Foreword
If you are an IT engineer, chief technology officer (CTO) you wish to submit a preliminary proposal for the company's future IT system infrastructure in a week; if you're an IT marketing staff, clients need you to report to him within the next week technical direction substantially large data systems ... ... in this era of information a serious surplus, within a week to seize the key technical context from the vast ocean of the technical details, and further proposed technology think there is some theoretical basis, it is almost impossible complete the task.
By reading the article you will be able to quickly build big data technology infrastructure-related knowledge and context, and not get lost in the vast knowledge of the details. Benpian help readers in the shortest possible time, the system grasp the big data related technical framework for the establishment of the ability to think and principles of system architecture level of technology. Suitable for large enterprise IT and data professionals, corporate chief technology officer (CTO), chief information officer (CIO).
In this part is divided into four parts, different readers can choose to read different content.
Because the content is too much, so small series only the part of the knowledge point shots out of the rough introduction about each section has a more granular content, I hope you can understand.
** The first part is when what should be considered from the "Getting Started with Big Data Architect", ** fictional character Xiao Ming perspective, to understand big data, understand the customer's worries, well presented building big data systems. After reading this section, readers will be given the ability to "ask questions" of big data solutions available. That is, if you are in front of a large building programs have taken the data, even if you have very little understanding of big data, it can also be based on the recommendations in this book, to judge the depth and breadth of integrity, program evaluation scheme.
** The second part is "big data base architect" ** This section will systematically introduce the basic knowledge related to big data. As shown, the base support layer introduced one by one, calculating the memory layer, middleware layer, mining analysis / application layer, presentation layer, the various parts, while, through the layers of the security and management of the two modules do presentation content, readers trying to present a relatively complete knowledge of big data architecture.
Wherein calculating the storage layer includes Hadoop architecture, Spark architecture, analyze the content of mining components such as: middleware layer including the role and significance of middleware, and industry-used middleware and application scenarios: presentation layer, including visual and content-related knowledge: Security Module including physical security, host security, network security, data security and other content: management module, including automated deployment, automatic upgrades, automatic inspection, automatic maintenance and so on.
** The third part is the "big data architect practice" ** including big data development practices of some experience, combined with elaborate cases. The actual accumulated knowledge and wisdom that will help integrate theory with practice, a better understanding of Big Data technologies.
** The fourth part is the "big data architect expand", ** including other technologies associated with big data. These techniques generally speaking, does not belong to the category of big data technology, but because these big data technologies and close relationship, as an architect, but also requires the system to understand and think about these related technologies, in order to grasp the entire program globally . This section will attempt a brief introduction of these technologies, and trying to explain the relationship between these technologies and big data.
Due to space restrictions, so small series here do not do too much introduction, and thank you for your cooperation and trust.
Benpian can help you:
quickly grasp the context of large data systems technology;
Efficient build enterprise | T system infrastructure;
system builds the future direction of Big Data technologies;
