With the popularization of cloud computing, more and more websites and applications on IDC begin to be on the cloud. At the same time, for us IT practitioners, we are also facing the challenge of accelerating the learning of cloud computing and not being eliminated by new technologies. So let's talk about how to learn cloud computing faster!
Cloud computing can be divided into two categories: broad sense and narrow sense
In a narrow sense, cloud computing refers to the delivery and use mode of IT infrastructure. It refers to obtaining the required resources through network demand and expansion; in a broad sense, cloud computing refers to the use and delivery mode of services, which refers to obtaining corresponding services through the network in a demand and expansion manner. This kind of service can be IT, but software, can make the Internet, can also be any other service, it has the unique functions of ultra-large scale, virtualization, reliability and security.
Cloud computing learning route 1. Big data foundation enhancement
This cloud computing learning route focuses on the basics of Linux operation, Shell programming, Zookeeper cluster and Hadoop cluster environment preparation. It can help students who have no Linux foundation or a weak Linux foundation to achieve proficiency in using Linux, proficient in installing software on Linux, and familiar with the load Cluster-related concepts such as balance and high reliability, build a high-concurrency and high-reliability Internet service architecture, and make sufficient preparations for in-depth learning of big data content.
Cloud computing learning route 2: Big data Hadoop offline distributed system
Big data Hadoop offline distributed system
Cloud computing learning route three, big data NoSQL, Kafka and ELK technology combat
Master the characteristics and application scenarios of NoSQL databases; master the application scenarios and core principles of Hbase; master the strategies of Hbase's RowKey design; master the performance optimization methods commonly used by Hbase; master Kafka, a tool for distributed data publishing and subscription; master the use of Kafka tools And performance optimization; master the application scenarios of ELK technology stack (end-to-end); master Logstash data extraction, cleaning, ElasticSearch distributed retrieval, Kibana data display applications.
Cloud computing learning route four, big data Spark memory computing system
Master the idea of distributed memory computing; master the architecture and ideas of Spark distributed computing; master the comparison and difference between Spark and Mapreduce distributed computing frameworks; master Spark’s RDD, DAG, Task, Partition and other design ideas; master the functions of SparkSQL, Integration of SparkSQL+Hive; master the programming model of DataFrame and DataSet; master the application scenarios of StructuredStreaming and integration with Kafka; master the ideas of MLlib data mining and GraphX graph computing; master the advanced features and performance tuning of the Spark technology stack ability
Cloud computing learning route 5, big data Spark project actual combat
Big data Spark memory computing system project actual combat
Cloud computing learning route VI. Big data flink real-time computing system
Master the framework and ideas of distributed real number computing; master the differences between Flink, Spark and MapReduce; master the functions and applications of Flink streaming computing; master the use of FlinkSQL; master the use of FlinkDataStream;
Cloud computing learning route seven, big data flink project actual combat
Solve the data analysis and application of the massive data in the enterprise for real-time requirements; solve the common optimization techniques and methods of Flink enterprise-level applications.
Cloud computing learning route 8. Detailed explanation of new big data technologies
Master the functions and application scenarios of Druid;
Cloud computing learning route 9, machine learning
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