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  • Spark Overview LECTURE
  • Spark Opportunity and Solution
  • Capabilities and Ecosystem
  • Spark Components vs Hadoop HANDS-ON
  • Databricks Lab Environment
  • Working with Notebooks
  • Spark Clusters and Files MODULE 2 RDD Fundamentals LECTURE
  • Purpose and Structure of RDDs
  • Transformations, Actions, and DAG
  • RDD programming API HANDS-ON
  • Creating RDDs from Data Files
  • Reshaping Data to Add Structure
  • Interactive Queries Using RDDs MODULE 3 Spark SQL / Dataframes LECTURE
  • Spark SQL and DataFrame Uses
  • DataFrame / SQL APIs
  • Catalyst Query Optimization HANDS-ON
  • Creating DataFrames
  • Query with DataFrame API and SQL
  • Caching and Re-using DataFrames
  • Generating Graphics and Reports MODULE 4 Spark Job Execution LECTURE
  • Jobs, Stages, and Tasks
  • Partitions and Shuffles
  • Data Locality
  • Job Performance HANDS-ON
  • Visualizing DAG Execution
  • Observing Task Scheduling
  • Understanding Performance
  • Measuring Memory Usage MODULE 5 Clustering Architecture LECTURE Cluster Managers for Spark: Spark Standalone, YARN, and Mesos
  • Understanding Spark on YARN HANDS-ON
  • Tracking Jobs through the Cluster UI
  • Understanding Deploy Modes
  • Specifying Executors, Cores, Memory MODULE 6 Spark Streaming LECTURE
  • Streaming Sources and Tasks
  • DStream APIs and Stateful Streams
  • Reliability and Fault Recovery HANDS-ON
  • Creating DStreams from Sources
  • Operating on DStream Data
  • Viewing Streaming Jobs in the Web UI MODULE 7 Machine Learning LECTURE
  • Basic Principles of Machine Learning
  • Spark ML API Patterns
  • Built-in Featurizing and Algorithm APIs LECTURE
  • Featurizing and Learning with RDDs
  • ML Using Pipelines and DataFrames