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Google Could Platform can be used for following purposes: Compute: https://cloud.google.com/products/com... Storage and Databases: https://cloud.google.com/products/sto... Networking: https://cloud.google.com/products/net... Big Data: https://cloud.google.com/products/big... Data Transfer: https://cloud.google.com/products/dat... API Platform & Ecosystems: https://cloud.google.com/products/#ap... Internet of Things: https://cloud.google.com/solutions/iot/ Cloud AI: https://cloud.google.com/products/mac... Management Tools: https://cloud.google.com/products/man... Productivity Tools: https://cloud.google.com/products/#pr... Developer Tools: https://cloud.google.com/products/tools/ Identity & Security: https://cloud.google.com/products/#id... Professional Services: https://cloud.google.com/products/#pr... In compare with MapReduce, on MapReduce you have to take your data and you need to shard it, or split it, across all of the compute nodes, which means the size of your datasets and the size of your compute nodes just because you need to store your data there. Or if you need to process some data you can process it on the computer nodes that already have that data. Google Cloud Engine do not working as MapReduce anymore. In this video I demonstrate how to create a Compute Engine Instance in very simple way with only basic explanation for totally beginners in Google Cloud Platform (web user interface). Also I show how to install git software on Google Cloud Platform Instance via terminal. Hoping you enjoyed. Subscribe me if you want to get more useful new videos related with Data Analysis, Data Science, Machine learning and similar stuff. You can contact me on LinkedIn: / bielinskas