云计算 阅读材料 22 Message Queues and Stream Processing
In this module, we will look at message queues and stream processing. With the rise of internet services and the availability of continuous streams of real-time data, the challenge
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In this module, we will look at message queues and stream processing. With the rise of internet services and the availability of continuous streams of real-time data, the challenge
MapReduce is a data-parallel framework, originally created by Google, for processing big-data applications on large suites of computers. The system attempts to decrease interproce
We now move on to discuss another distributed analytics engine that is growing in popularity and adoption.
Machine-learning and data-mining (MLDM) problems are growing exponentially in scale today. Interest in analytics engines that can execute MLDM algorithms efficiently on distributed
Developing cloud programs (i.e., designing and implementing software systems that successfully exploit the capabilities of massively distributed computational resources) presents f
In this module, we discuss a few popular NoSQL data stores: Apache HBase, MongoDB, Apache Cassandra and Amazon's DynamoDB.
Now that we have covered specific file systems and database implementations, we turn our focus to object storage. Object storage is an important facet in cloud storage, and is one
In this module, we discuss two distributed file systems: the Hadoop Distributed File System (HDFS) and Ceph FS.
We now move on to the next part of the course, namely Cloud Storage. Storage is an important aspect of cloud computing and has seen a lot of innovation in the last decade or so. In
A typical data center architecture consists of many loosely integrated components including pools of servers (CPU and OS), storage arrays, a hierarchical physical network to interc