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SQL NoSQL Big Data and Hadoop

SQL NoSQL Big Data and Hadoop

IT & Software

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About This Course

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

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SQL NoSQL Big Data and Hadoop

About This Course