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  • Big Data
  • Introduction in Apache Spark
Cursurile noastreBig DataIntroduction in Apache Spark
  • Day 1: Spark Overview 13

    • Capitol1.1
      A brief history of Spark
    • Capitol1.2
      Where Spark fits in the big data landscape
    • Capitol1.3
      Apache Spark vs. Apache MapReduce: An overall architecture comparison
    • Capitol1.4
      Cluster Architecture: cluster manager, workers, executors; Spark Context; Cluster Manager Types; Deployment scenarios
    • Capitol1.5
      How Spark schedules and executes jobs and tasks
    • Capitol1.6
      Resilient Distributed Datasets : Fundamentals & hands on exercises
    • Capitol1.7
      Ways to create an RDD: Parallelize Collection; Read from external data source; From existing RDD
    • Capitol1.8
      Introduction to Transformations and Actions
    • Capitol1.9
      Caching
    • Capitol1.10
      RDD Types
    • Capitol1.11
      How transformations lazily build up a Directed Acyclic Graph (DAG)
    • Capitol1.12
      Shuffling
    • Capitol1.13
      Hands on: using Spark for ETL
  • Day 2: SparkSQL and DataFrames/Datasets : Fundamentals and hands on exercises 5

    • Capitol2.1
      What are DataFrames/Datasets vs RDD’s
    • Capitol2.2
      The DataFrames/Datasets API
    • Capitol2.3
      Spark SQL
    • Capitol2.4
      Creating and running DataFrame operations
    • Capitol2.5
      Reading from multiple data sources and hands on exercises: HDFS; noSQL; Hive
  • Day 3: Spark Streaming 6

    • Capitol3.1
      When to use Spark Streaming
    • Capitol3.2
      DStream
    • Capitol3.3
      Structured streaming: Building a Spark streams out of Kafka topics; Windowing & Aggregation; Register a Spark DF stream
    • Capitol3.4
      Spark’s MLlib and MLlib Pipeline API (Spark.ml) for Machine Learning
    • Capitol3.5
      Spark MLlib and Spark.ml
    • Capitol3.6
      Machine Learning Examples: Collaborative filtering: Alternating Least Squares; Classification and regression
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