Section 1 : Introduction

 		Lecture 1 Introduction
Lecture 2 Building a Data-driven Organization - Introduction 3:49
Lecture 3 Data Engineering 6:10
Lecture 4 Learning Environment & Course Material
Lecture 5 Movielens Dataset 3:18

Section 2 : Relational Database Systems

 		Lecture 6 Introduction to Relational Databases 8:36
Lecture 7 SQL 4:47
Lecture 8 Movielens Relational Model 14:52
Lecture 9 Movielens Relational Model Normalization vs Denormalization 15:33
Lecture 10 MySQL 5:6
Lecture 11 Movielens in MySQL Database import 5:42
Lecture 12 OLTP in RDBMS CRUD Applications 17:6
Lecture 13 Indexes 15:34
Lecture 14 Data Warehousing 15:27
Lecture 15 Analytical Processing 16:37
Lecture 16 Transaction Logs
Lecture 17 Relational Databases - Wrap Up 3:4

Section 3 : Database Classification

 		Lecture 18 Distributed Databases 7:25
Lecture 19 CAP Theorem 9:55
Lecture 20 BASE 7:18
Lecture 21 Other Classification 6:58

Section 4 : Key-Value Store

 		Lecture 22 Introduction to KV Stores 2:21
Lecture 23 Redis 3:57
Lecture 24 Install Redis 7:7
Lecture 25 Time Complexity of Algorithm 4:40
Lecture 26 Data Structures in Redis Key & String 20:17
Lecture 27 Data Structures in Redis II Hash & List 18:14
Lecture 28 Data structures in Redis III Set & Sorted Set 20:34
Lecture 29 Data structures in Redis IV Geo & HyperLogLog 10:33
Lecture 30 Data structures in Redis V Pubsub & Transaction 7:51
Lecture 31 Modelling Movielens in Redis 11:23
Lecture 32 Redis Example in Application 28:54
Lecture 33 KV Stores Wrap Up 2:3

Section 5 : Document-Oriented Databases

 		Lecture 34 Introduction to Document-Oriented Databases 4:33
Lecture 35 MongoDB 3:47
Lecture 36 MongoDB installation 1:30
Lecture 37 Movielens in MongoDB 13:12
Lecture 38 Movielens in MongoDB Normalization vs Denormalization 11:20
Lecture 39 Movielens in MongoDB Implementation 10:0
Lecture 40 CRUD Operations in MongoDB 12:46
Lecture 41 Indexes
Lecture 42 MongoDB Aggregation Query - MapReduce function 9:19
Lecture 43 MongoDB Aggregation Query - Aggregation Framework 15:39
Lecture 44 Demo MySQL vs MongoDB 1:49
Lecture 45 Document Stores Wrap Up 3:7

Section 6 : Search Engine

 		Lecture 46 Introduction to Search Engine Stores 5:12
Lecture 47 Elasticsearch 8:34
Lecture 48 Basic Terms Concepts and Description 12:48
Lecture 49 Movielens in Elastisearch 11:42
Lecture 50 CRUD in Elasticsearch 15:10
Lecture 51 Search Queries in Elasticsearch 22:52
Lecture 52 Aggregation Queries in Elasticsearch 14:8
Lecture 53 The Elastic Stack (ELK) 11:44
Lecture 54 Use case UFO Sighting in ElasticSearch 28:47
Lecture 55 Search Engines Wrap Up 3:48

Section 7 : Wide Column Store

 		Lecture 56 Introduction to Columnar databases 6:29
Lecture 57 HBase 6:53
Lecture 58 HBase Architecture 8:58
Lecture 59 HBase Installation 8:46
Lecture 60 Apache Zookeeper 6:28
Lecture 61 Movielens Data in HBase 18:6
Lecture 62 Performing CRUD in HBase 24:20
Lecture 63 SQL on HBase - Apache Phoenix 13:43
Lecture 64 SQL on HBase - Apache Phoenix - Movielens 10:8
Lecture 65 Demo GeoLife GPS Trajectories 1:46
Lecture 66 Wide Column Store Wrap Up 4:43

Section 8 : Time Series Databases

 		Lecture 67 Introduction to Time Series 9:26
Lecture 68 InfluxDB 2:50
Lecture 69 InfluxDB Installation 6:35
Lecture 70 InfluxDB Data Model 7:27
Lecture 71 Data manipulation in InfluxDB 16:37
Lecture 72 TICK Stack I 11:47
Lecture 73 TICK Stack II 22:57
Lecture 74 Time Series Databases Wrap Up

Section 9 : Graph Databases

 		Lecture 75 Introduction to Graph Databases 5:0
Lecture 76 Modelling in Graph 13:38
Lecture 77 Modelling Movielens as a Graph 10:0
Lecture 78 Neo4J 3:32
Lecture 79 Neo4J installation 8:27
Lecture 80 Cypher 12:20
Lecture 81 Cypher II 19:9
Lecture 82 Movielens in Neo4J Data Import 17:16
Lecture 83 Movielens in Neo4J Spring Application 11:33
Lecture 84 Data Analysis in Graph Databases 5:10
Lecture 85 Examples of Graph Algorithms in Neo4J 18:20
Lecture 86 Graph Databases Wrap Up 6:56

Section 10 : Hadoop Platform

 		Lecture 87 Introduction to Big Data With Apache Hadoop 6:23
Lecture 88 Big Data Storage in Hadoop (HDFS) 15:47
Lecture 89 Big Data Processing YARN 11:1
Lecture 90 Installation 12:41
Lecture 91 Data Processing in Hadoop (MapReduce) 14:1
Lecture 92 Examples in MapReduce 24:51
Lecture 93 Data Processing in Hadoop (Pig) 11:45
Lecture 94 Examples in Pig 21:19
Lecture 95 Data Processing in Hadoop (Spark) 8:48
Lecture 96 Examples in Spark 22:37
Lecture 97 Data Analytics with Apache Spark 9:0
Lecture 98 Data Compression 5:41
Lecture 99 Data serialization and storage formats 19:50
Lecture 100 Hadoop Wrap Up 7:6

Section 11 : Big Data SQL Engines

 		Lecture 101 Introduction Big Data SQL Engines 2:51
Lecture 102 Apache Hive 10:25
Lecture 103 Apache Hive Demonstration 19:40
Lecture 104 MPP SQL-on-Hadoop Introduction 3:25
Lecture 105 Impala 5:35
Lecture 106 Impala Demonstration 17:55
Lecture 107 PrestoDB 13:12
Lecture 108 PrestoDB Demonstration 13:35
Lecture 109 SQL-on-Hadoop Wrap Up 1:58

Section 12 : Distributed Commit Log

 		Lecture 110 Data Architectures 4:45
Lecture 111 Introduction to Distributed Commit Logs 6:30
Lecture 112 Apache Kafka 2:55
Lecture 113 Confluent Platform Installation 10:7
Lecture 114 Data Modeling in Kafka I 12:48
Lecture 115 Data Modeling in Kafka II 14:59
Lecture 116 Data Generation for Testing 8:29
Lecture 117 Use case Toll fee Collection 4:23
Lecture 118 Stream processing 11:5
Lecture 119 Stream Processing II with Stream + Connect APIs 19:6
Lecture 120 Example Kafka Streams 15:2
Lecture 121 KSQL Streaming Processing in SQL 4:10
Lecture 122 KSQL Example 14:11
Lecture 123 Demonstration NYC Taxi and Fares 1:28
Lecture 124 Streaming Wrap Up 2:15

Section 13 : Summary

 		Lecture 125 Database Polyglot 3:44
Lecture 126 Extending your knowledge 8:29
Lecture 127 Data Visualization 10:49
Lecture 128 Building a Data-driven Organization - Conclusion 6:52
Lecture 129 Conclusion 3:9