Learning Spark can help data mining to be very effective. At the same time, due to the consideration of task pipeline, when generating multiple stages, the output of each stage needs to be stored based on the underlying file system, which is compatible with HDFS and Hive, and can be integrated into Hadoop ecosystem to make up for the deficiency of MapReduce. Spark is efficient, easy to use, universal and compatible, which can improve the calculation speed by hundreds of times, and can also query the optimization program and physical execution engine to achieve high performance of batch and stream data. At the same time, Spark supports the APIs of Java, Python and Scala, and also supports many advanced algorithms, so that users can quickly build different applications. Spark cluster can be easily used in these shell to verify the solution of the problem. Spark can be easily integrated with other open source products.

For the course of big data mining engineer, CDA data analyst is recommended. This course not only cultivates students' hard data mining theory and Python data mining algorithm skills, but also cultivates students' soft data governance thinking, business strategy optimization thinking, mining management thinking, algorithm thinking and predictive analysis thinking, so as to improve students' data insight in all directions. Click to make an appointment for a free audition class.