processing massive, disparate datasets in real time. This enables faster, more accurate insights while reducing manual effort and costs. Modern networks are more dynamic and interconnected than ever, with thousands or millions of devices generating telemetry. This complexity presents key challenges: Fragmented Data: Logs, metrics, and traces are siloed. Alert Fatigue: The volume of alarms overwhelms engineers. Manual Root Cause Analysis: Identifying issues requires stitching data together. Skyrocketing Costs: Observability has become a major expense, often second only to infrastructure. Traditional observability tools rely on static representations and manual correlation, making them inadequate for today’s fast-evolving systems. NetAI’s GNN-Powered Observability NetAI addresses these challenges by modeling network behaviors and relationships through GNNs. This approach transforms raw data into actionable insights: Real-Time Data Integration: Logs, metrics, and traces are connected into a unified graph. Behavior Modeling: GNNs capture interactions and dependencies across devices, services, and applications. Accurate Correlation: Our AI automatically identifies root causes by linking anomalies across the network. Actionable Insights: Engineers receive clear recommendations to resolve issues quickly. The Next Wave: AI Workloads AI systems add another layer of complexity, requiring real-time monitoring of inference requests, impacts, and interactions. NetAI’s dynamic, scalable GNN-based approach is designed to handle these growing demands, ensuring reliability as systems evolve. Efficiency and Cost Reduction NetAI improves observability efficiency while reducing costs: Noise Reduction: AI filters irrelevant data, cutting noise by 90%. Health-Based Aggregation: Healthy data is aggregated, while anomalies remain granular. Optimized Storage: Efficient data lakes store raw data for troubleshooting and backfilling. Seamless Integration NetAI integrates smoothly with existing environments, providing immediate benefits like faster root cause analysis, reduced downtime, and lower observability costs. The Future of Observability Static, fragmented observability tools can’t keep up with modern networks. By leveraging GNNs to model behaviors and relationships, NetAI delivers actionable insights, transforming how networks are monitored and managed. The future of observability isn’t just about more data—it’s about understanding it. With NetAI, that future is here. Please contact my cofounder Mike to learn more mike@netai.ai

MSPs and MSSPs aren’t just targets, they’re entry points. Compromising a single provider gives attackers access to multiple businesses all at once. Here is why network flow intelligence is the missing piece in modern cybersecurity: First, let's set up the problem. Many traditional service providers rely on some form of a config DB to store all the known facts about a client’s environment. To be accurate and effective at any given time requires having a regular and consistent practice of querying the environment that’s being protected, and updating the config DB. But any lag between an actual change and the updating of the database creates a window of time for cybercriminals to exploit or work around the protections that are in place. Better solutions that many providers are adopting are highly automated tools like IaC (Infrastructure As Code) solutions, or specifically in the network space, NCM’s (Network Config Managers) to reduce errors and to scale up while leveraging scarce human resources. But even with really good implementations of these more modern tools, we are often left with the question, “How good is the resulting stack of configured solutions at protecting my environment?” At Intrusion, we believe the best way to answer the “How Good is it….?” question is to examine network flow at a very granular level to understand the good, the bad, and the ugly with respect to what traffic is actually traversing the network. For example, we’re interested in knowing who's talking to whom at a packet by packet level, and then making – or suggesting – a judgment call if the communication is suspicious. Network flow intelligence applied in real time is crucial to understand and protect the health of an IT environment and to see whether everything in that environment is behaving normally or not. It’s not unlike a blood test: the bloodstream is one of the best places to get a full picture of a person’s health. Here’s the path to greater protection: 1. Create Visibility Do you really know what's going on? Are the tools you’re using really showing you everything you need to be concerned about? There’s a good chance they aren’t able to see some of the more dangerous things going on in the network. 2. Evaluate your Risk tolerance What's your risk tolerance? Can you afford to take a few hits because there’s not much at stake? If so, you can be a little more open and free about the flow of information. If not, you need to do the necessary to improve your risk level. If you're not looking at that network traffic flow, it's highly likely that you're missing the signals – or blood markers – that something bad is happening. 3. Take Action Our suggestion? Schedule a cybersecurity checkup. Cybercriminals work like cancer in your system. The threat is growing, and no one is immune.

You don’t need to wait to detect a network threat. Here's how to catch malicious traffic in real time—without slowing your business down: Most companies only find out about breaches after damage is done. By then, it’s too late. Data is gone. Customers are angry. Trust is broken. I’ve seen it firsthand—businesses crushed by attacks they never saw coming. That’s why I built a real-time monitoring tool that tracks what goes in and out of your network, 24/7. It spots suspicious activity before it becomes a full-blown breach. You don’t need to pause operations or overwork your security team. Here’s how it helps: - Detects threats as they happen, not days later - Reduces downtime during an attack - Keeps systems running while protecting sensitive data You avoid the chaos of reacting too late. Try this: run a test on your network and watch what real-time traffic shows you. It’s eye-opening. Peace of mind shouldn’t come after the fact. It starts with seeing the threat before it spreads.