SQL, NoSQL, Big Data and Hadoop

SQL, NoSQL, Big Data and Hadoop, available at $69.99, has an average rating of 4.25, with 129 lectures, based on 445 reviews, and has 4424 subscribers.

You will learn about 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 This course is ideal for individuals who are Chief Data Officers or IT Decision Makers or Database Architects or Software Developers or Big data Engineers or Anyone who wants to understand the where each NoSQL class of database best fits. or Anyone who is curious about NoSQL or Big Data Systems It is particularly useful for Chief Data Officers or IT Decision Makers or Database Architects or Software Developers or Big data Engineers or Anyone who wants to understand the where each NoSQL class of database best fits. or Anyone who is curious about NoSQL or Big Data Systems.

Enroll now: SQL, NoSQL, Big Data and Hadoop

Summary

Title: SQL, NoSQL, Big Data and Hadoop

Price: $69.99

Average Rating: 4.25

Number of Lectures: 129

Number of Published Lectures: 129

Number of Curriculum Items: 129

Number of Published Curriculum Objects: 129

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

A comprehensive look at the wide landscape of database systems and how to make a good choice in your next project

The 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 limitations

Why NoSQL?

CAP Theorem

Key-Value store and their use cases

Document-oriented databases and their use cases

Wide-columnar store and their use cases

Time-series databases and their use cases

Search Engines and their use cases

Graph databases and their use cases

Distributed Logs and real time streaming systems

Hadoop and its use cases

SQL-on-Hadoop tools and their use cases

How to make informed decisions in building a good data storage platform

What is the target audience?

Chief data officers

Application developer

Data analyst

Data architects

Data engineers

Students

Anyone 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.

Course Curriculum

Chapter 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

Chapter 2: Relational Database Systems

Lecture 1: Introduction to Relational Databases

Lecture 2: SQL

Lecture 3: Movielens Relational Model

Lecture 4: Movielens Relational Model: Normalization vs Denormalization

Lecture 5: MySQL

Lecture 6: Movielens in MySQL: Database import

Lecture 7: OLTP in RDBMS: CRUD Applications

Lecture 8: Indexes

Lecture 9: Data Warehousing

Lecture 10: Analytical Processing

Lecture 11: Transaction Logs

Lecture 12: Relational Databases – Wrap Up

Chapter 3: Database Classification

Lecture 1: Distributed Databases

Lecture 2: CAP Theorem

Lecture 3: BASE

Lecture 4: Other Classification

Chapter 4: Key-Value Store

Lecture 1: Introduction to KV Stores

Lecture 2: Redis

Lecture 3: Install Redis

Lecture 4: Time Complexity of Algorithm

Lecture 5: Data Structures in Redis : Key & String

Lecture 6: Data Structures in Redis II : Hash & List

Lecture 7: Data structures in Redis III : Set & Sorted Set

Lecture 8: Data structures in Redis IV : Geo & HyperLogLog

Lecture 9: Data structures in Redis V : Pubsub & Transaction

Lecture 10: Modelling Movielens in Redis

Lecture 11: Redis Example in Application

Lecture 12: KV Stores: Wrap Up

Chapter 5: Document-Oriented Databases

Lecture 1: Introduction to Document-Oriented Databases

Lecture 2: MongoDB

Lecture 3: MongoDB installation

Lecture 4: Movielens in MongoDB

Lecture 5: Movielens in MongoDB: Normalization vs Denormalization

Lecture 6: Movielens in MongoDB: Implementation

Lecture 7: CRUD Operations in MongoDB

Lecture 8: Indexes

Lecture 9: MongoDB Aggregation Query – MapReduce function

Lecture 10: MongoDB Aggregation Query – Aggregation Framework

Lecture 11: Demo: MySQL vs MongoDB. Modeling with Spark

Lecture 12: Document Stores: Wrap Up

Chapter 6: Search Engine

Lecture 1: Introduction to Search Engine Stores

Lecture 2: Elasticsearch

Lecture 3: Basic Terms Concepts and Description

Lecture 4: Movielens in Elastisearch

Lecture 5: CRUD in Elasticsearch

Lecture 6: Search Queries in Elasticsearch

Lecture 7: Aggregation Queries in Elasticsearch

Lecture 8: The Elastic Stack (ELK)

Lecture 9: Use case: UFO Sighting in ElasticSearch

Lecture 10: Search Engines: Wrap Up

Chapter 7: Wide Column Store

Lecture 1: Introduction to Columnar databases

Lecture 2: HBase

Lecture 3: HBase Architecture

Lecture 4: HBase Installation

Lecture 5: Apache Zookeeper

Lecture 6: Movielens Data in HBase

Lecture 7: Performing CRUD in HBase

Lecture 8: SQL on HBase – Apache Phoenix

Lecture 9: SQL on HBase – Apache Phoenix – Movielens

Lecture 10: Demo : GeoLife GPS Trajectories

Lecture 11: Wide Column Store: Wrap Up

Chapter 8: Time Series Databases

Lecture 1: Introduction to Time Series

Lecture 2: InfluxDB

Lecture 3: InfluxDB Installation

Lecture 4: InfluxDB Data Model

Lecture 5: Data manipulation in InfluxDB

Lecture 6: TICK Stack I

Lecture 7: TICK Stack II

Lecture 8: Time Series Databases: Wrap Up

Chapter 9: Graph Databases

Lecture 1: Introduction to Graph Databases.

Lecture 2: Modelling in Graph

Lecture 3: Modelling Movielens as a Graph

Lecture 4: Neo4J

Lecture 5: Neo4J installation

Lecture 6: Cypher

Lecture 7: Cypher II

Lecture 8: Movielens in Neo4J: Data Import

Lecture 9: Movielens in Neo4J: Spring Application

Lecture 10: Data Analysis in Graph Databases

Lecture 11: Examples of Graph Algorithms in Neo4J

Lecture 12: Graph Databases: Wrap Up

Chapter 10: Hadoop Platform

Lecture 1: Introduction to Big Data With Apache Hadoop

Lecture 2: Big Data Storage in Hadoop (HDFS)

Lecture 3: Big Data Processing : YARN

Lecture 4: Installation

Instructors

Michael Enudi
Okmich

Rating Distribution

1 stars: 12 votes

2 stars: 18 votes

3 stars: 55 votes

4 stars: 125 votes

5 stars: 235 votes

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