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 | ||
Resources | |||
Resources – SQL NoSQL Big Data And Hadoop | 00:00:00 |
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