Teradata on Monday revealed its plans to break down the borders between multi-system, multi-technology analytics environments. The efforts center around two initiatives -- Borderless Analytics and Teradata Everywhere -- that Teradata says will create a single analytic organism on both public and private clouds.
Teradata said it is creating a specialized team of data scientists, data engineers and software designers to tackle cloud-based analytics for the Internet of Things. The Global IoT Analytics unit will operate within Teradata Labs and will focus on building new cloud-based analytic services that could automate data movement and database management for IoT applications. For Teradata, the move reaffirms the company's focus on growing its data and analytics unit after it decided to sell off its marketing applications business last year. The restructure came after Teradata's primary business of data warehousing slowed as many enterprises moved to the cloud. "The smartest people at Teradata are laser focused on building the best technologies to power the Analytics of Things," said Oliver Ratzesberger, president of Teradata Labs.
It should surprise no one that the cloud is forcing the data warehouse to adapt and evolve. In August, I wrote about it an article entitled "What Should the Data Warehouse Become in the Cloud?," In this follow-up to that article, I want to look at Teradata, one of the biggest and most established players in the data warehouse landscape, and examine how much of my framework for the perfect cloud data warehouse it can implement. Teradata recently announced that its core platform, the Teradata Database, is now supported fully on the public cloud on Amazon Web Services (AWS) and soon also on Microsoft Azure. For a while it has been available on Teradata's managed cloud, on virtualized infrastructure or private cloud, and of course on-premises on an appliance.
The internet of things continually emits a torrent of data, which usually must be analyzed in terms of when it was created to be of full value. IoT data analysis often calls for specialized time series methods to be applied. But while they're well-known in areas such as signal processing, economics and weather forecasting, there is a gap in such expertise in many enterprises. Data warehouse pioneer Teradata is looking to fill in that sensor analytics gap with new capabilities for its Teradata Analytics Platform. In a new update, existing geospatial data analytics tools are enhanced with time series analytics capabilities tuned to deal with the types of problems that IoT data creates.
A majority of technology leaders at large organizations find themselves at a crossroads: they believe that the cloud is the best place to run enterprise-scale analytics, but almost all of them also agree that the march toward the cloud is moving slower than it should. This executive summary discusses the disparity between where global companies are--and where they want to be--when it comes to running analytics in the cloud, according to findings in a comprehensive new survey conducted by the market research firm Vanson Bourne, on behalf of Teradata.