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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
Course Overview
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
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Relational Database Systems, their features, use cases and limitations
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Why NoSQL?
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CAP Theorem
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Key-Value store and their use cases
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Document-oriented databases and their use cases
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Wide-columnar store and their use cases
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Time-series databases and their use cases
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Search Engines and their use cases
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Graph databases and their use cases
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Distributed Logs and real time streaming systems
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Hadoop and its use cases
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SQL-on-Hadoop tools and their use cases
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How to make informed decisions in building a good data storage platform
What is the target audience?
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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.
Pre-requisites
- 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)
Target Audience
- 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
Curriculum 129 Lectures 19:51:53
-
Section 1 : Introduction
- Lecture 2 :
- Data Engineering
- Lecture 3 :
- Learning Environment & Course Material
- Lecture 4 :
- Movielens Dataset
- Lecture 5 :
- Introduction
-
Section 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
-
Section 3 : Database Classification
- Lecture 1 :
- Distributed Databases
- Lecture 2 :
- CAP Theorem
- Lecture 3 :
- BASE
- Lecture 4 :
- Other Classification
-
Section 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
-
Section 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
-
Section 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
-
Section 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
-
Section 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
-
Section 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
-
Section 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
- Lecture 5 :
- Data Processing in Hadoop (MapReduce)
- Lecture 6 :
- Examples in MapReduce
- Lecture 7 :
- Data Processing in Hadoop (Pig)
- Lecture 8 :
- Examples in Pig
- Lecture 9 :
- Data Processing in Hadoop (Spark)
- Lecture 10 :
- Examples in Spark
- Lecture 11 :
- Data Analytics with Apache Spark
- Lecture 12 :
- Data Compression
- Lecture 13 :
- Data serialization and storage formats
- Lecture 14 :
- Hadoop: Wrap Up
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Section 11 : Big Data SQL Engines
- Lecture 1 :
- Introduction Big Data SQL Engines
- Lecture 2 :
- Apache Hive
- Lecture 3 :
- Apache Hive : Demonstration
- Lecture 4 :
- MPP SQL-on-Hadoop: Introduction
- Lecture 5 :
- Impala
- Lecture 6 :
- Impala : Demonstration
- Lecture 7 :
- PrestoDB
- Lecture 8 :
- PrestoDB : Demonstration
- Lecture 9 :
- SQL-on-Hadoop: Wrap Up
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Section 12 : Distributed Commit Log
- Lecture 1 :
- Data Architectures
- Lecture 2 :
- Introduction to Distributed Commit Logs
- Lecture 3 :
- Apache Kafka
- Lecture 4 :
- Confluent Platform Installation
- Lecture 5 :
- Data Modeling in Kafka I
- Lecture 6 :
- Data Modeling in Kafka II
- Lecture 7 :
- Data Generation for Testing
- Lecture 8 :
- Use case: Toll fee Collection
- Lecture 9 :
- Stream processing
- Lecture 10 :
- Stream Processing II with Stream + Connect APIs
- Lecture 11 :
- Example: Kafka Streams
- Lecture 12 :
- KSQL : Streaming Processing in SQL
- Lecture 13 :
- KSQL: Example
- Lecture 14 :
- Demonstration: NYC Taxi and Fares
- Lecture 15 :
- Streaming: Wrap Up
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Section 13 : Summary
- Lecture 1 :
- Database Polyglot
- Lecture 2 :
- Extending your knowledge
- Lecture 3 :
- Data Visualization
- Lecture 4 :
- Building a Data-driven Organization - Conclusion
- Lecture 5 :
- Conclusion
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Instructor
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A big data specialist residing in Johannesburg, South Africa with 14 years of experience in various areas of software development include enterprise, mobile and database application. Michael currently holds a Big Data Tech Specialist role in StructureIT where he uses software and big data technologies to build a data platform used by financial institutions for make financial research and investment decisions. His passion includes learning new technologies, implementing enterprise software and/or data systems and sharing the knowledge.
For the past four years, Michael has also been a Hadoop and Big data instructor/trainer at Dezyre (.com) academy where has trained over 300 students in 4 different continents in various topics like Hadoop, NoSQL and other big data technologies. These training sessions usually take place in form of a small group of individuals or in a one-on-one webinar meeting.
When he is not writing or learning software, Michael spends most of his time with family, or writing and producing Music with his wife.
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