What is NoSQL Database?

What Is NoSQL Database | NoSQL vs SQL Difference | NoSQL Tutorial For Beginners

This video is based on NoSQL tutorial. The topics covered in this NoSQL tutorial video are essential for mastering NoSQL. In this tutorial we will cover what is NoSQL, difference between RDBMS and NoSQL databases, benefits of NoSQL and types of NoSQL. NoSQL (commonly referred to as "Not Only SQL") represents a completely different framework of databases that allows for high-performance, agile processing of information at massive scale.

What is NoSQL?

In this article, you'll learn what a NoSQL database is, why (and when!) you should use one, and how to get started.

NoSQL databases (aka "not only SQL") are non-tabular databases and store data differently than relational tables. NoSQL databases come in a variety of types based on their data model. The main types are document, key-value, wide-column, and graph. They provide flexible schemas and scale easily with large amounts of data and high user loads.

What is a NoSQL database?

When people use the term “NoSQL database,” they typically use it to refer to any non-relational database. Some say the term “NoSQL” stands for “non SQL” while others say it stands for “not only SQL.” Either way, most agree that NoSQL databases are databases that store data in a format other than relational tables.

Brief history of NoSQL databases

NoSQL databases emerged in the late 2000s as the cost of storage dramatically decreased. Gone were the days of needing to create a complex, difficult-to-manage data model in order to avoid data duplication. Developers (rather than storage) were becoming the primary cost of software development, so NoSQL databases optimized for developer productivity.

As storage costs rapidly decreased, the amount of data that applications needed to store and query increased. This data came in all shapes and sizes — structured, semi-structured, and polymorphic — and defining the schema in advance became nearly impossible. NoSQL databases allow developers to store huge amounts of unstructured data, giving them a lot of flexibility.

Additionally, the Agile Manifesto was rising in popularity, and software engineers were rethinking the way they developed software. They were recognizing the need to rapidly adapt to changing requirements. They needed the ability to iterate quickly and make changes throughout their software stack — all the way down to the database. NoSQL databases gave them this flexibility.

Cloud computing also rose in popularity, and developers began using public clouds to host their applications and data. They wanted the ability to distribute data across multiple servers and regions to make their applications resilient, to scale out instead of scale up, and to intelligently geo-place their data. Some NoSQL databases like MongoDB provide these capabilities.

NoSQL database features

Each NoSQL database has its own unique features. At a high level, many NoSQL databases have the following features:

Flexible schemas
Horizontal scaling
Fast queries due to the data model
Ease of use for developers

Types of NoSQL databases

Over time, four major types of NoSQL databases emerged: document databases, key-value databases, wide-column stores, and graph databases.

Document databases store data in documents similar to JSON (JavaScript Object Notation) objects. Each document contains pairs of fields and values. The values can typically be a variety of types including things like strings, numbers, booleans, arrays, or objects.
Key-value databases are a simpler type of database where each item contains keys and values.
Wide-column stores store data in tables, rows, and dynamic columns.
Graph databases store data in nodes and edges. Nodes typically store information about people, places, and things, while edges store information about the relationships between the nodes.

Difference between RDBMS and NoSQL databases

While a variety of differences exist between relational database management systems (RDBMS) and NoSQL databases, one of the key differences is the way the data is modeled in the database. In this section, we'll work through an example of modeling the same data in a relational database and a NoSQL database. Then, we'll highlight some of the other key differences between relational databases and NoSQL databases.

RDBMS vs NoSQL: Data Modeling Example

Let's consider an example of storing information about a user and their hobbies. We need to store a user's first name, last name, cell phone number, city, and hobbies.

In a relational database, we'd likely create two tables: one for Users and one for Hobbies.

Users

ID	first_name	last_name	cell	city
1	Leslie	Yepp	8125552344	Pawnee

Hobbies

ID	user_id	hobby
10	1	scrapbooking
11	1	eating waffles
12	1	working

In order to retrieve all of the information about a user and their hobbies, information from the Users table and Hobbies table will need to be joined together.

The data model we design for a NoSQL database will depend on the type of NoSQL database we choose. Let's consider how to store the same information about a user and their hobbies in a document database like MongoDB.

{
   "_id": 1,
   "first_name": "Leslie",
   "last_name": "Yepp",
   "cell": "8125552344",
   "city": "Pawnee",
   "hobbies": ["scrapbooking", "eating waffles", "working"]
}

In order to retrieve all of the information about a user and their hobbies, a single document can be retrieved from the database. No joins are required, resulting in faster queries.

Other differences between RDBMS and relational databases

While the example above highlights the differences in data models between relational databases and NoSQL databases, many other important differences exist, including:

Flexibility of the schema
Scaling technique
Support for transactions
Reliance on data to object mapping

Why NoSQL?

NoSQL databases are used in nearly every industry. Use cases range from the highly critical (e.g., storing financial data and healthcare records) to the more fun and frivolous (e.g., storing IoT readings from a smart kitty litter box).

In the following sections, we'll explore when you should choose to use a NoSQL database and common misconceptions about NoSQL databases.

When should NoSQL be used?

When deciding which database to use, decision-makers typically find one or more of the following factors lead them to selecting a NoSQL database:

Fast-paced Agile development
Storage of structured and semi-structured data
Huge volumes of data
Requirements for scale-out architecture
Modern application paradigms like microservices and real-time streaming

NoSQL database misconceptions

Over the years, many misconceptions about NoSQL databases have spread throughout the developer community. In this section, we'll discuss two of the most common misconceptions:

Relationship data is best suited for relational databases.
NoSQL databases don't support ACID transactions.

Misconception: relationship data is best suited for relational databases

A common misconception is that NoSQL databases or non-relational databases don’t store relationship data well. NoSQL databases can store relationship data — they just store it differently than relational databases do.

In fact, when compared with relational databases, many find modeling relationship data in NoSQL databases to be easier than in relational databases, because related data doesn’t have to be split between tables. NoSQL data models allow related data to be nested within a single data structure.

Misconception: NoSQL databases don't support ACID transactions

Another common misconception is that NoSQL databases don't support ACID transactions. Some NoSQL databases like MongoDB do, in fact, support ACID transactions.

Note that the way data is modeled in NoSQL databases can eliminate the need for multi-record transactions in many use cases. Consider the earlier example where we stored information about a user and their hobbies in both a relational database and a document database. In order to ensure information about a user and their hobbies was updated together in a relational database, we'd need to use a transaction to update records in two tables. In order to do the same in a document database, we could update a single document — no multi-record transaction required.

NoSQL query tutorial

A variety of NoSQL databases exist. Today, we'll be trying MongoDB, the world's most popular NoSQL database according to DB-Engines.

In this tutorial, you'll load a sample database and learn to query it — all without installing anything on your computer or paying anything.

Authenticate to MongoDB Atlas

The easiest way to get started with MongoDB is MongoDB Atlas. Atlas is MongoDB's fully managed database-as-a-service. Atlas has a forever free tier, which is what you'll be using today.

Navigate to Atlas.
Create an account if you haven't already.
Log into Atlas.
Create an Atlas organization and project.

Create a cluster and a database

A cluster is a place where you can store your MongoDB databases. In this section, you'll create a free cluster.

Once you have a cluster, you can begin storing data in Atlas. You could choose to manually create a database in the Atlas Data Explorer, in the MongoDB Shell, in MongoDB Compass, or using your favorite programming language. Instead, in this example, you will import Atlas's sample dataset.

Create a free cluster by following the steps in the official MongoDB documentation.
Load the sample dataset by following the instructions in the official MongoDB documentation.

Loading the sample dataset will take several minutes.

While we don't need to think about database design for this tutorial, note that database design and data modeling are major factors in MongoDB performance.

Query the database

Now that you have data in your cluster, let's query it! Just like you had multiple ways to create a database, you have multiple options for querying a database: in the Atlas Data Explorer, in the MongoDB Shell, in MongoDB Compass, or using your favorite programming language.

In this section, you’ll query the database using the Atlas Data Explorer. This is a good way to get started querying, as it requires zero setup.

Navigate to the Data Explorer (the Collections tab), if you are not already there. See the official MongoDB documentation for information on how to navigate to the Data Explorer.

The left panel of the Data Explorer displays a list of databases and collections in the current cluster. The right panel of the Data Explorer displays a list of documents in the current collection.

A screenshot of the Collections tab in Atlas

The Data Explorer displays a list of documents in the listingsAndReviews collection.

Expand the sample_mflix database in the left panel. A list of the database's collections is displayed.

Select the movies collection. The Find View is displayed in the right panel. The first twenty documents of the results are displayed.

You are now ready to query the movies collection. Let's query for the movie Pride and Prejudice. In the query bar, input { title: "Pride and Prejudice"} in the query bar and click Apply.

Two documents with the title “Pride and Prejudice” are returned.

A screenshot of the query bar and results in the Atlas Data Explorer. A query { title: "Pride and Prejudice"} is in the query bar. Two documents with the title "Pride and Prejudice" are returned. The results for querying for movies with the title "Pride and Prejudice".

Congrats! You've successfully queried a NoSQL database!

Continue exploring your data

In this tutorial, we only scratched the surface of what you can do in MongoDB and Atlas.
Continue interacting with your data by using the Data Explorer to insert new documents, edit existing documents, and delete documents.

When you are ready to try more advanced queries that aggregate your data, create an aggregation pipeline. The aggregation framework is an incredibly powerful tool for analyzing your data. To learn more, take the free MongoDB University Course M121 The MongoDB Aggregation Framework.

When you want to visualize your data, check out MongoDB Charts. Charts is the easiest way to visualize data stored in Atlas and Atlas Data Lake. Charts allows you to create dashboards that are filled with visualizations of your data.

Summary

NoSQL databases provide a variety of benefits including flexible data models, horizontal scaling, lightning fast queries, and ease of use for developers. NoSQL databases come in a variety of types including document databases, key-values databases, wide-column stores, and graph databases.

#database #nosql #sql #mongodb #mysql

What is GEEK

Buddha Community

Kole Haag

1602403200

What is NoSQL and How is it Utilized?

Posted on September 25, 2020 by Dean Conally | Updated: October 8, 2020

Category: Tutorials | Tags: Cassandra, Columns, Database, Database Management, Database Structure, DB2, Document Stores, Dynamic Schema, Extensible Record Stores, Graph Stores, JSON, Key-Value, MSSQL, Multi-Row, MySQL, Node, Node Relationship Node, Non-Relational Databases, NoSQL, NoSQL Model, Query, Rows, Scalability, Schema Free, SQL, Stores, Tables, Wide-Column

Reading Time: 5 minutes

What is NoSQL?

A NoSQL or a NoSQL Database is a term used when referring to a “non SQL” or “not only SQL” database. NoSQL databases store data in a different format than a traditional relational database management systems. This is why NoSQL is often associated with the term “non-relational” database. Simply put, NoSQL databases are modern databases with high flexibility, blazing performance, and built for scalability. These databases are used when you require low latency and high extensibility while working with large data structures. The versatility of NoSQL is due to the nature of as being unrestricted in comparison to relational databases models such as MySQL or DB2.

SQL vs. NoSQL Comparison

There are multiple differences between SQL and NoSQL database types. In the table below, we will compare some of the most critical variations.

#tutorials #cassandra #columns #database #database management #database structure #db2 #document stores #dynamic schema #extensible record stores #graph stores #json #key-value #mssql #multi-row #mysql #node #node relationship node #non-relational databases #nosql #nosql model #query #rows #scalability #schema free #sql #stores #tables #wide-column

Ruth Nabimanya

1620633584

System Databases in SQL Server

Introduction

In SSMS, we many of may noticed System Databases under the Database Folder. But how many of us knows its purpose?. In this article lets discuss about the System Databases in SQL Server.

System Database

Fig. 1 System Databases

There are five system databases, these databases are created while installing SQL Server.

Master
Model
MSDB
Tempdb
Resource

Master

This database contains all the System level Information in SQL Server. The Information in form of Meta data.
Because of this master database, we are able to access the SQL Server (On premise SQL Server)

Model

This database is used as a template for new databases.
Whenever a new database is created, initially a copy of model database is what created as new database.

MSDB

This database is where a service called SQL Server Agent stores its data.
SQL server Agent is in charge of automation, which includes entities such as jobs, schedules, and alerts.

TempDB

The Tempdb is where SQL Server stores temporary data such as work tables, sort space, row versioning information and etc.
User can create their own version of temporary tables and those are stored in Tempdb.
But this database is destroyed and recreated every time when we restart the instance of SQL Server.

Resource

The resource database is a hidden, read only database that holds the definitions of all system objects.
When we query system object in a database, they appear to reside in the sys schema of the local database, but in actually their definitions reside in the resource db.

#sql server #master system database #model system database #msdb system database #sql server system databases #ssms #system database #system databases in sql server #tempdb system database

Ruth Nabimanya

1620648300

What is SQL? And Where is it Used?

Define: SQL [pron. “sequel”] – stands for Structured Query Language (SQL), used by databases to model and manage tabular/relational datasets; a set of standardized Data Definition Language (DDL) functions to create tables, views, and define relational schema models, and Data Manipulation Language (DML) to query, insert, and modify data in the tables.

*Read-only select queries are technically part of its Data Query Language (DQL) group. Still, operationally, many refer to it as DML because it can do more than read-only queries.

Super Brief History of SQL

Who Uses SQL?

What Can a SQL Database Do?

What is a SQL Query?

Different SQL Database Platforms

SQL for JSON

Application Developers

SQL for Big Data

Django-allauth: A simple Boilerplate to Setup Authentication

Django-Authentication

A simple Boilerplate to Setup Authentication using Django-allauth, with a custom template for login and registration using django-crispy-forms.

Getting Started

Prerequisites

Python 3.8.6 or higher

Project setup

# clone the repo
$ git clone https://github.com/yezz123/Django-Authentication

# move to the project folder
$ cd Django-Authentication

Creating virtual environment

Create a virtual environment for this project:

# creating pipenv environment for python 3
$ virtualenv venv

# activating the pipenv environment
$ cd venv/bin #windows environment you activate from Scripts folder

# if you have multiple python 3 versions installed then
$ source ./activate

Configured Enviromment

Environment variables

SECRET_KEY = #random string
DEBUG = #True or False
ALLOWED_HOSTS = #localhost
DATABASE_NAME = #database name (You can just use the default if you want to use SQLite)
DATABASE_USER = #database user for postgres
DATABASE_PASSWORD = #database password for postgres
DATABASE_HOST = #database host for postgres
DATABASE_PORT = #database port for postgres
ACCOUNT_EMAIL_VERIFICATION = #mandatory or optional
EMAIL_BACKEND = #email backend
EMAIL_HOST = #email host
EMAIL_HOST_PASSWORD = #email host password
EMAIL_USE_TLS = # if your email use tls
EMAIL_PORT = #email port

change all the environment variables in the .env.sample and don't forget to rename it to .env.

Run the project

After Setup the environment, you can run the project using the Makefile provided in the project folder.

help:
 @echo "Targets:"
 @echo "    make install" #install requirements
 @echo "    make makemigrations" #prepare migrations
 @echo "    make migrations" #migrate database
 @echo "    make createsuperuser" #create superuser
 @echo "    make run_server" #run the server
 @echo "    make lint" #lint the code using black
 @echo "    make test" #run the tests using Pytest

Preconfigured Packages

Includes preconfigured packages to kick start Django-Authentication by just setting appropriate configuration.

Package	Usage
django-allauth	Integrated set of Django applications addressing authentication, registration, account management as well as 3rd party (social) account authentication.
django-crispy-forms	django-crispy-forms provides you with a `crispy` filter and `{% crispy %}` tag that will let you control the rendering behavior of your Django forms in a very elegant and DRY way.

Contributing

Django-Authentication is a simple project, so you can contribute to it by just adding your code to the project to improve it.
If you have any questions, please feel free to open an issue or create a pull request.

Download Details:
Author: yezz123
Source Code: https://github.com/yezz123/Django-Authentication
License: MIT License

#django #python

Asp Dot Net Explorer

1607019321

NoSQL Database | What is NoSQL | Mongo DB | Asp.Net Core MVC

https://www.youtube.com/watch?v=FdIfWLCn_vA&t=10s

#mongo db #asp.net core mvc #nosql database #mongodb database #nosql

What is NoSQL Database?