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Apache Spark Programming
Section 1 Spark introduction and RDD data flow execution
Spark Lecture 1 : Introduction (65:55)
Spark Lecture 2 : spark vs other hadoop ecosystems comparisons (32:42)
Section 2 : Spark Components
Spark Lecture 3: Introduction to spark components part 1 (48:37)
Spark Lecture 4 : Spark components part 2 (47:44)
Spark Lecture 5 : Introduction to Spark Streaming (39:24)
Section 3 : RDDs and DataFlow excecutions
Spark Lecture 6 : different ways of RDD creations (32:24)
Spark Lecture 7 : more on RDD flow excecution (54:21)
Spark Lecture 8 : how fault tolerance will be applied during flow excecution
Section 4 : Spark architecture
Lecture 9 : Spark Core Architecture part 1 (70:47)
Lecture 10 : Spark core Architecture part 2 (56:42)
Section 5 : Scala programming fundamentals
Lecture 11 : Scala programming 1 (71:42)
Lecture 12 : Scala prgramming 2 (20:59)
Lecture 13 : Scala programming 3 (58:07)
Lecture 14 : Scala programming 4 (82:13)
Lecture 15 : Scala programming 5 (90:22)
Lecture 16 : Scala programming 6 (67:03)
Lecture 17 : Scala Programming 7 (46:09)
Lecture 18 : Scala programming 8 (69:43)
Lecture 19 : What next in Scala (13:05)
Lecture 20 : Scala programming 10 (62:55)
Lecture 21 : Testing String equality (9:21)
Lecture 22 : Creating Multiline Strings (16:33)
Lecture 23 : Splitting Strings With Regular Expression (16:37)
Lecture 24 How to Substitute Strings into Variables (19:47)
Lecture 25 : Processing String One Character at a Time (25:30)
Lecture 26 : Finding Patterns in Strings Using Regular Expressions (30:28)
Section 6 : Spark core programming
Lecture 27 : Creating spark Rdds (35:10)
Lecture 28 : Saving processed results into HDFS (23:13)
Lecture 29 : Different transformations on RDDs and map() (52:06)
Lecture 30 : More on map transformations (36:50)
Lecture 31 : Importance of JSON data (35:51)
Lecture 32 : JSON vs NOSQL databases (14:22)
Lecture 33 : How to Flatten Single JSON record (35:36)
Lecture 34 : How to Flatten Multiple json records (44:40)
Lecture 35 : How to Flatten Nested Multiple json records (42:42)
Lecture 36 : FlatMap Vs map transformations part 1 (32:19)
Lecture 37 : FlatMap vs map transformations part 2 (28:27)
Lecture 38 : reduce() , comparisions with aggregation functions such as sum/max/min.. Special use cases of reduce() (30:52)
Lecture 39 : more use cases on reduce() Task1: find max population city Task2 : find max qualification from given profiles. (27:05)
Lecture 40 : Task: find the poeple, who has max counted qualification. (13:35)
Lecture 41 : Introduction to grouping aggregations on spark rdds # different transformations for grouping aggregations. -- reduceByKey -- countByKey -- aggregateByKey -- groupByKey (5:02)
Lecture 42 : performing grouping sum and count aggregations on rdd (18:14)
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)
Lecture 44 : performing multi grouping with single aggregations. (40:45)
Lecture 45 : performing multi grouping with multiple aggregations. --> why reduceByKey can not solve multiple aggregations. (57:01)
Lecture 46 : Union Transformations Part 1 (39:33)
Lecture 47 : Union Tranformations Part 2 (17:11)
Lecture 48 : Union Transformations Part 3 (20:24)
Lecture 49 : Unions with Zip transformations (14:42)
Lecture 50 : performing seperate aggregations for each file and generate report (9:54)
Lecture 51 : Join transformations on rdds part 1 (36:51)
Lecture 52 : Join transformations on RDD part 2 (20:54)
Lecture 53: Denormalizing Data sets Using Joins (44:37)
Lecture 54 : Grouping aggregations and required transformations on Full Outer Join Content of RDDs (24:46)
Section 7 : Spark SQL
Lecture 55 : spark Sql introduction (78:28)
Lecture 56 : Spark Sql Lab (69:20)
Lecture 57 : Hive and Spark Sql Integration (53:34)
Lecture 58 : Data frames and Data sets (53:54)
Lecture 59 : Converting file into Data Frame and processing with sql function (49:24)
Lecture 60 : Working With Multiple files using Spark sql (21:41)
Lecture 61 : Programatically Assigning schema (34:56)
Lecture 62 : Loading and Saving from Parquet files (24:44)
Section 8 : Spark Streaming with Kafka
Lecture 63: Spark Streaming Vs Kafka Introduction part 1 (70:54)
Lecture 64 : Spark Streaming Vs Kafka Introduction part 2 (30:05)
Lecture 65 : Spark Streaming Architecture (57:43)
Lecture 66 : Spark Streaming Windowing and Sliding (29:22)
Lecture 67 : Kafka Components part 1 (24:12)
Lecture 68 : Kafka Components Part 2 (6:55)
Lecture 69 : Kafka Components Part 3 (62:57)
Lecture 70 : Kafka implementation and setup part 1 (37:36)
Lecture 71 : Kafka implementation Part 2 (25:07)
Lecture 72 : Spark Streaming and Kafka Integration Example (68:50)
Section 9 : Spark Machine Learning
Lecture 73 : Spark MLLIB part 1 (35:38)
Lecture 74 : Spark MLLIB Kmeans (97:53)
Section 10 : Spark GraphX
Lecture 75 : Spark GraphX Session 1
Lecture 76 : Spark GraphX Session 2
Lecture 77 : Spark GraphX Session 3
Lecture 78 : Data Set Used for above Graphx examples
Lecture 50 : performing seperate aggregations for each file and generate report
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