├── .gitignore
├── Azure Databricks Workshop Presentation.pdf
├── Data
├── asa
│ └── airlinecodes
│ │ └── airlinecodes.csv
├── bgdbxworkshop
│ ├── flights
│ │ ├── 0
│ │ │ └── 2019
│ │ │ │ └── 08
│ │ │ │ ├── 21
│ │ │ │ ├── 19
│ │ │ │ │ ├── 37
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 39
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 41
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 43
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 45
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 47
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 49
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 51
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 53
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 55
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 57
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ └── 59
│ │ │ │ │ │ └── 08.avro
│ │ │ │ ├── 20
│ │ │ │ │ ├── 11
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 13
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 15
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 17
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 19
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 21
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 23
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 25
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 27
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 29
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 31
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 33
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 35
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 37
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 39
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 41
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 43
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 45
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 47
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 49
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 51
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 53
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 55
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 57
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 59
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 01
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 03
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 05
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 07
│ │ │ │ │ │ └── 08.avro
│ │ │ │ │ └── 09
│ │ │ │ │ │ └── 08.avro
│ │ │ │ └── 21
│ │ │ │ │ ├── 11
│ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 13
│ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 15
│ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 17
│ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 01
│ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 03
│ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 05
│ │ │ │ │ └── 08.avro
│ │ │ │ │ ├── 07
│ │ │ │ │ └── 08.avro
│ │ │ │ │ └── 09
│ │ │ │ │ └── 08.avro
│ │ │ │ └── 22
│ │ │ │ ├── 00
│ │ │ │ ├── 29
│ │ │ │ │ └── 08.avro
│ │ │ │ ├── 31
│ │ │ │ │ └── 08.avro
│ │ │ │ ├── 33
│ │ │ │ │ └── 08.avro
│ │ │ │ ├── 35
│ │ │ │ │ └── 08.avro
│ │ │ │ ├── 37
│ │ │ │ │ └── 08.avro
│ │ │ │ ├── 39
│ │ │ │ │ └── 08.avro
│ │ │ │ ├── 41
│ │ │ │ │ └── 08.avro
│ │ │ │ ├── 43
│ │ │ │ │ └── 08.avro
│ │ │ │ ├── 45
│ │ │ │ │ └── 08.avro
│ │ │ │ ├── 47
│ │ │ │ │ └── 08.avro
│ │ │ │ ├── 49
│ │ │ │ │ └── 08.avro
│ │ │ │ ├── 51
│ │ │ │ │ └── 08.avro
│ │ │ │ ├── 53
│ │ │ │ │ └── 08.avro
│ │ │ │ ├── 55
│ │ │ │ │ └── 08.avro
│ │ │ │ ├── 57
│ │ │ │ │ └── 08.avro
│ │ │ │ └── 59
│ │ │ │ │ └── 08.avro
│ │ │ │ └── 09
│ │ │ │ ├── 45
│ │ │ │ └── 08.avro
│ │ │ │ ├── 47
│ │ │ │ └── 08.avro
│ │ │ │ ├── 49
│ │ │ │ └── 08.avro
│ │ │ │ ├── 51
│ │ │ │ └── 08.avro
│ │ │ │ ├── 53
│ │ │ │ └── 08.avro
│ │ │ │ ├── 55
│ │ │ │ └── 08.avro
│ │ │ │ └── 57
│ │ │ │ └── 08.avro
│ │ └── 1
│ │ │ └── 2019
│ │ │ └── 08
│ │ │ ├── 21
│ │ │ ├── 19
│ │ │ │ ├── 37
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 39
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 41
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 43
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 45
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 47
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 49
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 51
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 53
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 55
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 57
│ │ │ │ │ └── 09.avro
│ │ │ │ └── 59
│ │ │ │ │ └── 09.avro
│ │ │ ├── 20
│ │ │ │ ├── 11
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 13
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 15
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 17
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 19
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 21
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 23
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 25
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 27
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 29
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 31
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 33
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 35
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 37
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 39
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 41
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 43
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 45
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 47
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 49
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 51
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 53
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 55
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 57
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 59
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 01
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 03
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 05
│ │ │ │ │ └── 09.avro
│ │ │ │ ├── 07
│ │ │ │ │ └── 09.avro
│ │ │ │ └── 09
│ │ │ │ │ └── 09.avro
│ │ │ └── 21
│ │ │ │ ├── 11
│ │ │ │ └── 09.avro
│ │ │ │ ├── 13
│ │ │ │ └── 09.avro
│ │ │ │ ├── 15
│ │ │ │ └── 09.avro
│ │ │ │ ├── 17
│ │ │ │ └── 09.avro
│ │ │ │ ├── 01
│ │ │ │ └── 09.avro
│ │ │ │ ├── 03
│ │ │ │ └── 09.avro
│ │ │ │ ├── 05
│ │ │ │ └── 09.avro
│ │ │ │ ├── 07
│ │ │ │ └── 09.avro
│ │ │ │ └── 09
│ │ │ │ └── 09.avro
│ │ │ └── 22
│ │ │ ├── 00
│ │ │ ├── 29
│ │ │ │ └── 09.avro
│ │ │ ├── 31
│ │ │ │ └── 09.avro
│ │ │ ├── 33
│ │ │ │ └── 09.avro
│ │ │ ├── 35
│ │ │ │ └── 09.avro
│ │ │ ├── 37
│ │ │ │ └── 09.avro
│ │ │ ├── 39
│ │ │ │ └── 09.avro
│ │ │ ├── 41
│ │ │ │ └── 09.avro
│ │ │ ├── 43
│ │ │ │ └── 09.avro
│ │ │ ├── 45
│ │ │ │ └── 09.avro
│ │ │ ├── 47
│ │ │ │ └── 09.avro
│ │ │ ├── 49
│ │ │ │ └── 09.avro
│ │ │ ├── 51
│ │ │ │ └── 09.avro
│ │ │ ├── 53
│ │ │ │ └── 09.avro
│ │ │ ├── 55
│ │ │ │ └── 09.avro
│ │ │ ├── 57
│ │ │ │ └── 09.avro
│ │ │ └── 59
│ │ │ │ └── 09.avro
│ │ │ └── 09
│ │ │ ├── 45
│ │ │ └── 09.avro
│ │ │ ├── 47
│ │ │ └── 09.avro
│ │ │ ├── 49
│ │ │ └── 09.avro
│ │ │ ├── 51
│ │ │ └── 09.avro
│ │ │ ├── 53
│ │ │ └── 09.avro
│ │ │ ├── 55
│ │ │ └── 09.avro
│ │ │ └── 57
│ │ │ └── 09.avro
│ └── initechorders
│ │ ├── 0
│ │ └── 2019
│ │ │ └── 08
│ │ │ ├── 21
│ │ │ ├── 19
│ │ │ │ ├── 38
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 39
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 40
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 41
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 42
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 43
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 44
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 45
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 46
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 47
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 48
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 49
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 50
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 51
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 52
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 53
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 54
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 55
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 56
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 57
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 58
│ │ │ │ │ └── 52.avro
│ │ │ │ └── 59
│ │ │ │ │ └── 52.avro
│ │ │ ├── 20
│ │ │ │ ├── 10
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 11
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 12
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 13
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 14
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 15
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 16
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 17
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 18
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 19
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 20
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 21
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 22
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 23
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 24
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 25
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 26
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 27
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 28
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 29
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 30
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 31
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 32
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 33
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 34
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 35
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 36
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 37
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 38
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 39
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 40
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 41
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 42
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 43
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 44
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 45
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 46
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 47
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 48
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 49
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 50
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 51
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 52
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 53
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 54
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 55
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 56
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 57
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 58
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 59
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 00
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 01
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 02
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 03
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 04
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 05
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 06
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 07
│ │ │ │ │ └── 52.avro
│ │ │ │ ├── 08
│ │ │ │ │ └── 52.avro
│ │ │ │ └── 09
│ │ │ │ │ └── 52.avro
│ │ │ └── 21
│ │ │ │ ├── 10
│ │ │ │ └── 52.avro
│ │ │ │ ├── 11
│ │ │ │ └── 52.avro
│ │ │ │ ├── 12
│ │ │ │ └── 52.avro
│ │ │ │ ├── 13
│ │ │ │ └── 52.avro
│ │ │ │ ├── 14
│ │ │ │ └── 52.avro
│ │ │ │ ├── 15
│ │ │ │ └── 52.avro
│ │ │ │ ├── 16
│ │ │ │ └── 52.avro
│ │ │ │ ├── 17
│ │ │ │ └── 52.avro
│ │ │ │ ├── 00
│ │ │ │ └── 52.avro
│ │ │ │ ├── 01
│ │ │ │ └── 52.avro
│ │ │ │ ├── 02
│ │ │ │ └── 52.avro
│ │ │ │ ├── 03
│ │ │ │ └── 52.avro
│ │ │ │ ├── 04
│ │ │ │ └── 52.avro
│ │ │ │ ├── 05
│ │ │ │ └── 52.avro
│ │ │ │ ├── 06
│ │ │ │ └── 52.avro
│ │ │ │ ├── 07
│ │ │ │ └── 52.avro
│ │ │ │ ├── 08
│ │ │ │ └── 52.avro
│ │ │ │ └── 09
│ │ │ │ └── 52.avro
│ │ │ └── 22
│ │ │ ├── 00
│ │ │ ├── 29
│ │ │ │ └── 52.avro
│ │ │ ├── 30
│ │ │ │ └── 52.avro
│ │ │ ├── 31
│ │ │ │ └── 52.avro
│ │ │ ├── 32
│ │ │ │ └── 52.avro
│ │ │ ├── 33
│ │ │ │ └── 52.avro
│ │ │ ├── 34
│ │ │ │ └── 52.avro
│ │ │ ├── 35
│ │ │ │ └── 52.avro
│ │ │ ├── 37
│ │ │ │ └── 52.avro
│ │ │ ├── 38
│ │ │ │ └── 52.avro
│ │ │ ├── 39
│ │ │ │ └── 52.avro
│ │ │ ├── 40
│ │ │ │ └── 52.avro
│ │ │ ├── 41
│ │ │ │ └── 52.avro
│ │ │ ├── 42
│ │ │ │ └── 52.avro
│ │ │ ├── 43
│ │ │ │ └── 52.avro
│ │ │ ├── 44
│ │ │ │ └── 52.avro
│ │ │ ├── 45
│ │ │ │ └── 52.avro
│ │ │ ├── 46
│ │ │ │ └── 52.avro
│ │ │ ├── 47
│ │ │ │ └── 52.avro
│ │ │ ├── 48
│ │ │ │ └── 52.avro
│ │ │ ├── 49
│ │ │ │ └── 52.avro
│ │ │ ├── 50
│ │ │ │ └── 52.avro
│ │ │ ├── 51
│ │ │ │ └── 52.avro
│ │ │ ├── 52
│ │ │ │ └── 52.avro
│ │ │ ├── 53
│ │ │ │ └── 52.avro
│ │ │ ├── 54
│ │ │ │ └── 52.avro
│ │ │ ├── 55
│ │ │ │ └── 52.avro
│ │ │ ├── 56
│ │ │ │ └── 52.avro
│ │ │ ├── 57
│ │ │ │ └── 52.avro
│ │ │ ├── 58
│ │ │ │ └── 52.avro
│ │ │ └── 59
│ │ │ │ └── 52.avro
│ │ │ ├── 04
│ │ │ └── 11
│ │ │ │ └── 52.avro
│ │ │ └── 09
│ │ │ ├── 45
│ │ │ └── 52.avro
│ │ │ ├── 46
│ │ │ └── 52.avro
│ │ │ ├── 47
│ │ │ └── 52.avro
│ │ │ ├── 48
│ │ │ └── 52.avro
│ │ │ ├── 49
│ │ │ └── 52.avro
│ │ │ ├── 50
│ │ │ └── 52.avro
│ │ │ ├── 51
│ │ │ └── 52.avro
│ │ │ ├── 52
│ │ │ └── 52.avro
│ │ │ ├── 53
│ │ │ └── 52.avro
│ │ │ ├── 54
│ │ │ └── 52.avro
│ │ │ ├── 55
│ │ │ └── 52.avro
│ │ │ └── 56
│ │ │ └── 52.avro
│ │ └── 1
│ │ └── 2019
│ │ └── 08
│ │ ├── 21
│ │ ├── 19
│ │ │ ├── 38
│ │ │ │ └── 51.avro
│ │ │ ├── 39
│ │ │ │ └── 51.avro
│ │ │ ├── 40
│ │ │ │ └── 51.avro
│ │ │ ├── 41
│ │ │ │ └── 51.avro
│ │ │ ├── 42
│ │ │ │ └── 51.avro
│ │ │ ├── 43
│ │ │ │ └── 51.avro
│ │ │ ├── 44
│ │ │ │ └── 51.avro
│ │ │ ├── 45
│ │ │ │ └── 51.avro
│ │ │ ├── 46
│ │ │ │ └── 51.avro
│ │ │ ├── 47
│ │ │ │ └── 51.avro
│ │ │ ├── 48
│ │ │ │ └── 51.avro
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└── initech
│ ├── orders
│ ├── _SUCCESS
│ ├── _committed_7558485496134666940
│ ├── _started_7558485496134666940
│ └── part-00000-tid-7558485496134666940-862f4ac8-28a1-42c5-b52e-f3115d7c4a7a-3895-1-c000.snappy.parquet
│ ├── productRatings
│ ├── _SUCCESS
│ ├── _committed_2614548761011000950
│ ├── _started_2614548761011000950
│ ├── part-00000-tid-2614548761011000950-ae5f1608-f040-4128-aab2-f27eedb5fcc6-0-c000.snappy.parquet
│ ├── part-00001-tid-2614548761011000950-ae5f1608-f040-4128-aab2-f27eedb5fcc6-0-c000.snappy.parquet
│ ├── part-00002-tid-2614548761011000950-ae5f1608-f040-4128-aab2-f27eedb5fcc6-0-c000.snappy.parquet
│ ├── part-00003-tid-2614548761011000950-ae5f1608-f040-4128-aab2-f27eedb5fcc6-0-c000.snappy.parquet
│ ├── part-00004-tid-2614548761011000950-ae5f1608-f040-4128-aab2-f27eedb5fcc6-0-c000.snappy.parquet
│ ├── part-00005-tid-2614548761011000950-ae5f1608-f040-4128-aab2-f27eedb5fcc6-0-c000.snappy.parquet
│ ├── part-00006-tid-2614548761011000950-ae5f1608-f040-4128-aab2-f27eedb5fcc6-0-c000.snappy.parquet
│ └── part-00007-tid-2614548761011000950-ae5f1608-f040-4128-aab2-f27eedb5fcc6-0-c000.snappy.parquet
│ ├── productsCsv
│ └── product.csv
│ ├── productsFull
│ ├── _SUCCESS
│ ├── _committed_8107810065309857914
│ ├── _started_8107810065309857914
│ └── part-00000-tid-8107810065309857914-bc9722c8-f9ec-40de-abb3-3e1af2662905-0-c000.snappy.parquet
│ └── productsShort
│ ├── _SUCCESS
│ ├── _committed_2234460380132859798
│ ├── _started_2234460380132859798
│ └── part-00000-tid-2234460380132859798-58684b80-7470-423e-900c-73c2038518ec-0-c000.snappy.parquet
├── LICENSE
├── Notebooks
├── 01 - Databricks Overview.scala
├── 02 - Introduction to Notebooks.py
├── 03 - Data Engineering 1.py
├── 04 - Data Engineering 2.py
├── 05 - Data Engineering 3.py
├── 06 - Product Recommendation Engine.py
├── 07 - Model Hosting with AzureML and MLFlow.py
├── Solutions
│ ├── 03 - Data Engineering 1 Answers.py
│ ├── 04 - Data Engineering 2 Answers.py
│ └── 05 - Data Engineering 3 Answers.py
└── ValidationTests
│ ├── 04 - Data Engineering 2 Validation.py
│ └── 05 - Data Engineering 3 Validation.py
├── Other
└── labs2019-backup.dbc
└── README.md
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | *.egg-info/
24 | .installed.cfg
25 | *.egg
26 | MANIFEST
27 |
28 | # PyInstaller
29 | # Usually these files are written by a python script from a template
30 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
31 | *.manifest
32 | *.spec
33 |
34 | # Installer logs
35 | pip-log.txt
36 | pip-delete-this-directory.txt
37 |
38 | # Unit test / coverage reports
39 | htmlcov/
40 | .tox/
41 | .coverage
42 | .coverage.*
43 | .cache
44 | nosetests.xml
45 | coverage.xml
46 | *.cover
47 | .hypothesis/
48 | .pytest_cache/
49 |
50 | # Translations
51 | *.mo
52 | *.pot
53 |
54 | # Django stuff:
55 | *.log
56 | local_settings.py
57 | db.sqlite3
58 |
59 | # Flask stuff:
60 | instance/
61 | .webassets-cache
62 |
63 | # Scrapy stuff:
64 | .scrapy
65 |
66 | # Sphinx documentation
67 | docs/_build/
68 |
69 | # PyBuilder
70 | target/
71 |
72 | # Jupyter Notebook
73 | .ipynb_checkpoints
74 |
75 | # pyenv
76 | .python-version
77 |
78 | # celery beat schedule file
79 | celerybeat-schedule
80 |
81 | # SageMath parsed files
82 | *.sage.py
83 |
84 | # Environments
85 | .env
86 | .venv
87 | env/
88 | venv/
89 | ENV/
90 | env.bak/
91 | venv.bak/
92 |
93 | # Spyder project settings
94 | .spyderproject
95 | .spyproject
96 |
97 | # Rope project settings
98 | .ropeproject
99 |
100 | # mkdocs documentation
101 | /site
102 |
103 | # mypy
104 | .mypy_cache/
105 |
--------------------------------------------------------------------------------
/Azure Databricks Workshop Presentation.pdf:
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/Data/asa/airlinecodes/airlinecodes.csv:
--------------------------------------------------------------------------------
1 | AirlineName,AirlineCode,Designator,Alliance
2 | Adria Airways,JP,165,Star Alliance
3 | Aegean Airlines,A3,390,Star Alliance
4 | Aer Arann,RE,809,
5 | Aer Lingus,EI,53,
6 | Aeroflot Russian Airlines,SU,555,SkyTeam
7 | Aerolineas Argentinas,AR,44,
8 | Aeromexico,AM,139,SkyTeam
9 | Air Algerie,AH,124,
10 | Air Astana,KC,465,
11 | Air Canada,AC,14,Star Alliance
12 | Air China,CA,999,Star Alliance
13 | Air Europa,UX,996,SkyTeam
14 | Air France,AF,57,SkyTeam
15 | Air India,AI,98,Star Alliance
16 | Air Malta,KM,643,
17 | Air Namibia,SW,186,
18 | Air New Zealand,NZ,86,Star Alliance
19 | Air Seychelles,HM,61,
20 | Air Tahiti,VT,135,
21 | Air Zimbabwe,UM,168,
22 | Alaska Airlines,AS,27,
23 | Alitalia,AZ,55,SkyTeam
24 | All Nippon Airways,NH,205,Star Alliance
25 | American Airlines,AA,1,Oneworld
26 | Arik Air,W3,725,
27 | Asiana Airlines,OZ,988,Star Alliance
28 | Atlantic Airways,RC,767,
29 | Aurigny,GR,924,
30 | Austrian Airlines,OS,257,Star Alliance
31 | Avianca,AV,134,
32 | Azerbaijan Hava Yollary,J2,771,
33 | Bangkok Airways,PG,829,
34 | Blue1,KF,142,Star Alliance
35 | British Airways,BA,125,Oneworld
36 | Brussels Airlines,SN,82,Star Alliance
37 | Bulgaria Air,FB,623,
38 | Cathay Pacific,CX,160,Oneworld
39 | Czech Airlines,OK,64,SkyTeam
40 | China Airlines,CI,297,SkyTeam
41 | China Eastern Airlines,MU,781,SkyTeam
42 | China Southern Airlines,CZ,784,SkyTeam
43 | Croatia Airlines,OU,831,Star Alliance
44 | Cyprus Airways,CY,48,
45 | Delta Air Lines,DL,6,SkyTeam
46 | Eastern Airways,T3,,
47 | Egyptair,MS,77,Star Alliance
48 | El Al Israel Airlines,LY,114,
49 | Emirates,EK,176,
50 | Estonian Air,OV,960,
51 | Ethiopian Airlines,ET,71,Star Alliance
52 | Etihad Airways,EY,607,
53 | Eva Air,BR,695,Star Alliance
54 | Finnair,AY,105,Oneworld
55 | Flybe,BE,267,
56 | Garuda Indonesia,GA,126,SkyTeam
57 | Gulf Air,GF,72,
58 | HAHN Air,HR,169,
59 | Hong Kong Airlines,HX,851,
60 | Iberia,IB,75,Oneworld
61 | Icelandair,FI,108,
62 | Japan Airlines,JL,131,Oneworld
63 | Jet Airways,9W,589,
64 | Kenya Airways,KQ,706,SkyTeam
65 | KLM Royal Dutch Airlines,KL,74,SkyTeam
66 | Korean Air,KE,180,SkyTeam
67 | Kuwait Airways,KU,229,
68 | LAN Colombia,LA,45,Oneworld
69 | LOT - Polish Airlines,LO,80,Star Alliance
70 | Lufthansa,LH,220,Star Alliance
71 | Luxair,LG,149,
72 | Malaysia Airlines,MH,232,Oneworld
73 | Middle East Airlines,ME,76,SkyTeam
74 | Meridiana,IG,191,
75 | Mexicana,MX,132,Oneworld
76 | Northwest Airlines,NW,,
77 | Norwegian Air Shuttle,DY,328,
78 | Olympic Air,OA,50,
79 | Oman Air,WY,910,
80 | Rossiya-Russia Airlines,FV,195,
81 | Qantas Airways,QF,81,Oneworld
82 | Qatar Airways,QR,157,Oneworld
83 | Royal Air Maroc,AT,147,
84 | Royal Brunei Airlines,BI,672,
85 | Royal Jordanian,RJ,512,Oneworld
86 | Siberia Airlines,S7,421,Oneworld
87 | Saudi Arabian Airlines,SV,65,
88 | Scandinavian Airlines System (SAS),SK,117,Star Alliance
89 | Singapore Airlines,SQ,618,Star Alliance
90 | South African Airways,SA,83,Star Alliance
91 | Spanair,JK,680,Star Alliance
92 | SriLankan Airlines,UL,603,Oneworld
93 | SWISS International Air Lines,LX,724,Star Alliance
94 | TAM Airlines,JJ,957,Oneworld
95 | TAP Portugal,TP,47,Star Alliance
96 | Tarom,RO,281,SkyTeam
97 | Thai Airways International,TG,217,Star Alliance
98 | Transaero Airlines,UN,670,
99 | Tunisair,TU,199,
100 | Turkish Airlines,TK,235,Star Alliance
101 | Ukraine International Airlines,PS,566,
102 | United Airlines,UA,16,Star Alliance
103 | US Airways,US,37,Oneworld
104 | Uzbekistan Airways,HY,250,
105 | Vietnam Airlines,VN,738,SkyTeam
106 | Virgin Atlantic Airways,VS,932,
107 |
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1 | product_id,category,brand,model,price,processor,size,display
2 | 1,Laptops ,HP,"Spectre x360 2-in-1 13.3"" 4K Ultra HD Touch-Screen Laptop",1499.989990234375,null,null,null
3 | 2,Laptops ,Microsoft,"Surface Pro – 12.3""",1299.989990234375,null,null,null
4 | 3,Laptops ,Microsoft,"Surface Book 2 - 13.5""",1499.989990234375,null,null,null
5 | 4,Laptops ,Dell,"XPS 2-in-1 13.3""",1949.989990234375,null,null,null
6 | 5,Laptops ,Lenovo,"Yoga 920 2-in-1 13.9""",1799.989990234375,null,null,null
7 | 6,Laptops ,Apple ,"MacBook Pro - 15"" Display",2659.989990234375,null,null,null
8 | 7,Laptops ,Apple ,"MacBook Pro - 13"" Display",1499.989990234375,null,null,null
9 | 8,Laptops ,Apple ,"MacBook Pro - 15.4"" Display",1999.989990234375,null,null,null
10 | 9,Laptops ,Apple ,"MacBook Air - 13.3"" Display",999.989990234375,null,null,null
11 | 10,Laptops ,HP,"Pavilion x360 2-in-1 14"" Touch-Screen Laptop",999.989990234375,null,null,null
12 | 11,Laptops ,Dell,"2-in-1 17.3"" Touch-Screen Laptop",1299.989990234375,null,null,null
13 | 12,Laptops ,Samsung,"Notebook 9 Pro 13.3"" Touch-Screen Laptop",1099.989990234375,null,null,null
14 | 13,Laptops ,Google,"Pixelbook 12.3"" Touchscreen Chromebook",1699.989990234375,null,null,null
15 | 14,Laptops ,Samsung,"Notebook 9 spin 13.3"" Touch-Screen Laptop",1199.989990234375,null,null,null
16 | 15,Laptops ,Dell,"15.6"" Laptop",2279.989990234375,null,null,null
17 | 16,Laptops ,Dell,"2-in-1 17.3"" Touch-Screen Laptop",1299.989990234375,null,null,null
18 | 17,Laptops ,Microsoft,"Surface Book 13.5"" Touch Screen with Performance Base",2399.989990234375,null,null,null
19 | 18,Laptops ,Lenovo,"Star Wars Special Edition Galactic Empire - Yoga 910 2-in-1 13.9"" Laptop",1299.989990234375,null,null,null
20 | 19,Laptops ,Microsoft,Surface Pro – 12.3”,2699.989990234375,null,null,null
21 | 20,Laptops ,Lenovo,"15.6"" Laptop",2499.989990234375,null,null,null
22 | 21,tablets,HP®,Envy x2 (2018),299.99,Core-m,"12.3""""",1920×1280 (16:9)
23 | 22,tablets,Chuwi®,CoreBook,299.99,Core-m ,"13.3""""",1920×1080 (16:9)
24 | 23,tablets,Microsoft,Surface Pro (2017) ,299.99,Core-m/-i,"12.3""""",2736×1824 (3:2)
25 | 24,tablets,Huawai,Matebook E ,299.99,Core-m,"12.0""""",2160×1440 (3:2)
26 | 25,tablets,Acer,Switch 5 ,299.99,Core-i,"12.0""""",2160×1440 (3:2)
27 | 26,tablets,Porsche Design,Book One,299.99,Core-i,"13.3""""",3200×1800 (16:9)
28 | 27,tablets,HP®,Spectre x2 (2017) ,299.99,Core-i,"12.3""""",3000×2000 (3:2)
29 | 28,tablets,HP®,Elite x2 1012 G2 ,299.99,Core-i,"12.3""""",2736×1824 (3:2)
30 | 29,tablets,HP®,Pro x2 612 G2 ,299.99,Core-m,"12.0""""",1920×1280 (3:2)
31 | 30,tablets,Samsung,"""Galaxy Book 12"""" """,299.99,Core-i,"12.0""""",2160×1440 (3:2)
32 | 31,tablets,Samsung,"""Galaxy Book 10.6"""" """,299.99,Core-m,"10.6""""",1920×1280 (3:2)
33 | 32,tablets,Fujitsu,Stylistic Q737 ,299.99,Core-i,"13.3""""",1920×1080 (16:9)
34 | 33,tablets,Dell,Latitude 7285 2-in-1 ,299.99,Core-m,12.3”,2880×1920 (3:2)
35 | 34,tablets,Dell,Latitude 5285 2-in-1 ,299.99,Core-i,"""12.3"""" """,920×1280 (3:2)
36 | 35,tablets,ASUS,Transformer Pro (T304) ,299.99,Core-i,"12.6""""",2160×1440 (3:2)
37 | 36,tablets,Lenovo®,Miix 720 ,299.99,Core-i,"12.0""""",2880×1920 (3:2)
38 | 37,tablets,Lenovo®,Thinkpad X1 Tablet (2017) ,299.99,Core-m,"""12.0"""" """,2160×1440 (3:2)
39 | 38,tablets,Cube®,Mix Plus ,299.99,Core-m,"10.6""""",1920×1080 (16:9)
40 | 39,tablets,Eve ,V (Project Pyramid Flipper) ,299.99,Core-m,"""12.3"""" """,2736×1824 (3:2)
41 | 40,tablets,Fujitsu,Stylistic R727 ,299.99,Core-i,"""12.5"""" """,1920×1080 (16:9)
42 |
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/Notebooks/01 - Databricks Overview.scala:
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1 | // Databricks notebook source
2 | // MAGIC %md-sandbox
3 | // MAGIC
4 | // MAGIC
5 | // MAGIC
11 |
12 | // COMMAND ----------
13 |
14 | // MAGIC %md
15 | // MAGIC ## What is Azure Databricks?
16 | // MAGIC
17 | // MAGIC ####Azure Databricks is Unified Analytics Platform for Data Engineers, Data Scientists, and Analysis
18 | // MAGIC
19 | // MAGIC 
20 |
21 | // COMMAND ----------
22 |
23 | // MAGIC %md
24 | // MAGIC
25 | // MAGIC # A Gentle Introduction to Apache Spark on Azure Databricks
26 | // MAGIC
27 | // MAGIC ** Welcome to Azure Databricks! **
28 | // MAGIC
29 | // MAGIC This notebook is intended to be the first step in your process to learn more about how to best use Apache Spark on Azure Databricks together. We'll be walking through the core concepts, the fundamental abstractions, and the tools at your disposal.
30 | // MAGIC
31 | // MAGIC First, it's worth defining Databricks. Databricks is a managed platform for running Apache Spark - that means that you do not have to learn complex cluster management concepts nor perform tedious maintenance tasks to take advantage of Spark.
32 | // MAGIC
33 | // MAGIC Databricks also provides a host of features to help its users be more productive with Spark. It's a point and click platform for those that prefer a user interface like data scientists or data analysts. However, this UI is accompanied by a sophisticated API for those that want to automate aspects of their data workloads with automated jobs. To meet the needs of enterprises, Azure Databricks also includes features such as role-based access control, integration with Azure Active Directory and other intelligent optimizations that not only improve usability for users but also reduce costs and complexity for administrators.
34 | // MAGIC
35 | // MAGIC ## Databricks Terminology
36 | // MAGIC
37 | // MAGIC Databricks has key concepts that are worth understanding. You'll notice that many of these line up with the links and icons that you'll see on the left side. These together define the fundamental tools that Databricks provides to you as an end user. They are available both in the web application UI as well as the REST API.
38 | // MAGIC
39 | // MAGIC - ****Workspaces****
40 | // MAGIC - Workspaces allow you to organize all the work that you are doing on Databricks. Like a folder structure in your computer, it allows you to save ****notebooks**** and ****libraries**** and share them with other users. Workspaces are not connected to data and should not be used to store data. They're simply for you to store the ****notebooks**** and ****libraries**** that you use to operate on and manipulate your data with.
41 | // MAGIC - ****Notebooks****
42 | // MAGIC - Notebooks are a set of any number of cells that allow you to execute commands. Cells hold code in any of the following languages: `Scala`, `Python`, `R`, `SQL`, or `Markdown`. Notebooks have a default language, but each cell can have a language override to another language. This is done by including `%[language name]` at the top of the cell. For instance `%python`. We'll see this feature shortly.
43 | // MAGIC - Notebooks need to be connected to a ****cluster**** in order to be able to execute commands however they are not permanently tied to a cluster. This allows notebooks to be shared via the web or downloaded onto your local machine.
44 | // MAGIC
45 | // MAGIC - ****Dashboards****
46 | // MAGIC - ****Dashboards**** can be created from ****notebooks**** as a way of displaying the output of cells without the code that generates them.
47 | // MAGIC - ****Notebooks**** can also be scheduled as ****jobs**** in one click either to run a data pipeline, update a machine learning model, or update a dashboard.
48 | // MAGIC - ****Libraries****
49 | // MAGIC - Libraries are packages or modules that provide additional functionality that you need to solve your business problems. These may be custom written Scala or Java jars; python eggs or custom written packages. You can write and upload these manually or you may install them directly via package management utilities like pypi or maven.
50 | // MAGIC - ****Tables / SQL ****
51 | // MAGIC - Tables are structured data that you and your team will use for analysis. Tables can exist in several places. Tables can be stored in cloud storage, they can be stored on the cluster that you're currently using, or they can be cached in memory. [For more about tables see the documentation](https://docs.cloud.databricks.com/docs/latest/databricks_guide/index.html#02%20Product%20Overview/07%20Tables.html).
52 | // MAGIC - ****Clusters****
53 | // MAGIC - Clusters are groups of computers that you treat as a single computer. In Databricks, this means that you can effectively treat 20 computers as you might treat one computer. Clusters allow you to execute code from ****notebooks**** or ****libraries**** on set of data. That data may be raw data located on cloud storage or structured data that you uploaded as a ****table**** to the cluster you are working on.
54 | // MAGIC - It is important to note that clusters have access controls to control who has access to each cluster.
55 | // MAGIC - ****Jobs****
56 | // MAGIC - Jobs are the tool by which you can schedule execution to occur either on an already existing ****cluster**** or a cluster of its own. These can be ****notebooks**** as well as jars or python scripts. They can be created either manually or via the REST API.
57 | // MAGIC
58 | // MAGIC
59 | // MAGIC
60 |
61 | // COMMAND ----------
62 |
63 | // MAGIC %md ## Databricks and Apache Spark Help Resources
64 | // MAGIC
65 | // MAGIC Databricks comes with a variety of tools to help you learn how to use Databricks and Apache Spark effectively. Databricks holds the greatest collection of Apache Spark documentation available anywhere on the web. There are two fundamental sets of resources that we make available: resources to help you learn how to use Apache Spark and Databricks and resources that you can refer to if you already know the basics.
66 | // MAGIC
67 | // MAGIC To access these resources at any time, click the question mark button at the top right-hand corner.
68 | // MAGIC
69 | // MAGIC 
70 | // MAGIC
71 | // MAGIC - ****The Databricks Documentation****
72 | // MAGIC - The Databricks Guide is the definitive reference for you and your team once you've become accustomed to using and leveraging Apache Spark. It allows for quick reference of common Databricks and Spark APIs with snippets of sample code.
73 | // MAGIC - The Guide also includes a series of tutorials (including this one!) that provide a more guided introduction to a given topic.
74 | // MAGIC - ****The Apache Spark Documentation****
75 | // MAGIC - [The Apache Spark open source documentation](http://spark.apache.org/docs/latest/sql-programming-guide.html) is also made available for quick and simple search if you need to dive deeper into some of the internals of Apache Spark.
76 | // MAGIC - ****For this class - Slack****
77 | // MAGIC - [Join the Slack Workspace](https://tinyurl.com/adb-nyc) - We will use this for Q&A during the sessions. The TAs will monitor the #general channel
78 |
79 | // COMMAND ----------
80 |
81 | // MAGIC %md ## Apache Spark Architecture
82 | // MAGIC
83 | // MAGIC Before proceeding with our example, let's see an overview of the Apache Spark architecture. As mentioned before, Apache Spark allows you to treat many machines as one machine and this is done via a master-worker type architecture where there is a `driver` or master node in the cluster, accompanied by `worker` nodes. The master sends work to the workers and either instructs them to pull to data from memory or from disk (or from another data source).
84 | // MAGIC
85 | // MAGIC The diagram below shows an example Apache Spark cluster, basically there exists a Driver node that communicates with executor nodes. Each of these executor nodes have slots which are logically like execution cores.
86 | // MAGIC
87 | // MAGIC 
88 | // MAGIC
89 | // MAGIC The Driver sends Tasks to the empty slots on the Executors when work has to be done:
90 | // MAGIC
91 | // MAGIC 
92 |
93 | // COMMAND ----------
94 |
95 | // MAGIC %md ### The Data Interfaces
96 | // MAGIC
97 | // MAGIC There are several key interfaces that you should understand when you go to use Spark.
98 | // MAGIC
99 | // MAGIC - ****The Dataset****
100 | // MAGIC - The Dataset is Apache Spark's newest distributed collection and can be considered a combination of DataFrames and RDDs. It provides the typed interface that is available in RDDs while providing a lot of conveniences of DataFrames. It will be the core abstraction going forward.
101 | // MAGIC - ****The DataFrame****
102 | // MAGIC - The DataFrame is collection of distributed `Row` types. These provide a flexible interface and are similar in concept to the DataFrames you may be familiar with in python (pandas) as well as in the R language.
103 | // MAGIC - ****The RDD (Resilient Distributed Dataset)****
104 | // MAGIC - Apache Spark's first abstraction was the RDD or Resilient Distributed Dataset. Essentially it is an interface to a sequence of data objects that consist of one or more types that are located across a variety of machines in a cluster. RDD's can be created in a variety of ways and are the "lowest level" API available to the user. While this is the original data structure made available, new users should focus on Datasets as those will be supersets of the current RDD functionality.
105 |
106 | // COMMAND ----------
107 |
108 | // MAGIC %md
109 | // MAGIC ## Databricks Runtime & Spark Clusters
110 | // MAGIC
111 | // MAGIC ####Azure Databricks is designed for Azure! This means:
112 | // MAGIC * Decoupling Storage and Compute
113 | // MAGIC * Ephemerial Clusters
114 | // MAGIC * Multiple Clusters
115 | // MAGIC * Autoscaling / Serverless
116 | // MAGIC
117 | // MAGIC ####Azure Databricks Clusters:
118 | // MAGIC
119 | // MAGIC * Clusters Spin up in minutes (~5min)!
120 | // MAGIC * Two types of clusters:
121 | // MAGIC * Interactive (shared)
122 | // MAGIC * Job Clusters
123 | // MAGIC * Interactive clusters can also be Azure Databricks Serverless Pools
124 | // MAGIC * Cluster ACLs
125 | // MAGIC * SQL Endpoints (JDBC/ODBC) for Power BI, Tableau, etc.
126 | // MAGIC
127 | // MAGIC ####Serverless
128 | // MAGIC * Driver Fault Issolation
129 | // MAGIC * Preemption
130 | // MAGIC * Query Watch Dog
131 | // MAGIC * SQL Data ACLs
132 | // MAGIC * Optimal Spark Version
133 | // MAGIC * Optimal Spark configuration for sharing
134 | // MAGIC
135 | // MAGIC ####Let's Connect to a Cluster!
136 | // MAGIC
137 | // MAGIC 
138 |
139 | // COMMAND ----------
140 |
141 | // MAGIC %md
142 | // MAGIC
143 | // MAGIC %md
144 | // MAGIC ## Databricks Runtime Optimizations
145 | // MAGIC
146 | // MAGIC * Databricks Delta enabled
147 | // MAGIC * Multiple Version of Spark (ephemerial Clusters)
148 | // MAGIC * Fine grained data controls
149 | // MAGIC * Optimized Resource sharing
150 | // MAGIC * Fault isolations of shared resources
151 | // MAGIC * Data Skipping
152 | // MAGIC * Runtime Optimizations during joins and filters
153 | // MAGIC * Rapid release cycles
154 | // MAGIC * Autoscaling
155 | // MAGIC * MLFlow hosted and preconfigured
156 |
157 | // COMMAND ----------
158 |
159 | // MAGIC %md
160 | // MAGIC ## Databricks Workspace & Notebooks
161 | // MAGIC
162 | // MAGIC * Interactive notebooks with support for multiple languages (SQL, Python, R and Scala)
163 | // MAGIC * Real-time collaboration
164 | // MAGIC * Notebook revision history
165 | // MAGIC * One-click visualizations
166 | // MAGIC * Workspace ACLs
167 | // MAGIC * Library Management
168 | // MAGIC
169 | // MAGIC
170 | // MAGIC 
171 |
172 | // COMMAND ----------
173 |
174 | // MAGIC %md-sandbox
175 | // MAGIC © 2018 Databricks, Inc. All rights reserved.
176 | // MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
177 | // MAGIC
178 | // MAGIC Privacy Policy | Terms of Use | Support
--------------------------------------------------------------------------------
/Notebooks/02 - Introduction to Notebooks.py:
--------------------------------------------------------------------------------
1 | # Databricks notebook source
2 | # MAGIC %md
3 | # MAGIC 
4 |
5 | # COMMAND ----------
6 |
7 | # MAGIC %md
8 | # MAGIC
9 | # MAGIC © 2019 BlueGranite, Inc. All rights reserved.
10 |
11 | # COMMAND ----------
12 |
13 | # MAGIC %md
14 | # MAGIC
15 | # MAGIC # Notebook Introduction
16 | # MAGIC Each notebook has a default language that was selected when the notebook was created. It can not be changed.
17 | # MAGIC
18 | # MAGIC This is a python notebook.
19 | # MAGIC
20 | # MAGIC Notebooks can be created as
21 | # MAGIC - Python
22 | # MAGIC - Scala
23 | # MAGIC - SQL
24 | # MAGIC - R
25 |
26 | # COMMAND ----------
27 |
28 | #python code can be executed in a cell
29 | result = 1 + 1
30 | print(result)
31 |
32 | # COMMAND ----------
33 |
34 | # MAGIC %md
35 | # MAGIC
36 | # MAGIC Execute the previous cell by clicking the small **Play** button in the upper right corner. *NOTE: You may have to click the cell, then hover your mouse in the upper right corner for the menu to appear*
37 | # MAGIC
38 | # MAGIC You can also execute a cell using Keyboard Shortcuts
39 | # MAGIC
40 | # MAGIC - CTRL + Enter = Run Command in Cell
41 | # MAGIC - SHIFT + Enter = Run Command in Cell then Move to Next Cell
42 |
43 | # COMMAND ----------
44 |
45 | # MAGIC %md
46 | # MAGIC # Cell Magic
47 | # MAGIC
48 | # MAGIC You can change the language of any inidivial cell regardless of the notebook's default language
49 |
50 | # COMMAND ----------
51 |
52 | # MAGIC %sql
53 | # MAGIC
54 | # MAGIC SELECT 42 AS TheAnswer;
55 |
56 | # COMMAND ----------
57 |
58 | # MAGIC %scala
59 | # MAGIC val x = 1
60 | # MAGIC val y = x + x
61 | # MAGIC y
62 |
63 | # COMMAND ----------
64 |
65 | # MAGIC %fs
66 | # MAGIC
67 | # MAGIC ls /mnt
68 |
69 | # COMMAND ----------
70 |
71 | # MAGIC %sh
72 | # MAGIC
73 | # MAGIC ps -a
74 |
75 | # COMMAND ----------
76 |
77 | # MAGIC %md
78 | # MAGIC # Workflow Execution
79 | # MAGIC
80 | # MAGIC You can call one notebook from another to create a workflow
81 |
82 | # COMMAND ----------
83 |
84 | # MAGIC %run ../../Admin/PreWorkshopPrep
85 |
86 | # COMMAND ----------
87 |
88 | # MAGIC %md
89 | # MAGIC
90 | # MAGIC # Data Profiling
91 | # MAGIC In addition to developing code in multiple languages, Databricks notebooks are also great data profiling tools.
92 |
93 | # COMMAND ----------
94 |
95 | # MAGIC %sql DESCRIBE airlines.sampleflights
96 |
97 | # COMMAND ----------
98 |
99 | # MAGIC %sql SELECT actualelapsedtime FROM airlines.sampleflights
--------------------------------------------------------------------------------
/Notebooks/07 - Model Hosting with AzureML and MLFlow.py:
--------------------------------------------------------------------------------
1 | # Databricks notebook source
2 | # MAGIC %md-sandbox
3 | # MAGIC
4 | # MAGIC
5 | # MAGIC
11 |
12 | # COMMAND ----------
13 |
14 | # MAGIC %md
15 | # MAGIC
16 | # MAGIC Modfications and MLFlow examples added 2019 by Josh Fennessy, BlueGranite https://www.blue-granite.com
17 |
18 | # COMMAND ----------
19 |
20 | #setup connection to the Azure ML workspace
21 | import azureml
22 | from azureml.core import Workspace
23 |
24 | workspace_name = ""
25 | workspace_location=""
26 | resource_group = ""
27 | subscription_id = ""
28 |
29 | workspace = Workspace.create(name = workspace_name,
30 | location = workspace_location,
31 | resource_group = resource_group,
32 | subscription_id = subscription_id,
33 | exist_ok=True)
34 |
35 | # COMMAND ----------
36 |
37 | # MAGIC %md
38 | # MAGIC
39 | # MAGIC Next we need to use `run_id` field of the MLFlow Tracking portal to identify the version of the model we with to deploy.
40 | # MAGIC
41 | # MAGIC This model version will be packaged into a Docker container with the following code example.
42 | # MAGIC
43 | # MAGIC *Note: You can modify the **conda.yaml** file in the model directory to indclude additional dependencies and software packages that might be needed to execute your model in a container
44 |
45 | # COMMAND ----------
46 |
47 | # MAGIC %md
48 | # MAGIC
49 | # MAGIC ###Create a new Docker Image based on the MLFlow model
50 | # MAGIC
51 | # MAGIC *Note: images can take time to create. Plan on 10 minutes to create the image from this model*
52 |
53 | # COMMAND ----------
54 |
55 | import mlflow.azureml
56 |
57 |
58 | run_id = ""
59 |
60 | #build the URI to download the model details
61 | model_uri = "runs:/" + run_id + "/model"
62 |
63 | #enter a desired model description
64 | model_description = ""
65 | model_image, azure_model = mlflow.azureml.build_image(model_uri=model_uri,
66 | workspace=workspace,
67 | model_name="model",
68 | image_name="model",
69 | description=model_description,
70 | synchronous=False)
71 |
72 | #wait for the model to be created
73 | model_image.wait_for_creation(show_output=True)
74 |
75 | # COMMAND ----------
76 |
77 | # MAGIC %md
78 | # MAGIC
79 | # MAGIC ### Option 2: Connected to a previously created image
80 |
81 | # COMMAND ----------
82 |
83 | import mlflow.azureml
84 | from azureml.core import Image
85 | from azureml.core.image import ContainerImage
86 |
87 | #enter the image name from the Azure ML workspace
88 | image_name = "model"
89 |
90 | #retrieve the image configuration
91 | model_image = Image(workspace, name=image_name)
92 |
93 | # COMMAND ----------
94 |
95 | # #for DEV deployments, Azure Container Instances
96 |
97 | from azureml.core.webservice import AciWebservice, Webservice
98 |
99 | dev_webservice_name = "skurecs-dev"
100 | dev_webservice_deployment_config = AciWebservice.deploy_configuration()
101 | dev_webservice = Webservice.deploy_from_image(name=dev_webservice_name, image=model_image, deployment_config=dev_webservice_deployment_config, workspace=workspace)
102 |
103 | dev_webservice.wait_for_deployment()
104 |
105 | # COMMAND ----------
106 |
107 | #call the service via a webservice to score the results
108 | import requests
109 | import json
110 |
111 | def query_endpoint_example(scoring_uri, inputs, service_key=None):
112 | headers = {
113 | "Content-Type": "application/json",
114 | }
115 | if service_key is not None:
116 | headers["Authorization"] = "Bearer {service_key}".format(service_key=service_key)
117 |
118 | print("Sending batch prediction request with inputs: {}".format(inputs))
119 | response = requests.post(scoring_uri, data=json.dumps(inputs), headers=headers)
120 | preds = json.loads(response.text)
121 | print("Received response: {}".format(preds))
122 | return preds
123 |
124 | ratings_pd = my_ratings_df.toPandas()
125 |
126 | print(ratings_pd)
127 |
128 | sample = ratings_pd.iloc[[0]]
129 |
130 | query_input = sample.to_json(orient='split')
131 | query_input = eval(query_input)
132 | # query_input.pop('index', None)
133 | print(query_input)
134 |
135 | # COMMAND ----------
136 |
137 | # MAGIC %md
138 | # MAGIC
139 | # MAGIC To deploy an image, we need to create a new or connect to an existing Azure Kubernetes Service compute target.
140 |
141 | # COMMAND ----------
142 |
143 | # MAGIC %md
144 | # MAGIC ### Create a New AKS Cluster
145 | # MAGIC
146 | # MAGIC *Note: It will take a few minutes to create a new AKS cluster*
147 |
148 | # COMMAND ----------
149 |
150 | from azureml.core.compute import AksCompute, ComputeTarget
151 |
152 | # Use the default configuration (you can also provide parameters to customize this)
153 | prov_config = AksCompute.provisioning_configuration()
154 |
155 | aks_cluster_name = ""
156 |
157 | # Create the cluster
158 | aks_target = ComputeTarget.create(workspace = workspace,
159 | name = aks_cluster_name,
160 | provisioning_configuration = prov_config)
161 |
162 | # Wait for the create process to complete
163 | aks_target.wait_for_completion(show_output = True)
164 | print(aks_target.provisioning_state)
165 | print(aks_target.provisioning_errors)
166 |
167 | # COMMAND ----------
168 |
169 | # MAGIC %md
170 | # MAGIC ## Connect to an existing AKS Cluster
171 |
172 | # COMMAND ----------
173 |
174 | from azureml.core.compute import AksCompute, ComputeTarget
175 |
176 | # Get the resource group from https://portal.azure.com -> Find your resource group
177 | resource_group = ""
178 |
179 | # Give the cluster a local name
180 | aks_cluster_name = ""
181 |
182 | # Attatch the cluster to your workgroup
183 | attach_config = AksCompute.attach_configuration(resource_group=resource_group, cluster_name=aks_cluster_name)
184 | aks_target = ComputeTarget.attach(workspace, name=aks_cluster_name, attach_configuration=attach_config)
185 |
186 | # Wait for the operation to complete
187 | aks_target.wait_for_completion(True)
188 | print(aks_target.provisioning_state)
189 | print(aks_target.provisioning_errors)
190 |
191 | # COMMAND ----------
192 |
193 | # MAGIC %md
194 | # MAGIC
195 | # MAGIC With the AKS cluster created and connected to, the image can now be deployed as a web service.
196 | # MAGIC
197 | # MAGIC The code in the following cell will deploy the model selected from MLFlow in the cells above
198 |
199 | # COMMAND ----------
200 |
201 | # MAGIC %md
202 | # MAGIC ## Create a new web service deployment from a model image
203 |
204 | # COMMAND ----------
205 |
206 | from azureml.core.webservice import Webservice, AksWebservice
207 |
208 | # Set configuration and service name
209 | webservice_name = ""
210 |
211 | #default configuration can be modified for custom requirements
212 | webservice_deployment_config = AksWebservice.deploy_configuration()
213 |
214 | # Deploy from image selected above
215 | webservice = Webservice.deploy_from_image(workspace = workspace,
216 | name = webservice_name,
217 | image = model_image,
218 | deployment_config = webservice_deployment_config,
219 | deployment_target = aks_target)
220 |
221 | #wait for the webservice to be deployed
222 | webservice.wait_for_deployment(show_output = True)
223 |
224 | # COMMAND ----------
225 |
226 | print(webservice.get_logs())
227 |
228 | # COMMAND ----------
229 |
230 | # MAGIC %md if you receive an error deploying your image as a webservice, you can use the following code to get information from the image creation logs
231 |
232 | # COMMAND ----------
233 |
234 | # MAGIC %md
235 | # MAGIC
236 | # MAGIC ## Connect to an existing Web Service in Azure ML
237 |
238 | # COMMAND ----------
239 |
240 | from azureml.core.webservice import Webservice
241 |
242 | webservice_name = ""
243 |
244 | #connect to an existing web service
245 | webservice = Webservice(name=webservice_name, workspace=workspace)
246 |
247 | # COMMAND ----------
248 |
249 | # MAGIC %md
250 | # MAGIC With the webservice succesfully deployed, a test call to the API can be executed.
251 | # MAGIC
252 | # MAGIC Further integration with web and mobile apps is now possible using standard web technolgies. Because the model is hosted in Azure Kubernetes Services, scale is not an issue. The AKS cluster will automatically react to increased or decreased demand.
253 |
254 | # COMMAND ----------
255 |
256 | import requests
257 | import json
258 |
259 | #this function calls an Azure ML hosted webservice and returns the predicted results
260 | def query_endpoint_example(scoring_uri, inputs, service_key=None):
261 | headers = {
262 | "Content-Type": "application/json",
263 | }
264 | if service_key is not None:
265 | headers["Authorization"] = "Bearer {service_key}".format(service_key=service_key)
266 |
267 | # print ("Headers: {}") #don't show headers if we are going to save a key or other authentication
268 | print("Sending batch prediction request with inputs: {}".format(inputs))
269 | response = requests.post(scoring_uri, data=json.dumps(inputs), headers=headers)
270 | preds = json.loads(response.text)
271 | print("Received response: {}".format(preds))
272 | return preds
273 |
274 | #AzureML requires the input data to be in a Pandas data format
275 | #that has been converted to JSON using the **split** orientation
276 |
277 | #grab a record from the test data frame to test calling for a single score
278 | scoring_df = test_df.toPandas().iloc[[18]]
279 |
280 | query_input = scoring_df.to_json(orient='split')
281 | query_input = eval(query_input)
282 | query_input.pop('index', None)
283 |
284 | #collect values needed to call the service
285 | webserviceURI = webservice.scoring_uri
286 | service_auth_key = webservice.get_keys()[0] if len(webservice.get_keys()) > 0 else None #note: there are more secure ways to do this
287 |
288 | result = query_endpoint_example(scoring_uri=webserviceURI, inputs=query_input, service_key=service_auth_key)
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/Notebooks/Solutions/03 - Data Engineering 1 Answers.py:
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1 | # Databricks notebook source
2 | # DBTITLE 1,Exercise 1 (answer)
3 | #Answer the following questions about the file
4 |
5 | # 1.) What is the delimiter?
6 | # comma (,)
7 | #
8 | # 2.) Does the file have headers?
9 | # Yes
10 | #
11 | # 3.) Are there quoted strings in the file?
12 | # Yes. quoted and escaped (")
13 | #
14 |
15 | # COMMAND ----------
16 |
17 | # DBTITLE 1,Exercise 2 (answer)
18 | # A reference to our csv file
19 | inPath = "/mnt/training-sources/initech/productsCsv/"
20 |
21 | productDF = (
22 | spark.read # The DataFrameReader
23 | .option("header", True)
24 | .option("inferSchema", True)
25 | .option("quote", "\"") #bonus
26 | .option("escape", "\"") #bonus
27 | .csv(inPath) # Creates a DataFrame from CSV after reading in the file
28 | )
29 |
30 | # COMMAND ----------
31 |
32 | # DBTITLE 1,Exercise 3 (answer)
33 | # complete the following code to create the products temporary view
34 |
35 | tmpViewName = "userXX_products" #replace "XX" in this variable name with your user number
36 | productDF.createOrReplaceTempView(tmpViewName) # Edit this line to create the temporary view
37 |
38 | # COMMAND ----------
39 |
40 | # DBTITLE 1,Exercise 4a (answer)
41 | # MAGIC %sql
42 | # MAGIC
43 | # MAGIC -- How many products are found in the data file?
44 | # MAGIC SELECT COUNT(1) AS NumProducts FROM userXX_products;
45 |
46 | # COMMAND ----------
47 |
48 | # DBTITLE 1,Exercise 4b (answer)
49 | # MAGIC %sql
50 | # MAGIC
51 | # MAGIC -- How much is the least expensive product? How much is the most expensive? What is the average product price?
52 | # MAGIC
53 | # MAGIC SELECT MIN(price) AS minPrice, MAX(price) AS maxPrice, AVG(price) AS avgPrice FROM userXX.products;
54 |
55 | # COMMAND ----------
56 |
57 | # DBTITLE 1,Exercise 4c (answer)
58 | # MAGIC %sql
59 | # MAGIC
60 | # MAGIC -- How many Microsoft models are in the data and what is their average price?
61 | # MAGIC
62 | # MAGIC SELECT
63 | # MAGIC COUNT (DISTINCT model) AS modelCount,
64 | # MAGIC AVG(price) as avgPrice
65 | # MAGIC FROM
66 | # MAGIC userXX_products
67 | # MAGIC WHERE
68 | # MAGIC brand = 'Microsoft'
69 | # MAGIC GROUP BY
70 | # MAGIC brand
71 |
72 | # COMMAND ----------
73 |
74 | # DBTITLE 1,Exercise 5 (answer)
75 | # MAGIC %sql
76 | # MAGIC --Hint: Use IF NOT EXISTS to prevent errors when creating a database that may already exist
77 | # MAGIC CREATE DATABASE IF NOT EXISTS userXX;
78 |
79 | # COMMAND ----------
80 |
81 | # DBTITLE 1,Exercise 6 (answer)
82 | # edit the following code to write data to your table
83 | locationName = "/data/userXX/products"
84 | tableName = "products"
85 |
86 | (
87 | productDF.write
88 | .option("location", locationName)
89 | .mode("overwrite")
90 | .saveAsTable("userXX.products")
91 | )
--------------------------------------------------------------------------------
/Notebooks/Solutions/04 - Data Engineering 2 Answers.py:
--------------------------------------------------------------------------------
1 | # Databricks notebook source
2 | # DBTITLE 1,Exercise 1 (answer)
3 | # edit the following code to create the histOrders dataframe
4 |
5 | histOrders = (
6 | spark.read
7 | .format("parquet")
8 | .load("/mnt/training-sources/initech/orders")
9 | )
10 |
11 |
12 | # COMMAND ----------
13 |
14 | # DBTITLE 1,Exercise 2 (answer)
15 | #edit this code to read the data in the products table
16 | products = (
17 | spark.read.table("userXX.products")
18 | )
19 |
20 | # COMMAND ----------
21 |
22 | # DBTITLE 1,Exercise 3 (answer)
23 | from pyspark.sql.functions import *
24 | from pyspark.sql.types import *
25 | #complete the code below to join histOrders and products
26 | enrichedOrders = histOrders.join(products, histOrders.ProductID == products.product_id)
27 |
28 | # COMMAND ----------
29 |
30 | # DBTITLE 1,Exercise 4 (answer)
31 | #edit the code below to create the the data frame with the specified columns
32 | projectedOrders = enrichedOrders.select("rowguid", "ModifiedDate", "brand", "category", "model", "price", "OrderQty", "UnitPriceDiscount")
33 |
34 | #we will also accept the more verbose (but perhapse more flexible)
35 | cols = ["rowguid", "ModifiedDate", "brand", "category", "model", "price", "OrderQty", "UnitPriceDiscount"]
36 | projectedOrders = enrichedOrders.select(cols)
37 |
38 | # COMMAND ----------
39 |
40 | # DBTITLE 1,Exercise 5 (answer)
41 | #Edit the code below to create the finalOrders
42 | finalOrders = projectedOrders.withColumn("TotalLineItemAmt", col("OrderQty") * ((1 - col("UnitPriceDiscount")) * col("price")))
43 | # display(finalOrders)
44 |
45 |
46 | # COMMAND ----------
47 |
48 | # DBTITLE 1,Exercise 6 (answer)
49 | locationName = "/data/userXX/orders" #edit the userXX database to match your user number
50 | tableName = "userxx.orders" #replace xx with your user number
51 |
52 | (
53 | finalOrders #add the correct parameters to adjust the location and table name
54 | .write
55 | .format("delta")
56 | .mode("overwrite")
57 | .option("location", locationName)
58 | .saveAsTable(tableName)
59 | )
60 |
61 | # COMMAND ----------
62 |
63 | # MAGIC %sql SELECT * FROM userxx.orders
64 |
65 | # COMMAND ----------
66 |
67 | # MAGIC %sql
68 | # MAGIC OPTIMIZE userxx.orders ZORDER BY (rowguid);
--------------------------------------------------------------------------------
/Notebooks/Solutions/05 - Data Engineering 3 Answers.py:
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1 | # Databricks notebook source
2 | # DBTITLE 1,Exercise 1a (answer)
3 | #this is the path to the real-time orders feed.
4 | #files are stored in Avro format and are found in a deep recursive directory structure
5 |
6 | #spark allows wildcards in a path, which will instruct Spark to traverse all paths at that level
7 | ordersInputPath = "dbfs:/mnt/training-sources/bgdbxworkshop/initechorders/*/*/*/*/*/*/*.avro"
8 |
9 |
10 | #the schema property of the data frame can be used to set schema in another data frame definition
11 | #enter the correct **format** and **location** to read the data into a batch data frame and extract the schema
12 | messageSchema = spark.read.format("avro").load(ordersInputPath).schema
13 |
14 | # COMMAND ----------
15 |
16 | # DBTITLE 1,Exercise 1b (answer)
17 | #edit the code below to configure the connection to the real-time orders
18 | #hint: Remember the files are stored in Avro format
19 | ordersInputStream = (
20 | spark.readStream
21 | .schema(messageSchema) \
22 | .option("maxFilesPerTrigger", 1)
23 | .format("avro")
24 | .load(ordersInputPath)
25 | )
26 |
27 | # COMMAND ----------
28 |
29 | # DBTITLE 1,Exercise 2 (answer)
30 | from pyspark.sql.types import *
31 | from pyspark.sql.functions import *
32 |
33 | #build the command to select the body of the message
34 | #and cast it to a string
35 |
36 | #Helpful functions:
37 | # select()
38 | # cast()
39 |
40 | ordersMessageStream = ordersInputStream.select(ordersInputStream.Body.cast("string"))
41 |
42 | # COMMAND ----------
43 |
44 | # DBTITLE 1,Exercise 3 (answer)
45 | #create the message schema
46 | #fill in the missing values
47 |
48 | from pyspark.sql.types import *
49 |
50 | orderMessageSchema = StructType([
51 | StructField("SalesOrderID", IntegerType()),
52 | StructField("ProductID", IntegerType()),
53 | StructField("rowguid", StringType()),
54 | StructField("OrderQty", IntegerType()),
55 | StructField("UnitPriceDiscount", FloatType()),
56 | StructField("ModifiedDate", StringType())
57 | ])
58 |
59 | #now construct the final data frame that will parse the message
60 | #use from_json to convert the string to a json structure
61 | #then select all of the attributes from that structure
62 | RealTimeOrdersStream = (
63 | ordersMessageStream
64 | .select(from_json(col("Body"), orderMessageSchema).alias("orders"))
65 | .select("orders.*")
66 | )
67 |
68 | #prepare the stream configuration and present as a Temporary View
69 | #this will enable data profiling with SQL
70 |
71 | #replace userXX with your user number
72 | RealTimeOrdersStream.createOrReplaceTempView("userXX_RealTimeOrders")
73 |
74 | # COMMAND ----------
75 |
76 | # DBTITLE 1,Exercise 5a (answer)
77 | #Complete the code below tocreate a data frame containing product attributes.
78 | #hint: product attributes are available in the userXX.products table
79 | products = spark.read.table("userxx.products")
80 |
81 | #now combine the raw orders with the product attributes
82 | joinedRealTimeOrders = RealTimeOrdersStream.join(products, RealTimeOrdersStream.ProductID == products.product_id)
83 |
84 | # COMMAND ----------
85 |
86 | # DBTITLE 1,Exercise 5b (answer)
87 | #Next, you'll need to modify the data frame to ensure that it contains only the
88 | #following fields, in this order
89 |
90 | projectedRealTimeOrders = joinedRealTimeOrders.select("rowguid", "ModifiedDate", "brand", "category", "model", "price", "OrderQty", "UnitPriceDiscount")
91 |
92 | # COMMAND ----------
93 |
94 | # DBTITLE 1,Exercise 5c (answer)
95 | # finally calcuate the [totalLineItemAmount] metric
96 | # the formula to calucate [totalLineItemAmount] is
97 | # ItemQty * ((1 - UnitPriceDiscount) * UnitPrice)
98 |
99 |
100 | RealTimeOrders = projectedRealTimeOrders.withColumn("TotalLineItemAmt", col("OrderQty") * ((1 - col("UnitPriceDiscount")) * col("price")))
101 |
102 | #review your work
103 | display(RealTimeOrders)
104 |
105 | #replace the temporary view created in Lab 1
106 | RealTimeOrders.createOrReplaceTempView("userXX_RealTimeOrders") #be sure to replace userXX with your user number
107 |
108 | # COMMAND ----------
109 |
110 | # DBTITLE 1,Exercise 6 (answer)
111 | #first we have to set up a helper function that will merge
112 | #each microbatch into the delta table
113 |
114 | def upsertToDelta(microBatchDF, batchId):
115 |
116 | #create a temp view based on the microbatch so
117 | #the MERGE sql command can be executed
118 | microBatchDF.createOrReplaceTempView("orderUpdates")
119 |
120 | #edit the merge statement below to update the correct orders
121 | sqlMERGECommand = """
122 | MERGE INTO userxx.orders dest USING orderUpdates src
123 | ON src.rowguid = dest.rowguid
124 | WHEN MATCHED THEN UPDATE SET *
125 | WHEN NOT MATCHED THEN INSERT *"""
126 |
127 | #execute the microbatch MEREGE using the context of the microbatch
128 | (
129 | microBatchDF._jdf.sparkSession()
130 | .sql(sqlMERGECommand)
131 | )
132 |
133 | #now that the helper function that executes the MERGE is created
134 | #begin writing the Stream to the Delta table.
135 | (
136 | RealTimeOrders
137 | .writeStream
138 | .foreachBatch(upsertToDelta)
139 | .outputMode("update")
140 | .start()
141 | )
142 |
143 | # COMMAND ----------
144 |
145 | # MAGIC %sql SELECT * FROM userxx.orders ORDER BY ModifiedDate DESC
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/Notebooks/ValidationTests/04 - Data Engineering 2 Validation.py:
--------------------------------------------------------------------------------
1 | # Databricks notebook source
2 | # DBTITLE 1,Lab 1 Exercise 1
3 | def testLab1Ex1(df):
4 | columns = df.dtypes
5 | assert len(columns) == 6, "Expected 6 columns but found " + str(len(columns))
6 |
7 | return ("Great work! All validation tests passed!")
8 |
9 | # COMMAND ----------
10 |
11 | # DBTITLE 1,Lab 1 Exercise 2
12 | def testLab1Ex2(df):
13 | columns = df.columns
14 | assert len(columns) == 8, "Expected 8 columns but found " + str(len(columns))
15 |
16 | assert "product_id" in columns, "Unable to find a 'product_id' field. Available fields are {0}".format(columns)
17 |
18 | return ("Great work! All validation tests passed!")
19 |
20 | # COMMAND ----------
21 |
22 | # DBTITLE 1,Lab 1 Exercise 3
23 | def testLab1Ex3(df):
24 | columns = df.columns
25 | assert len(columns) == 14, "Expected 14 columns but found " + str(len(columns))
26 |
27 | assert "price" in columns, "Unable to find a 'price' field. Available fields are {0}".format(columns)
28 |
29 | assert "brand" in columns, "Unable to find a 'brand' field. Available fields are {0}".format(columns)
30 |
31 | assert "model" in columns, "Unable to find a 'model' column. Available fields are {0}".format(columns)
32 |
33 |
34 | types = df.dtypes
35 |
36 | assert ("price", "double") in types, "'price' should be a double."
37 |
38 | return ("Great work! All validation tests passed!")
39 |
40 | # COMMAND ----------
41 |
42 | # DBTITLE 1,Lab 2 Exercise 1
43 | def testLab2Ex1(df):
44 | coltypes = df.dtypes
45 |
46 | assert coltypes[0][0] == "rowguid", "Expected column 0 to be \"rowguid\" but found \"" + coltypes[0][0] + "\"."
47 |
48 | assert coltypes[0][1] == "string", "Expected column 0 to be of type \"string\" but found \"" + coltypes[0][1] + "\"."
49 |
50 | assert coltypes[1][0] == "ModifiedDate", "Expected column 0 to be \"ModifiedDate\" but found \"" + coltypes[1][0] + "\"."
51 |
52 | assert coltypes[1][1] == "timestamp", "Expected column 0 to be of type \"timestamp\" but found \"" + coltypes[1][1] + "\"."
53 |
54 | assert coltypes[2][0] == "brand", "Expected column 1 to be \"brand\" but found \"" + coltypes[2][0] + "\"."
55 | assert coltypes[2][1] == "string", "Expected column 1 to be of type \"string\" but found \"" + coltypes[2][1] + "\"."
56 |
57 | assert coltypes[3][0] == "category", "Expected column 2 to be \"category\" but found \"" + coltypes[3][0] + "\"."
58 | assert coltypes[3][1] == "string", "Expected column 2 to be of type \"string\" but found \"" + coltypes[3][1] + "\"."
59 |
60 | assert coltypes[4][0] == "model", "Expected column 3 to be \"model\" but found \"" + coltypes[4][0] + "\"."
61 | assert coltypes[4][1] == "string", "Expected column 3 to be of type \"string\" but found \"" + coltypes[4][1] + "\"."
62 |
63 | assert coltypes[5][0] == "price", "Expected column 4 to be \"price\" but found \"" + coltypes[5][0] + "\"."
64 | assert coltypes[5][1] == "double", "Expected column 4 to be of type \"double\" but found \"" + coltypes[5][1] + "\"."
65 |
66 | assert coltypes[6][0] == "OrderQty", "Expected column 5 to be \"OrderQty\" but found \"" + coltypes[6][0] + "\"."
67 | assert coltypes[6][1] == "int", "Expected column 5 to be of type \"integer\" but found \"" + coltypes[6][1] + "\"."
68 |
69 | assert coltypes[7][0] == "UnitPriceDiscount", "Expected column 6 to be \"UnitPriceDiscount\" but found \"" + coltypes[7][0] + "\"."
70 | assert coltypes[7][1] == "float", "Expected column 6 to be of type \"float\" but found \"" + coltypes[7][1] + "\"."
71 |
72 | return ("Great work! All validation tests passed!")
73 |
74 | # COMMAND ----------
75 |
76 | # DBTITLE 1,Lab 3 Exercise 1
77 | def testLab3Ex1(df):
78 | columns = df.columns
79 | assert len(columns) == 9, "Expected 9 columns but found " + str(len(columns))
80 |
81 | coltypes = df.dtypes
82 |
83 | assert coltypes[8][0] == "TotalLineItemAmt", "Expected column 7 to be \"TotalLineItemAmt\" but found \"" + types[7][0] + "\"."
84 | assert coltypes[8][1] == "double", "Expected column 7 to be of type \"double\" but found \"" + types[7][1] + "\"."
85 |
86 | return ("Great work! All validation tests passed!")
87 |
88 | # COMMAND ----------
89 |
90 | # DBTITLE 1,Lab 4 Exercise 1
91 | def testLab4Ex1(tableName):
92 | rowcount = spark.sql("select * from {0}".format(tableName)).count()
93 |
94 | assert rowcount > 0, "Expected at least one row in {0} but found {1}".format(tableName, str(rowCount))
95 |
96 | return ("Great work! All validation tests passed!")
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/Notebooks/ValidationTests/05 - Data Engineering 3 Validation.py:
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1 | # Databricks notebook source
2 | # DBTITLE 1,Lab 1 Exercise 1
3 | def testLab1Ex1(df):
4 | columns = df.columns
5 | assert len(columns) == 6, "Expected 6 columns but found " + str(len(columns))
6 |
7 | coltypes = df.dtypes
8 | assert coltypes[5][0] == "Body", "Expected column 5 to be \"Body\" but found \"" + colTypes[5][0] + "\"."
9 | assert coltypes[5][1] == "binary", "Expected column 5 to be of type \"binary\" but found \"" + colTypes[5][1] + "\"."
10 |
11 | return ("Great work! All validation tests passed!")
12 |
13 | # COMMAND ----------
14 |
15 | # DBTITLE 1,Lab 1 Exercise 2
16 | def testLab1Ex2(df):
17 | columns = df.columns
18 | assert len(columns) == 1, "Expected 1 columns but found " + str(len(columns))
19 |
20 | coltypes = df.dtypes
21 | assert coltypes[0][0] == "Body", "Expected column 0 to be \"Body\" but found \"" + colTypes[5][0] + "\"."
22 | assert coltypes[0][1] == "string", "Expected column 0 to be of type \"string\" but found \"" + colTypes[5][1] + "\"."
23 |
24 | return ("Great work! All validation tests passed!")
25 |
26 | # COMMAND ----------
27 |
28 | # DBTITLE 1,Lab 1 Exercise 3
29 | def testLab1Ex3(df):
30 | coltypes = df.dtypes
31 |
32 | assert coltypes[0][0] == "SalesOrderID", "Expected column 0 to be \"SalesOrderID\" but found \"" + coltypes[0][0] + "\"."
33 |
34 | assert coltypes[0][1] == "int", "Expected column 0 to be of type \"int\" but found \"" + coltypes[0][1] + "\"."
35 |
36 | assert coltypes[1][0] == "ProductID", "Expected column 1 to be \"ProductID\" but found \"" + coltypes[1][0] + "\"."
37 | assert coltypes[1][1] == "int", "Expected column 1 to be of type \"int\" but found \"" + coltypes[1][1] + "\"."
38 |
39 | assert coltypes[2][0] == "rowguid", "Expected column 2 to be \"rowguid\" but found \"" + coltypes[2][0] + "\"."
40 | assert coltypes[2][1] == "string", "Expected column 2 to be of type \"string\" but found \"" + coltypes[2][1] + "\"."
41 |
42 | assert coltypes[3][0] == "OrderQty", "Expected column 3 to be \"OrderQty\" but found \"" + coltypes[3][0] + "\"."
43 | assert coltypes[3][1] == "int", "Expected column 3 to be of type \"int\" but found \"" + coltypes[3][1] + "\"."
44 |
45 | assert coltypes[4][0] == "UnitPriceDiscount", "Expected column 4 to be \"UnitPriceDiscount\" but found \"" + coltypes[4][0] + "\"."
46 | assert coltypes[4][1] == "float", "Expected column 4 to be of type \"float\" but found \"" + coltypes[4][1] + "\"."
47 |
48 | assert coltypes[5][0] == "ModifiedDate", "Expected column 5 to be \"ModifiedDate\" but found \"" + coltypes[5][0] + "\"."
49 | assert coltypes[5][1] == "string", "Expected column 5 to be of type \"string\" but found \"" + coltypes[5][1] + "\"."
50 |
51 | return ("Great work! All validation tests passed!")
52 |
53 | # COMMAND ----------
54 |
55 | # DBTITLE 1,Lab 2 Exercise 1
56 | def testLab2Ex1(df):
57 | coltypes = df.dtypes
58 |
59 | assert coltypes[0][0] == "rowguid", "Expected column 0 to be \"rowguid\" but found \"" + coltypes[0][0] + "\"."
60 |
61 | assert coltypes[0][1] == "string", "Expected column 0 to be of type \"string\" but found \"" + coltypes[0][1] + "\"."
62 |
63 | assert coltypes[1][0] == "ModifiedDate", "Expected column 1 to be \"ModifiedDate\" but found \"" + coltypes[1][0] + "\"."
64 |
65 | assert coltypes[1][1] == "string", "Expected column 1 to be of type \"string\" but found \"" + coltypes[1][1] + "\"."
66 |
67 | assert coltypes[2][0] == "brand", "Expected column 2 to be \"brand\" but found \"" + coltypes[2][0] + "\"."
68 | assert coltypes[2][1] == "string", "Expected column 2 to be of type \"string\" but found \"" + coltypes[2][1] + "\"."
69 |
70 | assert coltypes[3][0] == "category", "Expected column 3 to be \"category\" but found \"" + coltypes[3][0] + "\"."
71 | assert coltypes[3][1] == "string", "Expected column 3 to be of type \"string\" but found \"" + coltypes[3][1] + "\"."
72 |
73 | assert coltypes[4][0] == "model", "Expected column 4 to be \"model\" but found \"" + coltypes[4][0] + "\"."
74 | assert coltypes[4][1] == "string", "Expected column 4 to be of type \"string\" but found \"" + coltypes[4][1] + "\"."
75 |
76 | assert coltypes[5][0] == "price", "Expected column 5 to be \"price\" but found \"" + coltypes[5][0] + "\"."
77 | assert coltypes[5][1] == "double", "Expected column 5 to be of type \"double\" but found \"" + coltypes[5][1] + "\"."
78 |
79 | assert coltypes[6][0] == "OrderQty", "Expected column 6 to be \"OrderQty\" but found \"" + coltypes[6][0] + "\"."
80 | assert coltypes[6][1] == "int", "Expected column 6 to be of type \"integer\" but found \"" + coltypes[6][1] + "\"."
81 |
82 | assert coltypes[7][0] == "UnitPriceDiscount", "Expected column 7 to be \"UnitPriceDiscount\" but found \"" + coltypes[7][0] + "\"."
83 | assert coltypes[7][1] == "float", "Expected column 7 to be of type \"float\" but found \"" + coltypes[7][1] + "\"."
84 |
85 | assert coltypes[8][0] == "TotalLineItemAmt", "Expected column 8 to be \"TotalLineItemAmt\" but found \"" + coltypes[7][0] + "\"."
86 | assert coltypes[8][1] == "double", "Expected column 8 to be of type \"double\" but found \"" + coltypes[7][1] + "\"."
87 |
88 | return ("Great work! All validation tests passed!")
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/Other/labs2019-backup.dbc:
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https://raw.githubusercontent.com/BlueGranite/DatabricksTraining/eac959cd60d851418181f706f5313038b408d504/Other/labs2019-backup.dbc
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/README.md:
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1 | # BlueGranite Azure Databricks Training
2 |
3 | 
4 |
5 | For a list of BlueGranite-hosted Training Events, visit [blue-granite.com/events](https://www.blue-granite.com/events).
6 |
7 | Want to schedule training at your organization? [Contact Us](https://www.blue-granite.com/contact-us)!
8 |
9 | ## Training Materials
10 | This repository contains the notebooks used in the BlueGranite 1-day Hands On Databricks Workshop.
11 |
12 | To use these training materials in your own environment:
13 | 1. Import all of the notebooks found in the **notebooks/** directory of the repo.
14 | 2. Copy all of the data found in the **data/** directory to an Azure Blob Storage container or Azure Data Lake Store
15 | 3. Create a mount point in your Azure Databricks workpace pointing to the location of the data to **/mnt/training-sources**
16 |
17 | ## BlueGranite Resources
18 | - [__BlueGranite Databricks Blog Posts__](https://www.blue-granite.com/blog/topic/azure-databricks)
19 | - [__BlueGranite Webinar:__ Predicting Customer
20 | Churn using Azure Databricks](https://www.blue-granite.com/customer-churn-databricks-webinar-june-2019)
21 | - [__BlueGranite Webinar:__ Azure Databricks & Azure Data Warehouse: Better Together](https://www.blue-granite.com/azure-databricks-azure-data-warehouse-webinar)
22 |
23 | ## Additional Resources
24 | - [Azure Databricks Documentation](https://docs.azuredatabricks.net/)
25 | - [About Databricks on Azure](https://azure.microsoft.com/en-us/services/databricks/)
26 | - [Databricks Academy](https://academy.databricks.com/)
27 | - [MMLSpark](https://github.com/Azure/mmlspark)
28 | - [Azure Pass Redemption](https://www.microsoftazurepass.com/Home/HowTo)
29 |
30 |
31 | ## License
32 | Original Content © 2019 Databricks, Inc. All rights reserved.
33 |
34 |
35 |
36 | Modified for Azure Databricks by BlueGranite, Inc.
37 |
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