Field Equipment Monitoring and Analytics
Hazelcast Jet Enables Real-time Monitoring of Sensors and Processing of Streaming Data from Field Equipment
Industries such as Oil & Gas use sensor data to automate decision making. Events reported from oil rig sensors are evaluated in the field by Jet so that the settings of the rig are adjusted in real time, i.e., sensor data on torque and depth of the drill are combined in Jet to indicate bottom of the oil reservoir. These fast data insights computed from complex events are the new frontier where companies are trying to unleash the power of big data and analytics to to make operations less labour intensive and more efficient. As the link between the oil rig site and the data center is usually limited, some processing has to be done in the field. This involves operating a processing infrastructure in the limited confines of the field locality. The biggest challenges are:
- Keeping latency low and consistent at scale while system operates turning insights into decisions that are executed in a closed loop system.
- Simplifying deployment and maintenance of system in remote locations with limited bandwidth.
- Sticking to standards that extend system viability and maintenance lowering TCO; there are many nascent protocols and standards in the emerging IoT industry.
Hazelcast Jet® is ideal for applications that require near real-time insights. It’s deployment simplicity makes it a optimal choice for both remote field and data center applications.
Why Most Processing Solutions Fall Short…
Why Hazelcast Jet is Ideal…
|Traditional data processing frameworks don’t allow direct embedding into an application or independent system, forcing users to install them as a secondary standalone server complicating the overall deployment of the total solution.||Jet can be used as an embedded library therefore avoiding the need for a separate server tier . Also, it doesn’t have any dependencies, so no further infrastructure necessary to run a Jet cluster.
Jet allows users to build self-contained applications.
|Field data processing spans from sensors to on-rig processing, and from on-site processing to processing within the data center, involving multiple execution environments and hardware architectures.
Traditional tools require different runtimes (for single-node, multi-node, different architectures), resulting in higher total system TCO.
|Jet is a lightweight tool with a small memory footprint. There is just one distribution to be used among all deployments.
Running on a JVM, Jet can be used on a variety of hardware platforms.
|Traditional complex event processing solutions are built for single machine/server deployments and cannot be scaled out easily.||Jet can be scaled out easily, as it’s built on top of a massively parallel, distributed computing core (Hazelcast IMDG®). It’s designed to scale out.|
|As shown in this stream processing benchmark, most processing solutions experience growing latencies as the workload throughput increases.||Jet is designed for low latency under the most grueling workloads with latency remaining relatively flat even up to 5,000,000 aggregations per second.|
Customer Success Story
A leading oil & gas system integrator, specializing in the acquisition, persistence, secure transportation and dissemination of high frequency sensor data, was developing a custom application for a major US oil company and needed a solution that was powerful enough to process streaming big data from oil rig sensors, but lightweight enough it could be deployed across the rough physical landscape typical of oil drilling locations.
The application gathers data from various devices and sensors throughout the oil exploration, drilling, and lifting process. Tens of thousands of events per second are gathered from oil rigs and various points in this process. The events are correlated and analyzed in real-time allowing actions to be taken for mitigating risks or maximizing opportunities. Application high-availability and performance are required for early detection of issues to avoid costly production losses and optimize the productivity of wells.
Hazelcast Jet is used as the processing backbone of the application. The application makes use of a pluggable Jet API to connect Jet to multiple sensor data streams with varying format and frequency. After joined and filtered, the sliding and session windows are used to compute data insights that are turned into decisions. Hazelcast IMDG, which is embedded in Jet, is the operational data store, making the deployment easily scaled. This same Jet-based application is used in bare metal field installations as well as in the AWS cloud.