Duration

3 days

OVERVIEW

The market is abuzz with terms like NoSQL, Big Data etc. IT decision makers get very confused why they should consider a newer, alternative database when RDBMSs have been around for 20+ years. The characteristics of the data play a major role in selecting the right database. Data such as geo-spatial, engineering parts or molecular modelling requires multiple levels of nesting and complete data model which cannot fit into two-dimensional row-column structure naturally. Multi-level nesting and hierarchies are easily represented in NoSQL products. The next characteristics of the data are volatility of the data model, how frequently does it change. The modern technology world requires dynamic schema, where changes need to be made daily, to fit the ever changing data model.

There are a few popular open source NoSQL databases in market such as Apache CouchDB, MongoDB, Reddis and etc. Those databases are easy scalable, document oriented and NoSQL databases. Most of these databases use JSON or in-memory key-value to store data. NoSQL databases are designed for data whose relations are well represented as a graph consisting of elements interconnected with a finite number of relations between them. This data could be social relations, network topologies, public transport links or road maps.

MongoDB is an open-source document oriented database. MongoDB provides JSON-like structured documents and Dynamic Schema with a highly scalable and performance oriented database. MongoDB has become one of the leading NoSQL databases, helping businesses to rapidly react to new requirements and scale up capabilities. Apache CouchDB focuses on ease of use and having a scalable architecture. It has a document-oriented NoSQL database architecture and it uses JSON to store data, JavaScript as its query language using MapReduce, and HTTP for an API. CouchDB database maintains its data as a collection of independent documents. In CourchDB document metadata contains revision information. CouchDB also implements a form of multi version concurrency control (MVCC) so it does not lock the database file during writes. This course focus on various NoSQL concepts and participants get the hands on experience using MongoDB and Apache CouchDB.

OBJECTIVES

Understand NoSQL concepts
Understand categories of NoSQL Databases
Understand how data stored in a NoSQL data storage
Architecture of Mongo & CouchDB
Understand NoSQL database queries and update languages
Indexing and searching in NoSQL Databases
Reducing data via reduce function
Securing NoSQL Database
Clustering and Scaling of NoSQL Databases

PREREQUISITES

Participant should have a basic understanding of database, text editor and execution of programs. Because during the course participant is required to develop high performance database, so it will be good if the participant has an understanding on the basic concepts of Database (RDBMS).

Day 1

Introduction and Defining NoSQL Database

Introduction

What is NoSQL
What NoSQL Database can do
Categories of NoSQL Databases
Installing MongoDB
Installing CouchDB
NoSQL Client
Introduction to MongoDB
Overview of MongoDB
MongoDB fundamentals
Architecture of MongoDB
Database
Collection
Document
Advantages of MongoDB
Why use MongoDB
When to use MongoDB
Data Modelling
Create Database
Drop Database
Create Collection
Drop Collection
Data Types
Insert Document
Query Document
Update Document
Delete Document
Queries
Field equality tests
Operators available
Projections
Limiting Records
Paging
Sorting Records
Simple Updates and Deletes
Field updates
Field insertions and removal
Document deletion
Existential field values

Day 2

Indexing and query optimization
Indexing theory
Indexing in practice
Query optimization
Join $lookup
Replication
Replication overview
Replica sets
Master - slave replication
Drivers and replication
Sharding
Sharding overview
A sample shard cluster
Querying and indexing a shard cluster
Choosing a shard key
Sharding in production
Introduction to CouchDB
Overview of CouchDB
CouchDB fundamentals
Architecture of CouchDB
Database
Document
Advantages of CouchDB
Why use CouchDB
When to use CouchDB
Data Modelling
Create Database
Drop Database
Create Collection
Drop Collection
Data Types
Insert Document
Query Document
Update Document
Delete Document
Indexing and query optimization
Indexing theory
Indexing in practice
Query optimization
Join $lookup
Replication
Replication overview
Replica sets
Master - slave replication
Drivers and replication

Day 3

Introduction to Redis

13.1 Redis Compared to Other Databases and Software

13.2 Features

13.3 Why Redis?

What Redis Data Structures Look Like
Strings in Redis
Lists in Redis
Sets in Redis
Hashes in Redis
Sorted sets in Redis
Redis Web Applications
Login and Cookie Caching
Shopping Carts in Redis
Web Page Caching
Database Row Caching
Web Page Analytics
Commands in Redis
Strings
Lists Sets
Hashes
Sorted Sets
Publish/Subscribe
Sorting
Transactions
Expiring Keys
Data Management
Persistence Options
Snapshots
Append-only File Persistence
Rewriting/Compacting Append-only Files
Replication
Configuring for Replication
Master/Slave Chains
Verifying Disk Writes
System Failures
Search-based Applications

18.1 Searching in Redis

18.2 Basic Search Theory

18.3 Sorting Search Results

18.4 Sorted Indexes

18.5 Sorting Search Results with ZSETs

Reducing Memory Use

19.1 Short Structures

19.2 The ziplist Representation

19.3 The intset Encoding for SETs

19.4 Performance Issues for Long ziplists and intsets

19.5 Sharded Structures

19.6 HASHes

19.7 SETs

Scaling Redis

20.1 Scaling Reads

20.2 Scaling Writes and Memory Capacity

20.3 Handling Shard Configuration

20.4 Creating a Server-sharded Connection Decorator

20.5 Scaling Complex Queries

20.6 Scaling Search Query Volume

20.7 Scaling Search Index Size

20.8 Scaling a Social Network

Programme scope, delivery arrangements and suitability are confirmed during enquiry.