What is Edge Management?
Cloudera Edge Management (CEM) manages, controls, and monitors data collection and processing at the edge with a low code authorship experience addressing data management challenges with streaming and IoT use cases.
It provides two categories of capabilities:
Edge Data Collection: MiNiFi is a lightweight edge agent that implements the core features of Apache NiFi, focusing on data collection and processing at the edge. The MiNiFi agents come in two flavors: MiNiFi Java agents for full capabilities of Apache NiFi and MiNiFi C++ for very low footprint agents
Edge Flow Management: Edge Flow Manager is an agent management hub that provides a low-code experience for designing, deploying, and monitoring edge flow applications on thousands of MiNiFi agents. It also acts as the single management and monitoring layer for all the MiNiFi agents deployed at the edge. EFM supports the entire edge flow lifecycle including authorship, deployment, and monitoring
These capabilities address IoT use cases such as predictive maintenance, fleet management, and asset tracking. The MiNiFi agents are also widely used in cybersecurity logs collection use cases to collect logs in real time across an infinite number of devices, servers, laptops, and so on, as well as a modern solution to collect logs from cloud-native applications running on Kubernetes.
Lower costs and reduce downtime with predictive maintenance.
Predictive Maintenance is a data-driven approach to analyze IoT and sensor data from connected equipment to effectively predict when and how an asset might fail, detect variances, understand warning signals, and quickly identify patterns that might indicate a potential breakdown. Cloudera DataFlow’s Edge Management capabilities modernize and simplify data ingestion from hundreds of connected assets to enhance predictive maintenance.
Capture real-time feeds from patient-monitoring devices to detect anomalies.
Biometric and telemetric devices are used in healthcare organizations to monitor post-surgery or high-risk patients. Ingesting sensor data from these devices about various patient vitals helps detect abnormalities or concerning patterns. Cloudera Edge Management helps capture patient-monitoring data and delivers them to stream-processing engines for insights.
Connect, integrate, and move massive volumes of data across hybrid and multi-cloud environments.
Traditional ETL processes are for use cases where data must move from one database to another. Modern enterprises transfer data from on-premises to cloud or cloud-to-cloud, moving petabytes of information in a matter of just hours.
Hundreds of prebuilt processors are available to connect with a range of data sources, devices, and protocols. The user interface allows you to build sophisticated data flow pipelines with drag-and-drop ease.
Understand the origin and attribution of data as it moves throughout the enterprise, empowering the governance team to explain how any data point is affected by any system. Data lineage information is generated for everything it does at a fine-grained level, even when records change before and after an event.
Ingest, capture, and deliver data in real-time from any streaming source, including clickstreams, social media, mobile, or IoT devices. Enable actionable insights by easily connecting, transforming, managing, and monitoring them using complex data flow applications with NiFI’s 450+ processors and a custom-build monitoring dashboard.
Handle any throughput by moving petabytes of data from one data center to another in just a few hours or move data from your on-premises environment to the cloud or vice versa. Enable a multi-cloud model with a cloud-vendor-agnostic approach to managing data.
Adopt a DevOps-style data flow development lifecycle with NiFi Registry to deliver your flow applications faster and deploy them easily from one environment to another. Enable your development team to version their data flows and set up promotion schemes across environments.
Enable edge management at scale with command, control, and monitoring of hundreds of thousands of agents with minimal footprint to collect, filter, and process data. Allow end-to-end machine learning algorithms at the edge with automated learning loops.
Review technical specifications, architecture, tutorials, and how-tos about Edge Management.