Data Engineering | Garage Education

Garage Education

Garage Education

Garage Education is a nonprofit organization; its vision is to empower science and engineering communities. We aim to bring knowledge, inspiration, and innovation to everyone and teach others to become successful. All our work is open source, and available for free, sharing is permitted, with the mention to the channel as the reference. . All Garage Education work is licensed under GPL-3.0 terms and conditions.. -----------------------------------------------------------. Follow us. ----

Course Details

  • Course Lessons92
  • Course Period19h 19m
  • No.Students2
  • Languageعربي
  • No Prerequisite
  • (1)
  • Start Now for free

Course Lessons

  1. 1 | Big Data Engineering In Depth Promo 00:00:09
  2. 2 | Ch.01-01 Course Introduction 00:10:58
  3. 3 | Ch.01-02 Getting the max benefit from this course 00:07:55
  4. 4 | Ch.01-03 Assignments, Labs, and Textbooks 00:06:02
  5. 5 | Ch.01-04 Course Content Overview 00:07:00
  6. 6 | Ch.02-01 Introduction To Data Management 00:11:51
  7. 7 | Ch.02-02 Data Abstraction 00:12:32
  8. 8 | Ch.02-03 Physical Layer 00:05:46
  9. 9 | Ch.02-04 Logical Layer 00:05:10
  10. 10 | Ch.02-05 View Layer 00:05:30
  11. 11 | Ch.02-06 Data Solution Thinking 00:07:03
  12. 12 | Ch.02-07 Introduction to DWH 00:10:14
  13. 13 | Ch.02-08 DWH Vs Transactional DB 00:09:57
  14. 14 | Ch.02-09 DWH BusinessTypes 00:08:31
  15. 15 | Ch.02-10 Use Cases For DWH Types and Transactional DB 00:10:21
  16. 16 | Ch.02-11 Multi Temperature Storage System 00:18:43
  17. 17 | Ch.02-12 DWH Characteristics and Architecture Components DWH Architecture 00:10:49
  18. 18 | Ch.02-13 Source Systems Integration Process DWH Architecture 00:11:08
  19. 19 | Ch.02-14 Source Systems Extraction Layer DWH Architecture 00:09:12
  20. 20 | Ch.02-15 Staging Layer DWH Architecture 00:05:04
  21. 21 | Ch.02-16 Data Modeling DWH Architecture 00:29:46
  22. 22 | Ch.02-17 Dimension Types: Conformed Dimension Data Modeling DWH Architecture 00:06:20
  23. 23 | Ch.02-18 Dimension Types: Degenerate Dimension Data Modeling DWH Architecture 00:03:51
  24. 24 | Ch.02-19 Dimension Types: Junk Dimension Data Modeling DWH Architecture 00:10:03
  25. 25 | Ch.02-20 Dimension Types: Role Playing Dimension Data Modeling DWH Architecture 00:05:56
  26. 26 | Ch.02-21 Dimension Types: Outrigger Dimension Data Modeling DWH Architecture 00:03:12
  27. 27 | Ch.02-22 Dimension Types: Snowflake Dimension Data Modeling DWH Architecture 00:03:47
  28. 28 | Ch.02-23 Dimension Types: Slowly changing dimension SCD 0,1,2,3,4 Data Modeling DWH Architecture 00:13:17
  29. 29 | Ch.02-24 Dimension Types: Fast Changing Dimensions Data Modeling DWH Architecture 00:08:24
  30. 30 | Ch.02-25 Dimension Types: Shrunken Dimension Data Modeling DWH Architecture 00:05:57
  31. 31 | Ch.02-26 Dimension Types: Multi Valued Dimension Data Modeling DWH Architecture 00:10:49
  32. 32 | Ch.02-28 Dimension Types: Heterogeneous Dimension Data Modeling DWH Architecture 00:07:00
  33. 33 | Ch.02-27 Dimension Types: Swappable Dimension Data Modeling DWH Architecture 00:14:30
  34. 34 | Ch.02-29 Fact Tables Data Modeling DWH Architecture 00:33:31
  35. 35 | Ch.02-30 Schema Types Data Modeling DWH Architecture 00:15:33
  36. 36 | Ch.02-31 Introduction ETL DWH Architecture 00:17:08
  37. 37 | Ch.02-32 Best Practices ETL DWH Architecture 00:33:51
  38. 38 | Ch.02-33 Surrogate Vs Natural Key Data Modeling 00:15:39
  39. 39 | Ch.02-34 Partitioning vs Bucketing Data Modeling 00:12:24
  40. 40 | Ch.02-35 Kimball vs Inmon Data Modeling 00:22:29
  41. 41 | Ch.03-01 Introduction To Distributed Systems Hadoop 00:25:05
  42. 42 | Ch.03-02 Introduction To Distributed Systems Hadoop 00:15:40
  43. 43 | Ch.03-03 Introduction To Hadoop 00:32:11
  44. 44 | Ch.03-04 HDFS Hadoop 00:15:41
  45. 45 | Ch.03-05 YARN Hadoop 00:39:41
  46. 46 | Ch.03-06 - Map Reduce Hadoop 00:37:39
  47. 47 | Ch.03-07 - Combiner Map Reduce Hadoop 00:11:01
  48. 48 | Ch.03-08 - With vs Without Combiners Map Reduce Hadoop 00:15:50
  49. 49 | Ch.03-09 - Inverted Index Map Reduce Hadoop 00:21:04
  50. 50 | Ch.03-10 - Custom Writable Implementation Map Reduce Hadoop 00:18:04
  51. 51 | Ch.03-11 - Custom Partitioner Map Reduce Hadoop 00:17:37
  52. 52 | Ch.03-12 - Secondary Sort - Part 1 Map Reduce Hadoop 00:19:00
  53. 53 | Ch.03-13 - Secondary Sort - Part 2 Map Reduce Hadoop 00:13:32
  54. 54 | Ch.03-14 - Reduce Side Join Map Reduce Hadoop 00:23:57
  55. 55 | Ch.03-15 -Map Side Join Map Reduce Hadoop 00:09:58
  56. 56 | Ch.03-16 - Hadoop Filesystems and CLI Hadoop 00:21:08
  57. 57 | Ch.03-17 - Anatomy of a File Read and Write HDFS Hadoop 00:20:21
  58. 58 | Ch.03-18 - Introduction to Apache Hive Hive Hadoop 00:13:02
  59. 59 | Ch.03-19- Apache Hive vs Traditional RDBMS Hive Hadoop 00:13:17
  60. 60 | Ch.03-20- Apache Hive Architecture Hive Hadoop 00:19:55
  61. 61 | Ch.03-21- Query Execution Flow Hive Hadoop 00:05:55
  62. 62 | Ch.03-22- Table Format Hive Hadoop 00:10:58
  63. 63 | Ch.03-23- Hive Database Hive Hadoop 00:09:44
  64. 64 | Ch.03-24- Hive Tables Hive Hadoop 00:37:58
  65. 65 | Ch.03-25- Hive Demo Hive Hadoop 00:17:29
  66. 66 | Ch.04-01: Introduction to Apache Spark 00:06:16
  67. 67 | Ch.04-02: Python Vs. Scala 00:10:46
  68. 68 | Ch.04-03: Introduction to Apache Spark 00:10:21
  69. 69 | Ch.04-04: About Databricks 00:08:02
  70. 70 | Ch.04-05: Spark In The Data Platforms 00:06:27
  71. 71 | Ch.04-06: Running Spark 00:02:30
  72. 72 | Ch.04-07: Demo: Running Spark on Linux Ubuntu 00:05:05
  73. 73 | Ch.04-08: Demo: Running Spark on MacOS 00:03:36
  74. 74 | Ch.04-09: Demo: Running Spark on Windows 00:09:08
  75. 75 | Ch.04-10: Demo: Running Spark on Databricks 00:05:19
  76. 76 | Ch.04-11: From Map Reduce To Spark 00:06:08
  77. 77 | Ch.04-12: Spark Characteristics 00:10:28
  78. 78 | Ch.04-13: Spark Applications 00:03:03
  79. 79 | Ch.04-14: Spark Driver 00:08:09
  80. 80 | Ch.04-15: Spark Session 00:07:45
  81. 81 | Ch.04-16: Spark Cluster Manager 00:05:22
  82. 82 | Ch.04-17: Spark Execution Mode 00:08:09
  83. 83 | Ch.04-18: Spark Executors 00:03:04
  84. 84 | Ch.04-19: Spark Data Partitioning 00:06:31
  85. 85 | Ch.04-20: Spark Operations 00:17:41
  86. 86 | Ch.04-21: Transformations Narrow Vs Wide 00:06:57
  87. 87 | Ch.04-22: Demo: Immutability In Spark 00:06:44
  88. 88 | Ch.04-23: Demo: RDD Text Manipulation 00:03:48
  89. 89 | Ch.04-24: Demo: GroupByKey Vs. ReduceByKey 00:06:57
  90. 90 | Ch.04-25: Demo: Joining RDDs 00:19:17
  91. 91 | Ch.04-26: Demo: Spark RDD APIs 00:21:15
  92. 92 | Ch.04-27: Demo: Repartition Vs. Coalesce 00:17:05
    Student Reviews

    ( 5 Of 5 )

    1 review
    5 Stars
    100%
    4 Stars
    0%
    3 Stars
    0%
    2 Stars
    0%
    1 Star
    0%
    Y
    Youtube

    18-10-2024
    Big Data Engineering In Depth

    About Big Data Engineering in Depth Course
    -------------------
    The Big Data in Depth is a free online course and doesn’t target any revenue income ever. This course aims to share knowledge in the big data and data engineering. It also focuses on getting you from any level to be a professional in this field. This course starts by explaining the Data Engineering and Distributed systems as basic billers for this course. Then it goes throw different topics in Big Data tools, DevOps, Docker, Functional Programming, Scala, Spark, Kafka, Data Orchestrations, Elastics, and Architecture design. This course is available online free on Youtube “without any advertisement as we need it to be free,” and the material is available on Google classroom and Github. This course includes lots of practical demos and coding sessions to get you to excel and understand these topics.
    -----------------------------------------------------------
    Google classroom
    -------------------
    You can contact and communicate with the instructors and other students on Google classroom from this link https://classroom.google.com/
    classroom code is p17slt
    -----------------------------------------------------------
    About Garage Education
    -------------------
    Garage Education is a nonprofit organization that aims to share knowledge, science, experience, and best practices in All technology eras. These areas include Big Data, Data Science, Distributed Systems, Data Warehouse, Programming, and Architecture design.
    Our vision is to empower science and engineering communities. We aim to bring knowledge, inspiration, and innovation to everyone and teach others to become successful.
    All our work is open source and free for use and sharing, but it is crucial to keep the reference for our channel. We don’t have any goal to get revenue from this content; it is free, and we don’t offer any paid advertising from companies or platforms, including Youtube.
    -----------------------------------------------------------
    Follow us
    -------------------
    Twitter: https://twitter.com/garageeducation