SQL NoSQL Big Data and Hadoop

A comprehensive journey through the world of database and data engineering concepts – from SQL, NoSQL to Hadoop
SQL NoSQL Big Data and Hadoop
File Size :
9.04 GB
Total length :
22h 7m



Michael Enudi


Last update




SQL NoSQL Big Data and Hadoop

What you’ll learn

Build an intuition from RDBMS system through NoSQL to the Big Data on the Cloud and Hadoop platform
Understand various distributed database classifications
Understand when and how to use Redis or Key-Value Stores
Understand when and how to use MongoDB or Document-oriented databases
Understand and use HBase as a Wide-Columnar Store
Understand and use Time series database (InfluxDB)
Understand and use Elasticsearch as a search engine
Understand and use Neo4J as a Graph Database Management System
Understand large scale distributed data storage and processing in Hadoop
Understand when and how to use and build Streaming architecture with Apache Kafka
Use Apache Hive and Understand where to use it in respect to big data platforms
Understand a number of SQL-on-Hadoop Engines and how they work
Understand how to use data engineering capabilities to enable a data-driven organization

SQL NoSQL Big Data and Hadoop


No strict requirement but knowledge of relational database will be helpful.
A Windows, Linux or Mac Machine to set up a lab
Any Hadoop Vendor Sandbox like Cloudera Quickstart or HDP VM (Hadoop)


A comprehensive look at the wide landscape of database systems and how to make a good choice in your next projectThe first time we ask or answer any question regarding databases is when building an application. The next is either when our choice of database becomes a bottleneck or when we need to do large-scale data analytics.This course covers almost all classes of databases or data storage platform there are and when to consider using them. It is a great journey through databases that will be great for software developers, big data engineers, data analysts as well as decision makers. It is not an in-depth look into each of the databases but promises to get you up and running with your first project for each class.In this course, we are going to cover Relational Database Systems, their features, use cases and limitationsWhy NoSQL?CAP TheoremKey-Value store and their use casesDocument-oriented databases and their use casesWide-columnar store and their use casesTime-series databases and their use casesSearch Engines and their use casesGraph databases and their use casesDistributed Logs and real time streaming systemsHadoop and its use casesSQL-on-Hadoop tools and their use casesHow to make informed decisions in building a good data storage platformWhat is the target audience?Chief data officersApplication developerData analystData architectsData engineersStudentsAnyone who wants to understand Hadoop from a database perspective.What this course does not cover?This course does not access any of the databases from the administrative perspective. So we don’t cover administrative tasks like security, backup, recovery, migration and the likes.Very in-depth features in the specific databases in discussion. An example is that we will not go into the different database engines for MySQL or how to write a stored procedures. What are the requirements?The lab for this course can be carried out in any machine (Microsoft Windows, Linux, Mac OX). However, the training on HBase or Hadoop will require you to have a hadoop environment. The suggestion for this will be to to use a pre-installed sandbox, a cloud offering or install your own custom sandbox.What do I need to know to get the best out of this course?This course does not assume any knowledge of NoSQL or data engineering.However a little knowledge of RDBMS (even Microsoft Access) is enough to get you into the best position for this course.


Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Building a Data-driven Organization – Introduction

Lecture 3 Data Engineering

Lecture 4 Learning Environment & Course Material

Lecture 5 Movielens Dataset

Section 2: Relational Database Systems

Lecture 6 Introduction to Relational Databases

Lecture 7 SQL

Lecture 8 Movielens Relational Model

Lecture 9 Movielens Relational Model: Normalization vs Denormalization

Lecture 10 MySQL

Lecture 11 Movielens in MySQL: Database import

Lecture 12 OLTP in RDBMS: CRUD Applications

Lecture 13 Indexes

Lecture 14 Data Warehousing

Lecture 15 Analytical Processing

Lecture 16 Transaction Logs

Lecture 17 Relational Databases – Wrap Up

Section 3: Database Classification

Lecture 18 Distributed Databases

Lecture 19 CAP Theorem

Lecture 20 BASE

Lecture 21 Other Classification

Section 4: Key-Value Store

Lecture 22 Introduction to KV Stores

Lecture 23 Redis

Lecture 24 Install Redis

Lecture 25 Time Complexity of Algorithm

Lecture 26 Data Structures in Redis : Key & String

Lecture 27 Data Structures in Redis II : Hash & List

Lecture 28 Data structures in Redis III : Set & Sorted Set

Lecture 29 Data structures in Redis IV : Geo & HyperLogLog

Lecture 30 Data structures in Redis V : Pubsub & Transaction

Lecture 31 Modelling Movielens in Redis

Lecture 32 Redis Example in Application

Lecture 33 KV Stores: Wrap Up

Section 5: Document-Oriented Databases

Lecture 34 Introduction to Document-Oriented Databases

Lecture 35 MongoDB

Lecture 36 MongoDB installation

Lecture 37 Movielens in MongoDB

Lecture 38 Movielens in MongoDB: Normalization vs Denormalization

Lecture 39 Movielens in MongoDB: Implementation

Lecture 40 CRUD Operations in MongoDB

Lecture 41 Indexes

Lecture 42 MongoDB Aggregation Query – MapReduce function

Lecture 43 MongoDB Aggregation Query – Aggregation Framework

Lecture 44 Demo: MySQL vs MongoDB. Modeling with Spark

Lecture 45 Document Stores: Wrap Up

Section 6: Search Engine

Lecture 46 Introduction to Search Engine Stores

Lecture 47 Elasticsearch

Lecture 48 Basic Terms Concepts and Description

Lecture 49 Movielens in Elastisearch

Lecture 50 CRUD in Elasticsearch

Lecture 51 Search Queries in Elasticsearch

Lecture 52 Aggregation Queries in Elasticsearch

Lecture 53 The Elastic Stack (ELK)

Lecture 54 Use case: UFO Sighting in ElasticSearch

Lecture 55 Search Engines: Wrap Up

Section 7: Wide Column Store

Lecture 56 Introduction to Columnar databases

Lecture 57 HBase

Lecture 58 HBase Architecture

Lecture 59 HBase Installation

Lecture 60 Apache Zookeeper

Lecture 61 Movielens Data in HBase

Lecture 62 Performing CRUD in HBase

Lecture 63 SQL on HBase – Apache Phoenix

Lecture 64 SQL on HBase – Apache Phoenix – Movielens

Lecture 65 Demo : GeoLife GPS Trajectories

Lecture 66 Wide Column Store: Wrap Up

Section 8: Time Series Databases

Lecture 67 Introduction to Time Series

Lecture 68 InfluxDB

Lecture 69 InfluxDB Installation

Lecture 70 InfluxDB Data Model

Lecture 71 Data manipulation in InfluxDB

Lecture 72 TICK Stack I

Lecture 73 TICK Stack II

Lecture 74 Time Series Databases: Wrap Up

Section 9: Graph Databases

Lecture 75 Introduction to Graph Databases.

Lecture 76 Modelling in Graph

Lecture 77 Modelling Movielens as a Graph

Lecture 78 Neo4J

Lecture 79 Neo4J installation

Lecture 80 Cypher

Lecture 81 Cypher II

Lecture 82 Movielens in Neo4J: Data Import

Lecture 83 Movielens in Neo4J: Spring Application

Lecture 84 Data Analysis in Graph Databases

Lecture 85 Examples of Graph Algorithms in Neo4J

Lecture 86 Graph Databases: Wrap Up

Section 10: Hadoop Platform

Lecture 87 Introduction to Big Data With Apache Hadoop

Lecture 88 Big Data Storage in Hadoop (HDFS)

Lecture 89 Big Data Processing : YARN

Lecture 90 Installation

Lecture 91 Data Processing in Hadoop (MapReduce)

Lecture 92 Examples in MapReduce

Lecture 93 Data Processing in Hadoop (Pig)

Lecture 94 Examples in Pig

Lecture 95 Data Processing in Hadoop (Spark)

Lecture 96 Examples in Spark

Lecture 97 Data Analytics with Apache Spark

Lecture 98 Data Compression

Lecture 99 Data serialization and storage formats

Lecture 100 Hadoop: Wrap Up

Section 11: Big Data SQL Engines

Lecture 101 Introduction Big Data SQL Engines

Lecture 102 Apache Hive

Lecture 103 Apache Hive : Demonstration

Lecture 104 MPP SQL-on-Hadoop: Introduction

Lecture 105 Impala

Lecture 106 Impala : Demonstration

Lecture 107 PrestoDB

Lecture 108 PrestoDB : Demonstration

Lecture 109 SQL-on-Hadoop: Wrap Up

Section 12: Distributed Commit Log

Lecture 110 Data Architectures

Lecture 111 Introduction to Distributed Commit Logs

Lecture 112 Apache Kafka

Lecture 113 Confluent Platform Installation

Lecture 114 Data Modeling in Kafka I

Lecture 115 Data Modeling in Kafka II

Lecture 116 Data Generation for Testing

Lecture 117 Use case: Toll fee Collection

Lecture 118 Stream processing

Lecture 119 Stream Processing II with Stream + Connect APIs

Lecture 120 Example: Kafka Streams

Lecture 121 KSQL : Streaming Processing in SQL

Lecture 122 KSQL: Example

Lecture 123 Demonstration: NYC Taxi and Fares

Lecture 124 Streaming: Wrap Up

Section 13: Summary

Lecture 125 Database Polyglot

Lecture 126 Extending your knowledge

Lecture 127 Data Visualization

Lecture 128 Building a Data-driven Organization – Conclusion

Lecture 129 Conclusion

Chief Data Officers,IT Decision Makers,Database Architects,Software Developers,Big data Engineers,Anyone who wants to understand the where each NoSQL class of database best fits.,Anyone who is curious about NoSQL or Big Data Systems

Course Information:

Udemy | English | 22h 7m | 9.04 GB
Created by: Michael Enudi

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