Moshe Shadmon
CEO, AnyLog
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Moshe Shadmon, CEO at Anylog. AnyLog’s Virtual Edge Data Network is a Plug & Play software, deployed at the edge, allowing real-time insight without centralizing the data. AnyLog enables deployment of applications and AI at the distributed edge. Prior to AnyLog, Moshe was the CEO of ScaleDB, a Time-Series Database. Prior to ScaleDB Moshe was the founder and CEO of multiple companies involved in big data projects.
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Integrating EdgeLak with OpenTelemetry: Enhancing Edge Data Management and Observability at the Edge
In this session, we will explore how integrating EdgeLake, an LF Edge project, with OpenTelemetry transforms edge data management and observability. EdgeLake offers a plug-and-play solution for processing data directly at the edge, removing the need for centralized cloud systems. This approach reduces latency, enhances security, and optimizes costs.
OpenTelemetry, an open-source observability framework, enables monitoring, tracing, and metrics collection across distributed systems. When integrated with EdgeLake, it enhances visibility into edge data flows, helping organizations gain real-time insights and make data-driven decisions.
This session will demonstrate how decentralized edge solutions, combined with OpenTelemetry, provide end-to-end visibility, better fault tolerance, and optimized performance for edge applications. By eliminating cloud dependencies, businesses can scale, improve reliability, and ensure data autonomy.
Attendees will learn how to deploy EdgeLake with OpenTelemetry in industrial IoT, smart city projects, Oil & Gas and more, enhancing data observability, system performance insights, and proactive management of edge infrastructure.
From Data Transfer Networks (DTN) to Data Service Networks (DSN)
When edge data is needed, networks provide the pipes to transfer edge data to the cloud, and the cloud services the data to the applications.
This approach is problematic. For example, KubeArmor analyzes telemetry data to understand application behavior for container/node forensics. With thousands of nodes deployed (using Open Horizon), sending event streams to a centralized node is not a viable option.
To address the need, Open Horizon was extended by AnyLog:
1) By services to store the KubeArmor data on the local nodes.
2) By a network protocol that is able to query the distributed data.
These functionalities allow KubeArmor to extract real-time insight without centralizing the data.
This approach transforms the network to be a data service provider: With Open Horizon extended functionalities, the network delivers the distributed edge data to the applications (by satisfying SQL queries) without centralizing the data.
This talk details this approach, including a live demo of distributed edge data that is serviced (using a network protocol) as a unified collection of data to edge and cloud applications.
From Data Transfer Networks (DTN) to Data Service Networks (DSN)
Traditional edge data processing relies on transferring massive data volumes to the cloud, causing high latency and costs. EdgeLake, a new LF Edge project, enables Data Service Networks (DSNs), bringing data processing to the edge. This reduces cloud dependency, lowers latency, and enhances data sovereignty. In this talk, we’ll explore how EdgeLake transformed the city of Sabetha, Kansas, into a smart city, showcasing a live demo of distributed edge data serviced directly at the edge, eliminating the need for cloud reliance.
Key Points:
1. Challenges with Traditional Data Processing – High latency, costs, and scalability issues with cloud-centric approaches.
2. Introduction to EdgeLake – DSNs shift focus to edge-based data services, reducing cloud dependency.
3. Real-World Use Case – Sabetha, Kansas – Transformation into a smart city using EdgeLake.
4. Live Demo – Showcasing EdgeLake’s capability to process and service distributed edge data locally.
5. Benefits of DSNs – Reduced cloud costs, real-time data access, and enhanced data sovereignty.
EdgeLake empowers edge-first data services, driving efficient, low-latency data processing directly at the edge.
EdgeLake-FL: An Automated Federated Learning Platform for the Edge
Edge AI today relies on centralizing data from edge devices to the cloud, but this is impractical due to costs and privacy constraints. Federated Learning (FL) is a viable alternative: edge nodes collaboratively train a ML model without transferring or exposing proprietary data. Instead, only model weights are shared, allowing each entity to develop a model that outperforms what it could train independently. Despite its potential, FL is largely academic due to the complexity of integrating expertise across the technology stack. Additionally, decentralized data can be heterogeneous, requiring non-generalizable, application-specific solutions. EdgeLake-FL is a hardware-agnostic framework leveraging EdgeLake, an LF Edge project, to automate the continuous learning FL workflow. With EdgeLake as the data management layer, decentralized data appears centralized and data heterogeneity is resolved. Using EdgeLake-FL, an ML engineer publishes a training application, and Edge nodes with relevant data autonomously train, share, and aggregate models. Each node can then leverage the aggregated models for inference directly at the Edge. In this talk, I will demo EdgeLake-FL in a real use case.
EdgeLake: Extending the Cloud to the Edge – An LF Edge Project
As data volumes grow and real-time processing becomes essential, traditional cloud architectures face limitations in cost, latency, and security. The traditional approach moves all edge data to where the queries are executed—in the cloud—leading to inefficiencies and high costs. EdgeLake (https://lfedge.org/projects/edgelake/), an LF Edge project, takes the opposite approach by bringing queries to the source data at the edge, enabling decentralized data management and local AI/ML processing.
In this talk, we’ll explore how EdgeLake eliminates cloud dependencies, optimizes data infrastructure, and reduces operational costs while ensuring real-time decision-making at the edge. We’ll discuss key use cases (and show a live demo) across industrial automation, smart cities, energy, and telecom, demonstrating how organizations can leverage EdgeLake to unlock the full potential of edge computing.
Join us to learn how EdgeLake is reshaping the future of distributed data architectures and making edge intelligence more accessible.
EdgeLake: A New Data Layer for Edge Analytics and AI
As organizations shift from cloud-first to edge-first strategies, traditional data tools struggle to handle the demands of real-time, distributed data.
EdgeLake — now an open-source project under the Linux Foundation — offers a powerful alternative: a decentralized data platform that simplifies how data is ingested, stored, queried, and managed directly at the edge.
In this session, we’ll introduce EdgeLake as a foundational data tool for edge computing, covering:
- How EdgeLake eliminates the need for centralized cloud pipelines
- How it integrates with analytics and AI tools (Grafana, PowerBI, TensorFlow, etc.)
- How it automates metadata, schema evolution, and secure cross-node queries
- How it enables pushdown processing and federated analytics with minimal overhead
- How it enables AI at the edge
- Real-world examples from smart cities, industrial IoT, and telecom
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