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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.Students13
  • Languageعربي
  • No Prerequisite
  • (1)
  • Start Now for free

Course Lessons

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