How ELISA successfully migrated its DWH to Vertica in record time

Presented by

Elina Tuominen, Service Manager, Elisa

About this talk

Elisa is a leading telecommunications provider in Finland founded in 1882. For almost 140 years Elisa has been a trailblazer in the deployment of new technologies. In 2018 Elisa launched the world's first commercial 5G NR network in the Finnish city of Tampere and in the Estonian capital of Tallinn. After 20 years of data warehousing of different generations and vendors also faced with an end-of-life deadline on 30 June 2020, Elisa had to select a replacement technology that is easy to implement, cost-effective, and meet all performance and integration criteria. After a thorough selection process and benchmark, Elisa embarked on a very challenging migration project with the mission to deliver a fully operational new platform in less than seven months with no impact on business reporting and operations. Join this webcast to hear from Elina Tuominen, Service Mgr, as she describes how she managed to turn this around and achieve “the best IT project ever”.

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The Vertica Unified Analytics Platform is built to handle the most demanding analytic use cases and is trusted by thousands of leading data-driven enterprises around the world, including Etsy, Bank of America, Uber, and more. Based on a massively scalable architecture with a broad set of analytical functions spanning event and time series, pattern matching, geospatial, and built-in machine learning capability, Vertica enables data analytics teams to easily apply these powerful functions to large and demanding analytical workloads. Vertica unites the major public clouds and on-premises data centers, as needed, and integrates data in cloud object storage and HDFS without forcing any data movement. Available as a SaaS option, or as a customer-managed system, Vertica helps teams combine growing data siloes for a more complete view of available data. Vertica features separation of compute and storage, so teams can spin up storage and compute resources as needed, then spin down afterwards to reduce costs.