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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