Met Office selects 2ndQuadrant to help with data migration to open source

By Sooraj Shah
28 May 2014 View Comments
Met Office building

The Met Office has selected PostgreSQL specialists 2ndQuadrant to provide training, support and consultancy as the weather service bids to shift from proprietary database solutions that require a licence fee to other alternatives.

The selection of 2ndQuadrant comes after two pilot projects went into production in April when the Met Office's locations management database (Strabo) and LIDAR (light detector) data capture system were implemented again into object-relational database management system PostgreSQL and open source software program PostGIS.

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"Making data accessible and available is at the forefront of our ICT strategy so, following some refreshingly comprehensive training delivered by 2ndQuadrant experts deeply immersed in the PostgreSQL community, we trust them to help us change the software which stores our vast quantities of data," said James Tomkins, data services portfolio technical lead at the Met Office.

"This is a titanic task which requires considerable investment in terms of time and training but, in the long run, simplifying our databases in this way will be cost effective."

2ndQuadrant is already training many of the Met Office's staff in PostgreSQL, covering aspects such as back-up and recovery, high availability, data replication and geospatial maturity.

The company has renewed its tender on the government procurement portal, G-Cloud 5, and said it is only one of two companies that can provide PostgreSQL support, training and consultancy to the public sector through this framework.

Earlier this year, at Computing's Big Data Summit 2014, Met Office CIO Charles Ewen explained that traditional database and business intelligence tools and technologies were inadequate for conducting big data analysis at the organisation because they cannot easily handle the "dimensions" of time and uncertainty.

However, he added that companies ought to have an open-minded "mixed economy approach" to big data that embraces mainframe computers, if necessary, as well as traditional relational database technology, where appropriate.

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