Section 1 : Introduction
Lecture 1 Introduction
Lecture 2 Building a Data-driven Organization - Introduction 00:03:49 Duration
Lecture 3 Data Engineering 00:06:10 Duration
Lecture 4 Learning Environment & Course Material
Lecture 5 Movielens Dataset 00:03:18 Duration

Section 2 : Relational Database Systems
Lecture 1 Introduction to Relational Databases 00:08:36 Duration
Lecture 2 SQL 00:04:47 Duration
Lecture 3 Movielens Relational Model 00:14:52 Duration
Lecture 4 Movielens Relational Model Normalization vs Denormalization 00:15:33 Duration
Lecture 5 MySQL 00:05:06 Duration
Lecture 6 Movielens in MySQL Database import 00:05:42 Duration
Lecture 7 OLTP in RDBMS CRUD Applications 00:17:06 Duration
Lecture 8 Indexes 00:15:34 Duration
Lecture 9 Data Warehousing 00:15:27 Duration
Lecture 10 Analytical Processing 00:16:37 Duration
Lecture 11 Transaction Logs
Lecture 12 Relational Databases - Wrap Up 00:03:04 Duration

Section 3 : Database Classification
Lecture 1 Distributed Databases 00:07:25 Duration
Lecture 2 CAP Theorem 00:09:55 Duration
Lecture 3 BASE 00:07:18 Duration
Lecture 4 Other Classification 00:06:58 Duration

Section 4 : Key-Value Store
Lecture 1 Introduction to KV Stores 00:02:21 Duration
Lecture 2 Redis 00:03:57 Duration
Lecture 3 Install Redis 00:07:07 Duration
Lecture 4 Time Complexity of Algorithm 00:04:40 Duration
Lecture 5 Data Structures in Redis Key & String 00:20:17 Duration
Lecture 6 Data Structures in Redis II Hash & List 00:18:14 Duration
Lecture 7 Data structures in Redis III Set & Sorted Set 00:20:34 Duration
Lecture 8 Data structures in Redis IV Geo & HyperLogLog 00:10:33 Duration
Lecture 9 Data structures in Redis V Pubsub & Transaction 00:07:51 Duration
Lecture 10 Modelling Movielens in Redis 00:11:23 Duration
Lecture 11 Redis Example in Application 00:28:54 Duration
Lecture 12 KV Stores Wrap Up 00:02:03 Duration

Section 5 : Document-Oriented Databases
Lecture 1 Introduction to Document-Oriented Databases 00:04:33 Duration
Lecture 2 MongoDB 00:03:47 Duration
Lecture 3 MongoDB installation 00:01:30 Duration
Lecture 4 Movielens in MongoDB 00:13:12 Duration
Lecture 5 Movielens in MongoDB Normalization vs Denormalization 00:11:20 Duration
Lecture 6 Movielens in MongoDB Implementation 00:10:00 Duration
Lecture 7 CRUD Operations in MongoDB 00:12:46 Duration
Lecture 8 Indexes
Lecture 9 MongoDB Aggregation Query - MapReduce function 00:09:19 Duration
Lecture 10 MongoDB Aggregation Query - Aggregation Framework 00:15:39 Duration
Lecture 11 Demo MySQL vs MongoDB 00:01:49 Duration
Lecture 12 Document Stores Wrap Up 00:03:07 Duration

Section 6 : Search Engine
Lecture 1 Introduction to Search Engine Stores 00:05:12 Duration
Lecture 2 Elasticsearch 00:08:34 Duration
Lecture 3 Basic Terms Concepts and Description 00:12:48 Duration
Lecture 4 Movielens in Elastisearch 00:11:42 Duration
Lecture 5 CRUD in Elasticsearch 00:15:10 Duration
Lecture 6 Search Queries in Elasticsearch 00:22:52 Duration
Lecture 7 Aggregation Queries in Elasticsearch 00:14:08 Duration
Lecture 8 The Elastic Stack (ELK) 00:11:44 Duration
Lecture 9 Use case UFO Sighting in ElasticSearch 00:28:47 Duration
Lecture 10 Search Engines Wrap Up 00:03:48 Duration

Section 7 : Wide Column Store
Lecture 1 Introduction to Columnar databases 00:06:29 Duration
Lecture 2 HBase 00:06:53 Duration
Lecture 3 HBase Architecture 00:08:58 Duration
Lecture 4 HBase Installation 00:08:46 Duration
Lecture 5 Apache Zookeeper 00:06:28 Duration
Lecture 6 Movielens Data in HBase 00:18:06 Duration
Lecture 7 Performing CRUD in HBase 00:24:20 Duration
Lecture 8 SQL on HBase - Apache Phoenix 00:13:43 Duration
Lecture 9 SQL on HBase - Apache Phoenix - Movielens 00:10:08 Duration
Lecture 10 Demo GeoLife GPS Trajectories 00:01:46 Duration
Lecture 11 Wide Column Store Wrap Up 00:04:43 Duration

Section 8 : Time Series Databases
Lecture 1 Introduction to Time Series 00:09:26 Duration
Lecture 2 InfluxDB 00:02:50 Duration
Lecture 3 InfluxDB Installation 00:06:35 Duration
Lecture 4 InfluxDB Data Model 00:07:27 Duration
Lecture 5 Data manipulation in InfluxDB 00:16:37 Duration
Lecture 6 TICK Stack I 00:11:47 Duration
Lecture 7 TICK Stack II 00:22:57 Duration
Lecture 8 Time Series Databases Wrap Up

Section 9 : Graph Databases
Lecture 1 Introduction to Graph Databases 00:05:00 Duration
Lecture 2 Modelling in Graph 00:13:38 Duration
Lecture 3 Modelling Movielens as a Graph 00:10:00 Duration
Lecture 4 Neo4J 00:03:32 Duration
Lecture 5 Neo4J installation 00:08:27 Duration
Lecture 6 Cypher 00:12:20 Duration
Lecture 7 Cypher II 00:19:09 Duration
Lecture 8 Movielens in Neo4J Data Import 00:17:16 Duration
Lecture 9 Movielens in Neo4J Spring Application 00:11:33 Duration
Lecture 10 Data Analysis in Graph Databases 00:05:10 Duration
Lecture 11 Examples of Graph Algorithms in Neo4J 00:18:20 Duration
Lecture 12 Graph Databases Wrap Up 00:06:56 Duration

Section 10 : Hadoop Platform
Lecture 1 Introduction to Big Data With Apache Hadoop 00:06:23 Duration
Lecture 2 Big Data Storage in Hadoop (HDFS) 00:15:47 Duration
Lecture 3 Big Data Processing YARN 00:11:01 Duration
Lecture 4 Installation 00:12:41 Duration
Lecture 5 Data Processing in Hadoop (MapReduce) 00:14:01 Duration
Lecture 6 Examples in MapReduce 00:24:51 Duration
Lecture 7 Data Processing in Hadoop (Pig) 00:11:45 Duration
Lecture 8 Examples in Pig 00:21:19 Duration
Lecture 9 Data Processing in Hadoop (Spark) 00:08:48 Duration
Lecture 10 Examples in Spark 00:22:37 Duration
Lecture 11 Data Analytics with Apache Spark 00:09:00 Duration
Lecture 12 Data Compression 00:05:41 Duration
Lecture 13 Data serialization and storage formats 00:19:50 Duration
Lecture 14 Hadoop Wrap Up 00:07:06 Duration

Section 11 : Big Data SQL Engines
Lecture 1 Introduction Big Data SQL Engines 00:02:51 Duration
Lecture 2 Apache Hive 00:10:25 Duration
Lecture 3 Apache Hive Demonstration 00:19:40 Duration
Lecture 4 MPP SQL-on-Hadoop Introduction 00:03:25 Duration
Lecture 5 Impala 00:05:35 Duration
Lecture 6 Impala Demonstration 00:17:55 Duration
Lecture 7 PrestoDB 00:13:12 Duration
Lecture 8 PrestoDB Demonstration 00:13:35 Duration
Lecture 9 SQL-on-Hadoop Wrap Up 00:01:58 Duration

Section 12 : Distributed Commit Log
Lecture 1 Data Architectures 00:04:45 Duration
Lecture 2 Introduction to Distributed Commit Logs 00:06:30 Duration
Lecture 3 Apache Kafka 00:02:55 Duration
Lecture 4 Confluent Platform Installation 00:10:07 Duration
Lecture 5 Data Modeling in Kafka I 00:12:48 Duration
Lecture 6 Data Modeling in Kafka II 00:14:59 Duration
Lecture 7 Data Generation for Testing 00:08:29 Duration
Lecture 8 Use case Toll fee Collection 00:04:23 Duration
Lecture 9 Stream processing 00:11:05 Duration
Lecture 10 Stream Processing II with Stream + Connect APIs 00:19:06 Duration
Lecture 11 Example Kafka Streams 00:15:02 Duration
Lecture 12 KSQL Streaming Processing in SQL 00:04:10 Duration
Lecture 13 KSQL Example 00:14:11 Duration
Lecture 14 Demonstration NYC Taxi and Fares 00:01:28 Duration
Lecture 15 Streaming Wrap Up 00:02:15 Duration

Section 13 : Summary
Lecture 1 Database Polyglot 00:03:44 Duration
Lecture 2 Extending your knowledge 00:08:29 Duration
Lecture 3 Data Visualization 00:10:49 Duration
Lecture 4 Building a Data-driven Organization - Conclusion 00:06:52 Duration
Lecture 5 Conclusion 00:03:09 Duration