Section 1 Spark introduction and RDD data flow execution
Section 2 : Spark Components
Section 3 : RDDs and DataFlow excecutions
Section 4 : Spark architecture
Section 5 : Scala programming fundamentals
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Lecture 11 : Scala programming 1 (71:42)
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Lecture 12 : Scala prgramming 2 (20:59)
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Lecture 13 : Scala programming 3 (58:07)
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Lecture 14 : Scala programming 4 (82:13)
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Lecture 15 : Scala programming 5 (90:22)
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Lecture 16 : Scala programming 6 (67:03)
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Lecture 17 : Scala Programming 7 (46:09)
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Lecture 18 : Scala programming 8 (69:43)
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Lecture 19 : What next in Scala (13:05)
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Lecture 20 : Scala programming 10 (62:55)
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Lecture 21 : Testing String equality (9:21)
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Lecture 22 : Creating Multiline Strings (16:33)
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Lecture 23 : Splitting Strings With Regular Expression (16:37)
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Lecture 24 How to Substitute Strings into Variables (19:47)
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Lecture 25 : Processing String One Character at a Time (25:30)
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Lecture 26 : Finding Patterns in Strings Using Regular Expressions (30:28)
Section 6 : Spark core programming
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Lecture 27 : Creating spark Rdds (35:10)
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Lecture 28 : Saving processed results into HDFS (23:13)
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Lecture 29 : Different transformations on RDDs and map() (52:06)
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Lecture 30 : More on map transformations (36:50)
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Lecture 31 : Importance of JSON data (35:51)
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Lecture 32 : JSON vs NOSQL databases (14:22)
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Lecture 33 : How to Flatten Single JSON record (35:36)
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Lecture 34 : How to Flatten Multiple json records (44:40)
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Lecture 35 : How to Flatten Nested Multiple json records (42:42)
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Lecture 36 : FlatMap Vs map transformations part 1 (32:19)
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Lecture 37 : FlatMap vs map transformations part 2 (28:27)
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Lecture 38 : reduce() , comparisions with aggregation functions such as sum/max/min.. Special use cases of reduce() (30:52)
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Lecture 39 : more use cases on reduce() Task1: find max population city Task2 : find max qualification from given profiles. (27:05)
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Lecture 40 : Task: find the poeple, who has max counted qualification. (13:35)
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Lecture 41 : Introduction to grouping aggregations on spark rdds # different transformations for grouping aggregations. -- reduceByKey -- countByKey -- aggregateByKey -- groupByKey (5:02)
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Lecture 42 : performing grouping sum and count aggregations on rdd (18:14)
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Lecture 43 : performing single grouping single aggregations by reading data from hdfs file --> grouping sum --> grouping count --> joining sum and count --> grouping average --> grouping max --> grouping min (22:44)
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Lecture 44 : performing multi grouping with single aggregations. (40:45)
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Lecture 45 : performing multi grouping with multiple aggregations. --> why reduceByKey can not solve multiple aggregations. (57:01)
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Lecture 46 : Union Transformations Part 1 (39:33)
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Lecture 47 : Union Tranformations Part 2 (17:11)
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Lecture 48 : Union Transformations Part 3 (20:24)
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Lecture 49 : Unions with Zip transformations (14:42)
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Lecture 50 : performing seperate aggregations for each file and generate report (9:54)
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Lecture 51 : Join transformations on rdds part 1 (36:51)
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Lecture 52 : Join transformations on RDD part 2 (20:54)
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Lecture 53: Denormalizing Data sets Using Joins (44:37)
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Lecture 54 : Grouping aggregations and required transformations on Full Outer Join Content of RDDs (24:46)
Section 7 : Spark SQL
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Lecture 55 : spark Sql introduction (78:28)
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Lecture 56 : Spark Sql Lab (69:20)
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Lecture 57 : Hive and Spark Sql Integration (53:34)
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Lecture 58 : Data frames and Data sets (53:54)
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Lecture 59 : Converting file into Data Frame and processing with sql function (49:24)
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Lecture 60 : Working With Multiple files using Spark sql (21:41)
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Lecture 61 : Programatically Assigning schema (34:56)
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Lecture 62 : Loading and Saving from Parquet files (24:44)
Section 8 : Spark Streaming with Kafka
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Lecture 63: Spark Streaming Vs Kafka Introduction part 1 (70:54)
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Lecture 64 : Spark Streaming Vs Kafka Introduction part 2 (30:05)
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Lecture 65 : Spark Streaming Architecture (57:43)
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Lecture 66 : Spark Streaming Windowing and Sliding (29:22)
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Lecture 67 : Kafka Components part 1 (24:12)
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Lecture 68 : Kafka Components Part 2 (6:55)
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Lecture 69 : Kafka Components Part 3 (62:57)
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Lecture 70 : Kafka implementation and setup part 1 (37:36)
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Lecture 71 : Kafka implementation Part 2 (25:07)
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Lecture 72 : Spark Streaming and Kafka Integration Example (68:50)
Section 9 : Spark Machine Learning
Section 10 : Spark GraphX
