If you want to be a development engineer, the programming paradigm of MAP REDUCE, SPARK is fast to get started for programmers with some development experience. But according to the lecturer's own experience over the years, it's easy to simply be a programmer with a solidified mindset, limited vision, and repeated invention of the wheel. But to promote to a higher level, such as architect level, that hdfs, yarn, hive, hbase, kafka, zookeeper, impala, presto, phoenix, kylin, CAP, ELK, Solr a large number of face to face little monsters have become a roadblock.

The difficulty of Big Data is that its ecosystem is too complex, the family lineage is too confusing, in the face of an enterprise scenario there are n number of programs that say "I can, I'm not the average person". It's like eating lunch, you can either eat it in a bowl, or eat it in a pot, or even pour it on the table and grab it by hand. Each way of eating can be full, but some eating is very natural, very comfortable, some eating is very awkward, very obscene. How to be gentle and elegant, like a spring breeze this reflects the level of the architect.

This course is based on the teacher's many years of practical work experience in the domestic first-tier Internet companies, finishing and refining a set of focus on training big data architect level of combat courses, speaking focus on the use of big data in the first-tier enterprise program for each component, in addition to a detailed description of the operational requirements must be grasped, but also focus on the introduction of different business scenarios under the design and application of the skills. It is absolutely different from most of the operation manual readers on the market.

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This course is designed to include the following levels:

1. Big Data Integration: The main introduction to the ELK framework is currently very hot filebeat and logstash, compared to flume is lighter and easier to get started.

2. Big Data Transmission: mainly introduces the principle of kafka and the use of skills

3. Big Data Landing: mainly introduces the principle and use of the two standard combinations of hive and hbase, and combined with specific business scenarios to reveal the advanced design and application.

4. big data use: mainly introduces the most useful sql on hive, sql on hbase solutions in the enterprise, how to make hive speed up ten times, how to make hbase like a rdbms, how to implement scd2 in hive and other practical issues.

5. Big Data search engine: mainly introduces the current very hot ELK framework in Elasticsearch, and detailed demonstration from the regular operation to the high-level query of the full combat content.

It is believed that through the learning of this course, diligent you have been deep into the architect level of big data, the rest is in the work of the pit constantly filling the monster upgrade, and ultimately complete.