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
Requirements
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)
Description
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.
Overview
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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