Learning Path: Understanding Tool Integration for Big Data Architecture

Table of contents

1. Introduction to Clickstream Case Study 00:11:19
2. Requirements 00:08:04
3. Data Modeling 00:14:55
4. Data Ingest 00:16:16
5. Data Processing Engines - Part 1 00:16:23
6. Data Processing Engines - Part 2 00:10:59
7. Data Processing Patterns 00:09:32
8. Orchestration 00:14:34
9. Putting It All Together 00:03:08
10. Demo 00:21:47
11. Q&A 00:24:35
12. Introduction
    1. Introduction to Time Series Problems 00:09:58
13. Kafka
    1. Kafka Architecture and Deployment 00:11:33
    2. Kafka Usage 00:03:42
14. Spark
    1. Introduction to Spark 00:15:43
    2. Spark Architecture 00:12:02
15. Spark Streaming
    1. Spark Streaming: Windows & Slides 00:08:35
    2. Spark Streaming: Ingestion Sources & Using Kafka 00:08:32
    3. Sparks Streaming: Operations on the Stream 00:01:30
16. Cassandra
    1. Introduction to Cassandra 00:08:56
    2. Cassandra Basic Architecture 00:11:59
    3. Replication, High Availability and Multi Datacenter 00:14:06
    4. Cassandra Weather Website Example 00:11:46
    5. Cassandra Query Language (CQL) 00:18:00
    6. Cassandra Partitions & Clustering 00:08:22
    7. Cassandra Read and Write Path 00:12:17
    8. Working with Cassandra 00:06:32
    9. Cassandra Drivers and Access Patterns 00:10:37
17. Spark and Cassandra
    1. Spark and Cassandra Architecture 00:12:00
    2. Analyzing Cassandra Data & Spark SQL 00:12:12
    3. Spark and Cassandra DataStax Enterprise 00:04:31
18. Real World Use Cases
    1. Real World Use Cases: Streaming Problems 00:17:11
    2. Real World Use Cases: In-place Analytic Problems 00:10:58
19. Introduction
    1. Course Introduction 00:04:21
    2. About The Author 00:04:14
    3. What Is Big Data 00:11:07
    4. Historical Approaches 00:07:04
    5. Modern-Day Approach 00:12:42
    6. What Is Hadoop 00:11:05
    7. Hadoop Core Vs Ecosystem 00:05:03
    8. Hadoopable Problems 00:06:37
20. Hadoop Basics
    1. HDFS And Yarn 00:08:14
    2. Hive And Pig Interface Introduction 00:05:59
    3. Introduction To Spark 00:04:37
    4. Hadoop In The Cloud (Amazon Web Services Intro) 00:08:49
    5. Installing Hadoop Into EMR Part - 1 00:15:31
    6. Installing Hadoop Into EMR Part - 2 00:15:34
    7. Installing Cloudera Quickstart VM 00:11:01
    8. Web GUIs 00:11:06
21. Hadoop Distributed Filesystem (HDFS)
    1. HDFS Architecture 00:10:05
    2. HDFS File Write Walkthrough 00:17:57
    3. Secondary Name Node 00:06:38
    4. Basic HDFS Commands 00:09:23
    5. Using HDFS Commands Part - 1 00:07:34
    6. Using HDFS Commands Part - 2 00:09:27
    7. HA And Federation Basics 00:12:48
    8. HDFS Access Controls (Or Lack Thereof) 00:09:34
22. Yarn
    1. Yarn Purpose 00:06:16
    2. Yarn Architecture 00:07:25
    3. Yarn With Spark 00:06:44
23. MapReduce
    1. MapReduce Explained 00:11:52
    2. MapReduce Architecture 00:07:36
    3. MapReduce Code Walkthrough 00:11:59
    4. MapReduce Details Walkthrough 00:04:45
    5. Running MapReduce Job 00:08:59
24. HDFS Data Import And Export
    1. Import/Export Options 00:11:12
    2. Flume Introduction 00:10:53
    3. Using Flume 00:13:43
    4. Sqoop Introduction 00:09:25
    5. Using Sqoop 00:17:01
    6. HDFS Interaction Tools 00:06:01
    7. Oozie Introduction 00:10:17
25. Spark Basics
    1. Spark Value Propositions 00:08:30
    2. Spark Run Modes (Yarn, Standalone, Mesos) 00:07:33
    3. RDDs And Dataframes 00:17:24
    4. Hands On Spark Part - 1 00:08:12
    5. Hands On Spark Part - 2 00:10:38
    6. Running Spark Part - 1 00:09:58
    7. Running Spark Part - 2 00:13:55
    8. Optimizing And Debugging Spark 00:18:17
    9. Spark Libraries Overview 00:09:05
26. Spark Built-In Libraries
    1. Spark SQL 00:09:01
    2. Spark SQL Usage 00:12:02
    3. MLlib Basics 00:15:30
    4. Common MLlib Usage Part - 1 00:15:02
    5. Common MLlib Usage Part - 2 00:08:23
    6. Spark Streaming 00:12:43
    7. GraphX 00:09:58
27. Hive And Pig
    1. Hive Vs Pig 00:09:53
    2. Hive Basics 00:11:53
    3. Analysis With Hive 00:10:54
    4. Pig Basics 00:14:38
    5. ETL And Analytics With Pig 00:20:16
28. Hadoop In The Cloud
    1. Hadoop/Cloud Use Cases 00:05:16
    2. Elastic MapReduce (EMR) 00:12:47
29. Ecosystem
    1. HBase Basics 00:11:16
    2. Enterprise Integration 00:10:39
30. Wrap Up
    1. Wrap Up 00:03:41
31. Introduction to Sqoop
    1. Introduction 00:03:45
    2. About The Author 00:00:48
    3. Use Case #1: ELT 00:05:32
    4. Use Case #2: ETL From DWH 00:03:04
    5. Use Case #3: Data Analysis 00:03:38
    6. Use Case #4: Data Archival 00:02:02
    7. Use Case #5: Move Reports To Hadoop 00:05:26
    8. Use Case #6: Data Consolidation 00:02:54
32. Importing Data To Hadoop From A Relational Database
    1. Command Line Basics: Importing Data Using Sqoop 00:09:13
    2. Importing Data With Column Filters, Row Filters, And Free Text Queries 00:06:12
    3. Parallel Imports 00:04:33
    4. Import Data Directory To HIVE Tables 00:07:25
    5. Incremental Data Import Overview 00:06:00
    6. Incremental Data Import And Using Sqoop Stored Jobs 00:11:05
33. Sqoop Hands-On: Exporting Data From Hadoop To A Relational Database
    1. Exporting Data Back To A Relational Database Using Sqoop 00:05:42
    2. Exporting data from Hadoop back to RDBMS 00:01:57
34. Advanced topics
    1. Introduction to Sqoop2 Server 00:04:18
35. Course summary
    1. Wrap Up 00:04:07
    2. Continuous curation of event data for a customer event hub - Arvind Prabhakar (StreamSets) 00:40:27
    3. Big data governance - Steven Totman (Cloudera), Mark Donsky (Cloudera), Kristi Cunningham (Capital One), Ben Harden (CapTech Consulting) 00:42:12
    4. Preventing a big data security breach - Sam Heywood (Cloudera), Nick Curcuru (MasterCard Advisors), Ritu Kama (Intel) 00:39:23
    5. Hive + Amazon EMR + S3 = Elastic big data SQL analytics processing in the cloud, a real-world case study - Jaipaul Agonus (FINRA) 00:42:52