The analytics company has created a new Global IoT Analytics unit within Teradata Labs and also introduced a Hybrid Cloud environment to help customers shift analytic workloads among a mix of on-premises, managed cloud and public cloud systems.
At the Teradata Universe 2016 event in Hamburg, Germany today, Teradata took the wraps off a new unit that it likens to the Navy SEALs of Internet of Things (IoT) analytics.
The new special ops team of data scientists, data engineers and software designers — dubbed the Global IoT Analytics unit within Teradata Labs — are tasked with building new, cloud-based analytics solutions and services to simplify advanced analytics, data movement and database management for the IoT.
"The smartest people at Teradata are laser-focused on building the best technologies to power the Analytics of Things," Oliver Ratzesberger, president of Teradata Labs, said in a statement today. "With this announcement, we are making it easier for our customers to move sensor data around, optimize data management systems to deal with the massive volumes of data and run real-time, advanced analytics against streams of IoT data. We’re giving our customers powerful tools and technologies to analyze IoT data for new insights, applications and use cases."
The IoT Analytics unit helped to develop the Teradata Aster Scoring software developer’s kit (SDK) in Teradata’s Aster Analytics 6.21 offering. The Aster Scoring SDK operates in the runtime environment on analytic models built in the Aster database and allows you to build complex analytic models that Teradata says can be deployed into virtually any IoT edge servers, public clouds and the data center.
The team has also been working on extending IoT capabilities for Teradata Listener — the company’s Apache Kafka-based offering for following multiple streams of sensor and IoT data and propagating the data into multiple platforms in an analytical ecosystem. The new connectors developed by the special ops team make it easier to acquire and distribute streaming sensor data for analysis.
The next challenge for the team is applying machine learning and advanced analytics techniques to system administration and DevOps tasks. By applying machine learning to Teradata systems, they aim to solve complex performance and workload congestion problems. Some of the new solutions will include:
- Early warning detection using predictive analytics to detect and correct issues with machines and devices sooner
- Continuous monitoring of assets to enable new revenue opportunities and pricing strategies based on power-by-the-hour and pay-per-use models instead of purchases
- Real-time monitoring and analysis of physical assets, allowing companies to understand and act on a variety of real-time insights, including security alerts, energy and fuel usage, idle time, faulty parts, geo-positioning and more
Teradata says the new technologies and services will be available starting in the second quarter of this year.
A hybrid approach to analytics
The company also today announced Teradata Hybrid Cloud, designed to help its customers transparently shift analytics workloads across a mix of on-premises, managed cloud and public cloud systems. The environment provides orchestration to deliver automatic synchronization, optimized query routing and end-to-end management across the ecosystem.
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