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Conquer Big Data through Spark
Course Background:
Apache Spark™ is a fast and general engine for large-scale data processing. Spark has an advanced DAG execution engine that supports cyclic data flow and in-memory computing. You can run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.:
Spark powers a stack of high-level tools including Spark SQL,MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application:
You can run Spark readily using its standalone cluster mode, on EC2, or run it on Hadoop YARN or Apache Mesos. It can read from HDFS, HBase, Cassandra, and any Hadoop data source:
Write applications quickly in Java, Scala or Python.Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala and Python shells.
Apache Spark has seen phenomenal adoption, being widely slated as the successor to Hadoop MapReduce, and being deployed in clusters from a handful to thousands of nodes.
In the past few years ,Databricks, with the help of the Spark community, has contributed many improvements to Apache Spark to improve its performance, stability, and scalability. This enabled Databricks to use Apache Spark to sort 100 TB of data on 206 machines in 23 minutes, which is 3X faster than the previous Hadoop 100TB result on 2100 machines. Similarly, Databricks sorted 1 PB of data on 190 machines in less than 4 hours, which is over 4X faster than the previous Hadoop 1PB result on 3800 machines.
Spark is fulfilling its promise to serve as a faster and more scalable engine for data processing of all sizes. Spark enables equally dramatic improvements in time and cost for all Big Data users.
Course Introduction:
This course almost covers everything for Application Developer to build diverse Spark applications to fulfill all kinds of business requirements: Architecture of Spark、the programming model in Spark、internals of Spark、Spark SQL、MLlib、GraphX、Spark Streaming、Testing、Tuning、Spark on Yarn、JobServer and SparkR.
Additional，this course also covers the very necessary skills you need to write Scala code in Spark, to help whom is not familiar with Scala.
Who Needs to Attend
Anyone who is interested in Big Data Development;
Hadoop Developer；
Other Big Data Developer;
王家林老师(联系邮箱18610086859@126.com 电话：18610086859 QQ:1740415547 微信号：18610086859)
Prerequisites
Be familiar with the basics of object-oriented programming;
Course Outline
Day 1 | Class 1： The architecture of Spark 1 Ecosystem of Spark 2 Design of Spark 3 RDD 4 Fault-tolerance in Spark
Class 2Programming with Scala 1 Classes and Objects in Scala 2 Funtional Object 3 Traits 4 Case class and Pattern Matching 5 Collections 6 Implicit Conversions and Parameters 7 Actors and Concurrency
Class 3：Spark Programming Model 1 RDD 2 transformation 3 action 4 lineage 5 Dependency
Class 4：Spark Internals 1 Spark Cluster 2 Job Scheduling 3 DAGScheduler 4 TaskScheduler 5 Task Internal
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TIME | CONTENT | Note |
Day 2 | Class 5：Broadcasts and Accumulators 1 Broadcast Internal 2 Best practice in Broadcast 3 Accumulators Internal 4 Best practice in Accumulators
Class 6：Action in programming Spark 1 Data Source：File、HDFS、HBase、S3; 2 IDEA 3 Maven 4 sbt. 5 Code 6 Deployment
Class 7：Deep in Spark Driver 1 The Secret of SparkContext 2 The Secret of SparkConf 4 The Secret of SparkEnv
Class 8：Deep in RDD 1 DAG 2 Scala RDD Function 3 Spark Java RDD Function 4 RDD Tuning
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TIME | CONTENT | NOTE |
Day 3 | Class 9：Machine Learning on Spark 1 LinearRegression 2 K-Means 3 Collaborative Filtering
Class 10: Graph Computation on Spark 1 Table Operators 2 Graph Operators 3 GraphX Algorithms
Class 11: Spark SQL 1 Parquet、JSON、JDBC 2 DSL 3 SQL on RDD
Class 12：Spark Streaming 1 DStream 2 transformation 3 checkpoint 4 Tuning
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TIME | CONTENT | NOTE |
Day 4 | Class 13：Spark on Yarn 1 Internals of Spark on Yarn 2 Best practice of Spark on Yarn
Class 14：JobServer 1 Restful Architecture of JobServer 2 JobServer APIs 3 Best Practice of JobServer
Class 15：SparkR 1 Programming in R 2 R on Spark 3 Internals of SparkR 4 SparkR API
Class 16：Spark Tuing 1 Logs 2 Concurency 3 Memory 4 GC 5 Serializers 6 Safety 7 14s cases of Tuning
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Conquer Big Data through Spark