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Spark and Scala Course Content | Spark and Scala online training

Location:
Visakhapatnam, Andhra Pradesh - India

Published:
2018-07-24 11:19:53

Contact Info:
selfpacedtech

Phone Number: +1 201-905-1656

Website URL: Click To Visit

Description: Spark & Scala Course Contents Describe Features of Apache Spark • How Spark fits in Big Data ecosystem • Why Spark & Hadoop fit together Define Spark Components • Driver Program  Spark Context • Cluster Manager • Worker  Executor  Task • Spark RDD  Spark Context • Spark Libraries Load data into Spark • Different data sources and formats  HDFS  Amazon S3  Local File System  Text  JSON  CSV  Sequence File • Create & Use RDD, Data Frames Apply dataset operations to Resilient Distributed Datasets • Transformation • Actions • Cache Intermediate RDD  Lineage Graph  Lazy Evaluation Use Spark DataFrames for simple queries • Create Data Frame • Spark Interactive shell (Scala & Python) • Spark SQL Define different ways to run your application Build and launch a standalone application • Spark Program Life Cycle • Function of Spark Context • Different Way to Launch Spark Application  Local  Standalone  Hadoop YARN  Apache Mesos • Launch Spark Application  Spark-Submit  Monitor the Spark Job Describe & Create pair RDD • Key-Value pair • Apache Spark vs Apache Hadoop MapReduce • Create RDD from existing non-pair RDD • Create pair RDD by loading certain formats • Create pair RDD from in-memory collection of pairs Apply Operations on pair RDD • Group ByKey • Reduce ByKey • Other Transformations  Joins Control partitioning across nodes • RDD Partition • Types of Partition  Hash Partitioning  Range Partitioning • Benefit of Partitioning • Best Practices More on Data Frames • Explore Data in DataFrames • Create UDFs (user define functions)  UDF with Scala DSL  UDF with SQL • Repartition Data Frames. • Infer Schema by Reflection • DataFrame from database table • DataFrame from JSON Monitor Apache Spark Applications • Spark Execution Model • Debug and Tune Spark Applications Identify Spark Unified Stack Components • Spark SQL • Spark Streaming • Spark MLib • Spark GraphX Benefits of Apache Spark over Hadoop Ecosystem Describe Spark Data pipeline Use Cases • Spark Streaming Architecture • Dstream and a spark streaming application  Define Use Case (Time Series Data)  Basic Steps  Save Data to HBase • Operations on DStream  Transformations  Data Frame and SQL Operations • Define Windowed Operation  Sliding Window  Windowed Computation  Window based Transformation  Window Operations • Fault tolerance of streaming applications  Fault Tolerance in Spark Streaming  Fault Tolerance in Spark RDD  Check pointing Describe Graph X Define Regular, Directed, and property graphs Create a Property Graph Perform Operations on Graphs Describe Apache Spark MLib Describe the Machine Learning Techniques • Classifications • Clustering • Collaborative Filtering Use Collaborative filtering to predict user choice Scala • Introduction • A first example • Expressions and Simple Functions • First Class function • Classes and Objects • Case classes and Pattern matching • Generic types and methods • Lists • For- Comprehension • Mutable State • Computing with Streams • Lazy Values • Implicit Parameters and Conversions • Handley / Milner type Interface • Abstraction for concurrency Contact details: +1 416-834-6577 / +1 201-905-1656 WhatsApp : 9030990003/9000444287 Mail : selfpacedtech@gmail.com/training@selfpacedtech.com

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