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Course Curriculum

Course Curriculum

Section 01: Introduction
Introduction 00:07:00
Building a Data-driven Organization – Introduction 00:00:00
Data Engineering 00:00:00
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 00:07:00
HBase 00:07:00
HBase Architecture 00:09:00
HBase Installation 00:09:00
Apache Zookeeper 00:07:00
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 00:05:00
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
SQL-on-Hadoop: Wrap Up 00:02:00
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 00:09:00
Data Visualization 00:11:00
Building a Data-driven Organization – Conclusion 00:07:00
Conclusion 00:03:00

Sql Nosql Big Data and Hadoop All in One Course

4.8( 8 REVIEWS )
Duration: 22.37 Hours
Sql Nosql Big Data and Hadoop All in One Course
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