Microsoft office 365 2019 3x computers update#
Microsoft office 365 2019 3x computers software#

Microsoft office 365 2019 3x computers software#

What software layers are used to handle Big Data? What does it take to access such hardware? What are the properties of these hardware components? What components are hardware infrastructures made up of? these factors guided the design of current Big Data infratructures access pattern, hardware latency/bandwidth

insight in the main factors that underlie algorithm performance Solving such tasks(data management tasks where problem size/complexity requires using a cluster) requires The goal of the course is to gain insight into and experience in using hardware infrastructures and software technologies for analyzing ‘big data’. Services to remember are =S3= block storage (infinitely scaling storage - presumably implemented by spreading storage with replication over the hundreds of thousands of machines Amazon owns), =EBFS= filesystems which are virtual disks that store their data remotely on S3 but do a lot of caching to avoid network traffic, and the =EC2= service that allows to power up virtual machines in the cloud (and some of the options to choose from in terms of CPU, RAM, and local disks aka 'ephimeral storage' which are empty when you start-up). The last part of the lecture describes Amazon Web Services (AWS) in some detail to prepare you for the practicum. Amazon Redshift) and =SaaS=: Software-as-a-Service (application for rent, e.g.

Amazon EC2), =PaaS=: Platform as-a-service (database system for rent, e.g. In cloud computing, we further distinguish between =IaaS=: Infrastructure-as-a-Service (virtual machines for rent, e.g.

Super computing is out of scope for this module. In the practicum where we use =Hadoop= (cluster computing) on =Amazon Web Services= (cloud computing) we hence combine (2) and (3).

Microsoft office 365 2019 3x computers update#

We explain =Eric Brewers CAP theorem=: a global software system cannot achieve all three of =Consistency= (reads always reflect the latest updates), =Availability= (the system is always up), =Partitionability= (the system is resistant against loss of communication between datacenters), and describe concepts as replication and sharding and their consequences in terms of read speed, update speed and consistency.Īs we work on large computer infrastructures, we distinguish between three related areas: (1) =Super Computing=, where performance is king and programmability a side-issue, (2) =Cluster Computing=, which is about quickly getting things done on large clusters of unreliable machines, and (3) =Cloud Computing=, where computation is performed in large clusters operated by third party, sold as a commodity service to users based on their actual use, seamlessly allowing to get more or less of it based on their needs (elasticity). the distribution of amount of twitter followers has a power law distribution. Power Law distributions are typical in social networks, e.g. We also discuss =Power Laws= in many Big Data problems: the mass of data is in the long tail, the long tail cannot be ignored as it may represent the majority of all datapoints. In this lecture we first define Big Data in terms of data management problems with the =three V='s: (i) Volume: the data is large, (2) Variety: the data is often not clean and tabular, but messy (text, even images), (3) Velocity: new data keeps arriving continuously.