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Private and Open Data in Asia: A Regional Guide.
Basic programming experience in an object-oriented or functional language (the class will mostly be taught in Scala).
This technical class is designed as an introduction for engineers, data scientists, and analysts with less than a month or so experience with Spark.
All of the hands-on labs for class will be run on the Databricks platform in a browser. Please bring a laptop with updated versions of Chrome or Firefox (Internet Explorer and Safari are not supported). You do not need Spark, Scala or Python installed on your laptop. The operating system of your laptop does not matter.
The real power and value proposition of Apache Spark is in building a unified use case that combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing and visualizations. In class we will explore various Wikipedia datasets while applying the ideal programming paradigm for each analysis. The class will comprise of about 50% lecture and 50% hands on labs + demos.
Topics covered include:
Tutorial assisted by Paco Nathan and Reynold Xin
Sameer Farooqui is a client services engineer at Databricks, where he works with customers on Apache Spark deployments. Sameer works with the Hadoop ecosystem, Cassandra, Couchbase, and general NoSQL domain. Prior to Databricks, he worked as a freelance big data consultant and trainer globally and taught big data courses. Before that, Sameer was a systems architect at Hortonworks, an emerging data platforms consultant at Accenture R&D, and an enterprise consultant for Symantec/Veritas (specializing in VCS, VVR, and SF-HA).
Paco Nathan is known as a “player/coach” with core expertise in data science, natural language processing, machine learning, and cloud computing. He has 35+ years of experience in the tech industry, at companies ranging from Bell Labs to early-stage startups. His recent roles include director of the Learning Group at O’Reilly and director of community evangelism at Databricks and Apache Spark. Paco is the cochair of Rev conference and an advisor for Amplify Partners, Deep Learning Analytics, Recognai, and Primer. He was named one of the "top 30 people in big data and analytics" in 2015 by Innovation Enterprise.
Reynold Xin is a cofounder and chief architect at Databricks as well as an Apache Spark PMC member and release manager for Spark’s 2.0 release. Prior to Databricks, Reynold was pursuing a PhD at the UC Berkeley AMPLab, where he worked on large-scale data processing.
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