Teach you how to be a master of spark big Data?

Teach you how to be a master of spark big Data? Spark is now being used by more and more businesses, like Hadoop, where Spark is also submitting tasks to the cluster as a job, so how do you become a master of spark big Data? Here's an in-depth tutorial.

Spark is a cluster computing platform originating from the University of California, Berkeley, Amplab, which is a rare all-rounder based on memory computing, performance exceeding Hadoop, starting from multiple iterations of batch processing, eclectic data warehousing, streaming and graph computing. Spark uses a unified technology stack to solve all the core issues of cloud computing big data such as stream processing, graph technology, machine learning, NoSQL query and so on, and has a perfect ecosystem, which directly lays the dominant position in unified cloud computing big data field.

With the popularization of spark technology, the demand for professional talents is increasing. Spark professionals are also hot in the future and can easily get millions of pay. To be a spark master, you also need a recruit, from the internal skills: usually need to go through the following stages:

First stage: Mastering the Scala language skillfully

The spark framework, written in Scala, is refined and elegant. To be a spark master, you have to read the source code of Spark and you have to master Scala;

While the current spark can be used for application development in multi-lingual Java, Python, and so on, the fastest and best-supported development APIs remain and will always be Scala-style APIs, so you have to master Scala to write complex and high-performance spark distributed programs;

In particular, be proficient in Scala's trait, apply, functional programming, generics, contravariance and covariance;

Phase II: Proficiency in the Spark platform itself is provided to the developer API

Mastering the development pattern of the RDD in Spark, mastering the use of various transformation and action functions;

Mastering the wide dependency and narrow dependencies in spark and the lineage mechanism;

Master the calculation flow of the RDD, such as the division of the stage, the basic process that the spark application submits to the cluster, and how the Worker Node Foundation works

Phase three: Dive into the spark kernel

This phase is mainly through the Spark Framework's source reading to delve into the Spark kernel section:

Master Spark's task submission process through source code;

Master the task scheduling of Spark cluster through the source code;

In particular, be proficient in the details of each step of the work within the Dagscheduler, TaskScheduler, and worker nodes;

Class Four: Mastering the use of core frameworks based on spark

As a synthesizer in the era of cloud computing big data, Spark has a significant advantage in real-time streaming, graph technology, machine learning, NoSQL queries, and most of the time we use spark is using frameworks such as shark, spark streaming, and so on:

Spark streaming is a very good real-time streaming framework, mastering its dstream, transformation and checkpoint, etc.

Spark's off-line statistical analysis feature, Spark 1.0.0 version on the basis of shark launched spark SQL, offline statistical analysis of the efficiency of the function has significantly improved, need to focus on;

For spark machine learning and GRAPHX to master its principles and usage;

Class Five: Doing a business-class spark project

Complete every aspect of spark through a complete and representative spark project, including project architecture design, technical profiling, development implementation, operations, and more, all in one of these stages and details, so you can easily face the vast majority of spark projects in the future.

Class VI: Offering SPARK solutions

Thoroughly grasp every detail of the spark frame source code;

Provide spark solutions under different scenarios based on the needs of different business scenarios;

According to the actual needs, on the basis of the spark framework two development, build their own spark framework;

The first and second stages of the six stages of being a spark master can be completed by self-study, followed by three stages, preferably by a master or expert guidance of the next step, the last stage, basically is to "no strokes win The Recruit" period, a lot of things to be done with the heart.

Teach you how to be a master of spark big Data?