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Data science enthusiasts gather for 4th Colombo Big Data Meetup

20 Feb 2017 - {{hitsCtrl.values.hits}}      

From left: Panellists 99X Technology Software Architect and Data Science Specialist Samudra Kanankearachchi, Microsoft MVP and dinesQL Founder and Principal Architect Dinesh Priyankara and Microsoft MVP and VirtusaPolaris Senior Architect Dinesh Asanka who moderated the session

 

 

The fourth session of the Colombo Big Data Meetup was held at VirtusaPolaris recently, where industry professionals discussed the latest findings in big data tools and technologies.
99X Technology Software Architect and Data Science Specialist Samudra Kanankearachchi presented on the topic ‘Forecasting Seasonal Effects in Business Data (Time Series Modelling) Using Microsoft Azure Machine Learning Studio’. He discussed the extensive use of forecasting in business to make predictions about demand, capacity, budgets and revenue. Starting with an introduction to Time Series Modelling and walking through a few use cases, he then discussed the technical aspects of Time Series Modelling.
In the next session, Microsoft MVP and dinesQL Founder and Principal Architect Dinesh Priyankara discussed the topic ‘Processing data using Apache Hive’. He discussed how previously ignored data has suddenly become the most important part of the business and how it needs to be processed similar to how we process standard, relational data, which is called big data, unstructured data or semi-structured data. In addition, he discussed how initially it was a big challenge for engineers because processing data with unfamiliar languages was not easy but the introduction of Apache Hive, which provides a SQL-like interface, changed that. He also talked about Apache Hive and how it assists in processing Big Data.
The meetup was moderated by Microsoft MVP and VirtusaPolaris Senior Architect Dinesh Asanka. The Colombo Big Data Meetup, held every quarter, gives those interested in the field the opportunity to interact and discuss how the latest research and findings can influence real-life problems and thus create solutions.