See the Curriculum Section for materials and enjoy learning with Imperial Academy.

Course Curriculum

Section 01: Introduction
	Introduction	00:07:00
	Building a Data-driven Organization – Introduction	00:04:00
	Data Engineering	00:06:00
	Learning Environment & Course Material	00:04:00
	Movielens Dataset	00:03:00
Section 02: Relational Database Systems
	Introduction to Relational Databases	00:09:00
	SQL	00:05:00
	Movielens Relational Model	00:15:00
	Movielens Relational Model: Normalization vs Denormalization	00:16:00
	MySQL	00:05:00
	Movielens in MySQL: Database import	00:06:00
	OLTP in RDBMS: CRUD Applications	00:17:00
	Indexes	00:16:00
	Data Warehousing	00:15:00
	Analytical Processing	00:17:00
	Transaction Logs	00:06:00
	Relational Databases – Wrap Up	00:03:00
Section 03: Database Classification
	Distributed Databases	00:07:00
	CAP Theorem	00:10:00
	BASE	00:07:00
	Other Classifications	00:07:00
Section 04: Key-Value Store
	Introduction to KV Stores	00:02:00
	Redis	00:04:00
	Install Redis	00:07:00
	Time Complexity of Algorithm	00:05:00
	Data Structures in Redis : Key & String	00:20:00
	Data Structures in Redis II : Hash & List	00:18:00
	Data structures in Redis III : Set & Sorted Set	00:21:00
	Data structures in Redis IV : Geo & HyperLogLog	00:11:00
	Data structures in Redis V : Pubsub & Transaction	00:08:00
	Modelling Movielens in Redis	00:11:00
	Redis Example in Application	00:29:00
	KV Stores: Wrap Up	00:02:00
Section 05: Document-Oriented Databases
	Introduction to Document-Oriented Databases	00:05:00
	MongoDB	00:04:00
	MongoDB Installation	00:02:00
	Movielens in MongoDB	00:13:00
	Movielens in MongoDB: Normalization vs Denormalization	00:11:00
	Movielens in MongoDB: Implementation	00:10:00
	CRUD Operations in MongoDB	00:13:00
	Indexes	00:16:00
	MongoDB Aggregation Query – MapReduce function	00:09:00
	MongoDB Aggregation Query – Aggregation Framework	00:16:00
	Demo: MySQL vs MongoDB. Modeling with Spark	00:02:00
	Document Stores: Wrap Up	00:03:00
Section 06: Search Engines
	Introduction to Search Engine Stores	00:05:00
	Elasticsearch	00:09:00
	Basic Terms Concepts and Description	00:13:00
	Movielens in Elastisearch	00:12:00
	CRUD in Elasticsearch	00:15:00
	Search Queries in Elasticsearch	00:23:00
	Aggregation Queries in Elasticsearch	00:23:00
	The Elastic Stack (ELK)	00:12:00
	Use case: UFO Sighting in ElasticSearch	00:29:00
	Search Engines: Wrap Up	00:04:00
Section 07: Wide Column Store
	Introduction to Columnar databases	00:06:00
	HBase	00:07:00
	HBase Architecture	00:09:00
	HBase Installation	00:09:00
	Apache Zookeeper	00:06:00
	Movielens Data in HBase	00:17:00
	Performing CRUD in HBase	00:24:00
	SQL on HBase – Apache Phoenix	00:14:00
	SQL on HBase – Apache Phoenix – Movielens	00:10:00
	Demo : GeoLife GPS Trajectories	00:02:00
	Wide Column Store: Wrap Up	00:04:00
Section 08: Time Series Databases
	Introduction to Time Series	00:09:00
	InfluxDB	00:03:00
	InfluxDB Installation	00:07:00
	InfluxDB Data Model	00:07:00
	Data manipulation in InfluxDB	00:17:00
	TICK Stack I	00:12:00
	TICK Stack II	00:23:00
	Time Series Databases: Wrap Up	00:04:00
Section 09: Graph Databases
	Introduction to Graph Databases	00:05:00
	Modelling in Graph	00:14:00
	Modelling Movielens as a Graph	00:10:00
	Neo4J	00:04:00
	Neo4J installation	00:08:00
	Cypher	00:12:00
	Cypher II	00:19:00
	Movielens in Neo4J: Data Import	00:17:00
	Movielens in Neo4J: Spring Application	00:12:00
	Data Analysis in Graph Databases	00:05:00
	Examples of Graph Algorithms in Neo4J	00:18:00
	Graph Databases: Wrap Up	00:07:00
Section 10: Hadoop Platform
	Introduction to Big Data With Apache Hadoop	00:06:00
	Big Data Storage in Hadoop (HDFS)	00:16:00
	Big Data Processing : YARN	00:11:00
	Installation	00:13:00
	Data Processing in Hadoop (MapReduce)	00:14:00
	Examples in MapReduce	00:25:00
	Data Processing in Hadoop (Pig)	00:12:00
	Examples in Pig	00:21:00
	Data Processing in Hadoop (Spark)	00:23:00
	Examples in Spark	00:23:00
	Data Analytics with Apache Spark	00:09:00
	Data Compression	00:06:00
	Data serialization and storage formats	00:20:00
	Hadoop: Wrap Up	00:07:00
Section 11: Big Data SQL Engines
	Introduction Big Data SQL Engines	00:03:00
	Apache Hive	00:10:00
	Apache Hive : Demonstration	00:20:00
	MPP SQL-on-Hadoop: Introduction	00:03:00
	Impala	00:06:00
	Impala : Demonstration	00:18:00
	PrestoDB	00:13:00
	PrestoDB : Demonstration	00:14:00
	SQL-on-Hadoop: Wrap Up	00:02:00
Section 12: Distributed Commit Log
	Data Architectures	00:05:00
	Introduction to Distributed Commit Logs	00:07:00
	Apache Kafka	00:03:00
	Confluent Platform Installation	00:10:00
	Data Modeling in Kafka I	00:13:00
	Data Modeling in Kafka II	00:15:00
	Data Generation for Testing	00:09:00
	Use case: Toll fee Collection	00:04:00
	Stream processing	00:11:00
	Stream Processing II with Stream + Connect APIs	00:19:00
	Example: Kafka Streams	00:15:00
	KSQL : Streaming Processing in SQL	00:04:00
	KSQL: Example	00:14:00
	Demonstration: NYC Taxi and Fares	00:01:00
	Streaming: Wrap Up	00:02:00
Section 13: Summary
	Database Polyglot	00:04:00
	Extending your knowledge	00:08:00
	Data Visualization	00:11:00
	Building a Data-driven Organization – Conclusion	00:07:00
	Conclusion	00:03:00