Teradata has launched Loom 2.4, an updated tool designed to reduce the time and effort needed to carry out big data analytics.
Teradata hopes that Loom 2.4 will enhance the way large amounts of unstructured data are managed in Apache Hadoop environments known as 'data lakes'.
The volume and lack of structure of information stored in data lakes makes it difficult to determine the quality, format, content, history or location of the data, according to Teradata.
This makes tracking updates and previous analyses practically impossible, and requires the process to be started from scratch each time.
Analyst house Gartner has warned that a lack of governance or oversight into the data pooled in these lakes poses a substantial risk for companies trying to use them for analytics.
One particular risk is that private and regulated data can be in a lake but with widespread access.
The latest version of Loom aims to solve these problems by tracking the lineage of data in the lakes, and integrating metadata so that information can be identified and maintained.
Scott Gnau, president at Teradata, described Loom 2.4 as a breakthrough in making it easier and faster to prepare data lakes for analysis.
"Instead of taking months, big data analytic projects can now advance in hours," he said.
Gnau also claimed that the automated features of Loom 2.4 will shrink the learning curve for data scientists trying to analyse data lakes.
The new version includes support for international data formats, such as dates and currency, and can be used to create data partitions in Apache Hive to speed up query performance.
Loom 2.4 will be made available by 31 March with "competitive subscription pricing", the firm said.
Teradata also announced that Loom Community Edition 2.3 is now available as a free download.
Teradata recently launched Connection Analytics as a way to bolster big data analytics.
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