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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Big Data Real-time Stream Computing Scenario-based Solution | 30% | - Stream data ingestion and processing - Real-time stream processing frameworks (Flume, Kafka, Spark Streaming, Flink) - Streaming use cases and configuration |
| Topic 2: Big Data Real-time Retrieval Scenario-based Solution | 30% | - Real-time retrieval solution components - Real-time query optimization - Distributed database concepts (HBase, Elasticsearch, GES) |
| Topic 3: Big Data Application Development Overall Guide | 15% | - Application development fundamentals - Big data concepts and architecture - Big data scenario-based solutions |
| Topic 4: Big Data Offline Batch Processing Scenario-based Solution | 25% | - Spark SQL and data ingestion tools (Loader, Sqoop, Kettle) - Offline batch processing solutions - HDFS and Hive fundamentals |
In FusionInsight HD, which of the following are the configuration items of Oozie's MapReduce Action? (multiple choice)
In Spark application development, which of the following codes can correctly count words?
Suppose there is an application, there are 10 tables, each table has tens of millions of records, and the number of fields is about 20.
Now Redis is used to cache the data of these 10 tables. For the design of the data structure, which of the following is the best design?
In the process of Flume cascade transmission, you can use the fail over mode to transmit, so that if the Flume node of the next hop fails or the data is received abnormally, it can automatically switch to another way to continue transmission.
In FusionInsight HD, regarding Hive's data loading function (importing data through Hive's LOAD command), what is wrong in the following description?
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