The document provides information about NoSQL databases. Some key points:
- NoSQL databases differ from relational databases in that they do not separate schema from data. This makes NoSQL better for storing semi-structured data like text.
- NoSQL databases like MongoDB and Couchbase can natively store semi-structured documents in JSON format.
- NoSQL databases are increasingly used for real-time web applications due to their ability to handle unstructured and semi-structured data.
The Relational Database System is basic database used from many decades. Since Mysql, Oracle are used for relational kind of databases but Nowadays structure of data has been changed. The problem of Data storage has been raised. Different form of data is available i.e. multimedia databases which is difficult to store. MongoDb can be future alternative for Relational Database. This paper gives overview of NoSQL database MongoDb. This paper is evaluation of NoSQL classification, features and benefits. This paper include Case study on MongoDb which consists of MongoDB web Shell, Architecture and Storage engines and protocols that are included in MongoDB web shell. Deepa Suresh Wahane | Prof. Mayuri Dhondiba Dendge"Analysis on NoSQL: MongoDB Tool" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd13089.pdf http://www.ijtsrd.com/computer-science/database/13089/analysis-on-nosql-mongodb-tool/deepa-suresh-wahane
Everything You Need to Know About MongoDB Development.pptx75waytechnologies
Today, organizations from different verticals want to harness the power of data to grab new business opportunities and touch new heights of success. Such an urge leads them to follow unique ways to use and handle data effectively. After all, the right use of data boosts the ability to make business decisions faster. But at the same time, working with data is not as easy as a walk in the garden. It sometimes turns out to be a long-standing problem for businesses that also affects their overall functioning.
Companies expect fast phase development and better data management in every scenario. Modern web-based applications development demands a quality working system that can be deployed faster, and the application is able to scale in the future as per the constantly changing environment.
Earlier, relational databases were used as a primary data store for web application development. But today, developers show a high interest in adopting alternative data stores for modern applications such as NoSQL (Not Only Structured Query Language) because of its incredible benefits. And if you ask us, one of the technologies that can do wonders in modern web-based application development is MongoDB.
MongoDB is the first name strike in our heads when developing scalable applications with evolving data schemas. Because MongoDB is a document database, it makes it easier for developers to store both structured and unstructured data. Stores and handles large amounts of data quickly, MongoDB is undoubtedly the smart move toward building scalable and data-driven applications. If you’re wondering what MongoDB is and how it can help your digital success, this blog is surely for you.
The Relational Database System is basic database used from many decades. Since Mysql, Oracle are used for relational kind of databases but Nowadays structure of data has been changed. The problem of Data storage has been raised. Different form of data is available i.e. multimedia databases which is difficult to store. MongoDb can be future alternative for Relational Database. This paper gives overview of NoSQL database MongoDb. This paper is evaluation of NoSQL classification, features and benefits. This paper include Case study on MongoDb which consists of MongoDB web Shell, Architecture and Storage engines and protocols that are included in MongoDB web shell. Deepa Suresh Wahane | Prof. Mayuri Dhondiba Dendge"Analysis on NoSQL: MongoDB Tool" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd13089.pdf http://www.ijtsrd.com/computer-science/database/13089/analysis-on-nosql-mongodb-tool/deepa-suresh-wahane
Everything You Need to Know About MongoDB Development.pptx75waytechnologies
Today, organizations from different verticals want to harness the power of data to grab new business opportunities and touch new heights of success. Such an urge leads them to follow unique ways to use and handle data effectively. After all, the right use of data boosts the ability to make business decisions faster. But at the same time, working with data is not as easy as a walk in the garden. It sometimes turns out to be a long-standing problem for businesses that also affects their overall functioning.
Companies expect fast phase development and better data management in every scenario. Modern web-based applications development demands a quality working system that can be deployed faster, and the application is able to scale in the future as per the constantly changing environment.
Earlier, relational databases were used as a primary data store for web application development. But today, developers show a high interest in adopting alternative data stores for modern applications such as NoSQL (Not Only Structured Query Language) because of its incredible benefits. And if you ask us, one of the technologies that can do wonders in modern web-based application development is MongoDB.
MongoDB is the first name strike in our heads when developing scalable applications with evolving data schemas. Because MongoDB is a document database, it makes it easier for developers to store both structured and unstructured data. Stores and handles large amounts of data quickly, MongoDB is undoubtedly the smart move toward building scalable and data-driven applications. If you’re wondering what MongoDB is and how it can help your digital success, this blog is surely for you.
In my presentation i covered a few thing on NoSQL
What is NoSQL
NoSQL Features
Types of NoSQL
Advantages on NoSQL
and then i moved to MongoDB. This presentation deals with some basic question like
When do we embed data versus linking?
How many collections do we have, and what are they?
When do we need atomic operations?
What indexes will we create to make query and updates fast?
What is shard?
This presentation is related to nosql database and nosql database types information. this presentationa also contains discussion about, how mongodb works and mongodb security and mongodb sharding information.
Hands on Big Data Analysis with MongoDB - Cloud Expo Bootcamp NYCLaura Ventura
One of the most popular NoSQL databases, MongoDB is one of the building blocks for big data analysis. MongoDB can store unstructured data and makes it easy to analyze files by commonly available tools. This session will go over how big data analytics can improve sales outcomes in identifying users with a propensity to buy by processing information from social networks. All attendees will have a MongoDB instance on a public cloud, plus sample code to run Big Data Analytics.
This paper trying to focus on main features, advantages and applications of non-relational database namely Mongo DB and thus justifying why MongoDB is more suitable than relational databases in big data applications. The database used here for comparison with MongoDB is MySQL. The main features of MongoDB are flexibility, scalability, auto sharding and replication. MongoDB is used in big data and real time web applications since it is a leading database technology.
MongoDB.local Sydney: An Introduction to Document Databases with MongoDBMongoDB
This presentation will describe MongoDB's document database and what advantages it has over traditional databases. The presentation will explore MongoDB's server, query language, ecosystem and various tools. Brett will demonstrate using various MongoDB tools to assist in developing a Python application that utilises MongoDB as the database.
In my presentation i covered a few thing on NoSQL
What is NoSQL
NoSQL Features
Types of NoSQL
Advantages on NoSQL
and then i moved to MongoDB. This presentation deals with some basic question like
When do we embed data versus linking?
How many collections do we have, and what are they?
When do we need atomic operations?
What indexes will we create to make query and updates fast?
What is shard?
This presentation is related to nosql database and nosql database types information. this presentationa also contains discussion about, how mongodb works and mongodb security and mongodb sharding information.
Hands on Big Data Analysis with MongoDB - Cloud Expo Bootcamp NYCLaura Ventura
One of the most popular NoSQL databases, MongoDB is one of the building blocks for big data analysis. MongoDB can store unstructured data and makes it easy to analyze files by commonly available tools. This session will go over how big data analytics can improve sales outcomes in identifying users with a propensity to buy by processing information from social networks. All attendees will have a MongoDB instance on a public cloud, plus sample code to run Big Data Analytics.
This paper trying to focus on main features, advantages and applications of non-relational database namely Mongo DB and thus justifying why MongoDB is more suitable than relational databases in big data applications. The database used here for comparison with MongoDB is MySQL. The main features of MongoDB are flexibility, scalability, auto sharding and replication. MongoDB is used in big data and real time web applications since it is a leading database technology.
MongoDB.local Sydney: An Introduction to Document Databases with MongoDBMongoDB
This presentation will describe MongoDB's document database and what advantages it has over traditional databases. The presentation will explore MongoDB's server, query language, ecosystem and various tools. Brett will demonstrate using various MongoDB tools to assist in developing a Python application that utilises MongoDB as the database.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. NoSQL
Semi-structured data is also an important element of many NoSQL
(“not only SQL”) databases.
NoSQL databases differ from relational databases because they do not separate
the organization (schema) from the data.
This makes NoSQL a better choice to store information that does not easily fit into
the record and table format, such as text with varying lengths.
It also allows for easier data exchange between databases.
Some newer NoSQL databases like MongoDB and Couchbase also incorporate
semi-structured documents by natively storing them in the JSON format.
Prof.Indrani Sen MCA,MPhil Computer Science
3. What is NoSQL Database?
NoSQL databases basically called as Not Only SQL database.
These databases are non-relational database systems used for storing and
retrieving data.
Nowadays NoSQL Databases are predominantly utilized as real-time based web
applications.
NoSQL databases can therefore be called as Big Data databases or Cloud
databases.
NoSQL Databases are faster in compared to SQL Databases and thus it makes
major part of Data applications.
Prof.Indrani Sen MCA,MPhil Computer Science
4. Database design in relational models uses primary and foreign keys and follows
strict constraints when the tables are created.
The main motto of NoSQL is that the relational databases are good for smaller
data storage requirements,
but “big data” are mostly used to manage large queries.
To properly manage big data queries NoSQL databases are appropriate which
work differently than the relational databases.
Prof.Indrani Sen MCA,MPhil Computer Science
5. NoSQL features Result
Big Data types Structured,
unstructured and
semi-structured
Scalability Excellent
Biggest NoSQL
advantages
Large volumes of data,
dynamic schemas,
replication, auto-
sharding
Prof.Indrani Sen MCA,MPhil Computer Science
6. Why are NoSQL Databases so popular?
Elastic scalability – These databases are highly scalable and allow adding more hardware.
These are implemented to increase customers and provide service as per there requirement.
Architecture based – Lesser failures or system downtimes as these are continuously available for business-
critical applications.
Linear-scale performance – These are linearly scalable which increase the throughput of system which is
directly proportional to the number of nodes in the cluster. Therefore it helps to maintain a quick
response time.
Flexibility in data maintenance – The data storage includes: structured, semi-structured, and unstructured.
The changes are done as per our requirements.
Easy data distribution – Distribution of data can be replicated across multiple data centers.
Fast writes – These databases are designed to run on cheap commodity hardware. They perform write
operations in blazingly fast speed and store hundreds of terabytes of data, without affecting the read
efficiency.
Prof.Indrani Sen MCA,MPhil Computer Science
8. CouchDB
CouchDB is an Open Source NoSQL Database based on JSON to stores data and
JavaScript in the form of query language.
CouchDB is a multi-version controlling system for avoiding the blockage of the DB
file scripting.
It has been ranked as the best database in the year 2016 on the basis of its highest
popularity among all the NoSQL Databases.
Prof.Indrani Sen MCA,MPhil Computer Science
9. MongoDB
MongoDB is the most popular among NoSQL Databases which is a free and open-
source document-oriented database program.
It is a scalable and accessible database.
It is prescribed in C++ but uses JSON-like documents with schemas as JavaScript
can be utilized as the query language.
MongoDB scales horizontally which is preferable in JavaScript frameworks.
Prof.Indrani Sen MCA,MPhil Computer Science
10. Cassandra
Cassandra is a distributed data storage system from Apache for handling very large amounts of
structured data and highly scalable.
Essentially, these data are widely used across many commodity servers, providing high availability
with no failure till now.
It gives you the maximal flexibility for distribution of data.
It allows adding data storage to your service online and makes your task much easier.
There is no scope for complex configuration as all the nodes in a cluster are same.
Cassandra is java based programming language which supports MapReduce on Apache Hadoop.
Cassandra Query Language (CQL) is an SQL-like language for querying Cassandra Database.
Prof.Indrani Sen MCA,MPhil Computer Science
12. Properties
One such approach is the property graph model, where data is organized as
nodes, relationships, and properties
(data stored on the nodes or relationships).
Nodes are the entities in the graph.
They can hold any number of attributes (key-value pairs) called properties.
Nodes can be tagged with labels, representing their different roles in your
domain.
Relationships provide directed, named, semantically-relevant connections between
two node entities (e.g. Member ISSUE_RETURN Book).
Prof.Indrani Sen MCA,MPhil Computer Science
14. MongoDB
MONGODB IS A CROSS-PLATFORM, DOCUMENT ORIENTED DATABASE THAT
PROVIDES, HIGH PERFORMANCE, HIGH AVAILABILITY, AND EASY
SCALABILITY. MONGODB WORKS ON CONCEPT OF COLLECTION AND
DOCUMENT.
23. Creating database
The use Command
MongoDB
use DATABASE_NAME is used to create database. The command will create a new
database if it doesn't exist, otherwise it will return the existing database.
25. To list databases
>show dbs
local 0.78125GB
mydb 0.23012GB
test 0.23012GB
26. Deleting database
The dropDatabase() Method
db.dropDatabase() command is used to drop a existing database.
Syntax
db.dropDatabase()
This will delete the selected database.
If you have not selected any database, then it will delete default 'test' database.
27. Collections
MongoDB stores documents in collections.
Collections are analogous to tables in relational databases.
Create a Collection
If a collection does not exist, MongoDB creates the collection when you first store
data for that collection
28. Creating collection
The createCollection() Method
MongoDB db.createCollection(name, options) is used to create collection.
Syntax
db.createCollection(name, options) In the command, name is name of collection
to be created. Options is a document and is used to specify configuration of
collection likememory size etc.
29. List collections
>db.createCollection("mycollection")
using the command show collections.
>show collections
In MongoDB, you don't need to create collection. MongoDB creates collection
automatically, when you insert some document.
30. Documents
A record in MongoDB is a document, which is a data structure composed of field and value pairs.
MongoDB documents are similar to JavaScript Object Notation objects but use a variant called Binary
JSON (BSON) that accommodates more data types.
The fields in documents are akin to the columns in a relational database, and the values they contain can
be a variety of data types,
including other documents, arrays and arrays of documents
Documents, which also must incorporate a primary key as a unique identifier, are the basic unit of data in
MongoDB.
Collections contain sets of documents and function as the equivalent of relational database tables.
Collections can contain any type of data, but the restriction is the data in a collection cannot be spread
across different databases.
31.
32.
33. Advantages of mongodb
Flexible schema
No need of joins
Faster queries
Document based model
Efficient for Haddoop implementation
Prof.Indrani Sen MCA,MPhil Computer Science
35. Database
Database is a physical container for
collections. Each database gets its own set of
files on the file system.
A single MongoDB server typically has
multiple databases.
Prof.Indrani Sen MCA,MPhil Computer Science
36. Collection
Collection is a group of MongoDB documents.
It is the equivalent of an RDBMS table.
A collection exists within a single database.
Collections do not enforce a schema.
Documents within a collection can have different fields.
Typically, all documents in a collection are of similar or related purpose.
Prof.Indrani Sen MCA,MPhil Computer Science
37. Prof.Indrani Sen MCA,MPhil Computer Science
[Recipe_details
{recipe_id ,
Name:Lucknowi Mutton Biryani,
summary:Lucknowi Mutton Biryani is a very popular North Indian recipe. It is different
from the basic biryanis as it come straight from the Nawabs and is the soul of every
party. It is made from cashewnut paste, saffron, curd, star anise and mace powder that
give it an amazing flavour. In this delightful recipe, the process of marination is very
important. The longer you marinate the meat, the better is the taste of the biryani. It is
also important to soak the rice in water for sometime,
date_publish: 2017-05-15T15:45:37+05:30
category:Main
Cuisine:Awadhi
preptime:PT15M
Tottime:PT40M
Yield:4 Servings
40. Transactions
Transactions in a database environment have two main purposes:-
Reliability of data: In case of unpredictable circumstances such as power failures or
system failures while a database is getting updated or being read,
the transactions should ensure to give us consistent and accurate data.
Concurrent Access of data:When more than one user or application programs are
trying to access the same data,
the transactions should provide isolation between the programs accessing a database
concurrently.
Otherwise concurrent read or write operations to a database may lead to inconsistent
or erronaeous data.
41. Phases of transaction
Begin the transaction.
Execute a set of data manipulations and/or queries.
If no errors occur then commit the transaction and end it.
If errors occur then rollback the transaction and end it.
42. Atomicity
A transaction is an atomic unit of processing;
it is either performed in its entirety or not performed at all.
The atomicity property requires that we execute a transaction to completion.
It is the responsibility of the recovery subsystem of a DBMS to ensure atomicity.
If a transaction fails to complete for some reason, such as a system crash in the
midst of transaction execution,
the recovery technique must undo any effects of the transaction on the database.
44. Consistency preservation:
The DBMS has to ensure that the transaction preserves data consistency in a
database and does not violate any integrity constraints .
A database program should be written in a way that guarantees that, if the
database is in a consistent state before executing the transaction,
it will be in a consistent state after the complete execution of the transaction,
assuming that no interference with other transactions occurs
45. Consider an account hoder tries to withdraw an amount from his Account where
withdraw_amt is greater than the balance,
then the transaction should not cause a change in the database as the action will
violate a constraint in the database .
So the DBMS programmers must ensure that such a constraint should exist which
will not allow the database to go in an inconsistent state.
A Bank employee tries to open an Account with the same Account_no which
already has been assigned to another Account holder,
in this case the transaction should ensure that the Account_no should not be
duplicated and hence maintain database integrity.
46.
47. Isolation:
A transaction should appear as though it is being executed in isolation from other
transactions.
That is, the execution of a transaction should not be interfered with by any other
transactions executing concurrently.
48.
49. Durability or permanency
The changes applied to the database by a committed transaction must be
permanent in the database.
These changes must not be lost because of any failure.
For example ,Consider the Account_holder is transferring funds from Account A to
Account B then under the following conditions,
the changes should be permanent and cannot be reversed
even if the application crashes
or an error occurs
or the user decides to cancel the transaction after the transaction is over
50. Some Transaction Primitives
Examples of primitives for transactions.
Primitive Description
BEGIN_TRANSACTION Make the start of a transaction
END_TRANSACTION Terminate the transaction and try to commit
ABORT_TRANSACTION Kill the transaction and restore the old values
READ Read data from a file, a table, etc.
WRITE Write data to a file, a table, etc.
51. Base Model:
The rise in popularity of NoSQL databases provided a flexible and fluidity with
ease to manipulate data and as a result,
a new database model was designed, reflecting these properties.
The acronym BASE is slightly more confusing than ACID but however, the words
behind it suggest ways in which the BASE model is different
52. BASE
1. Basically Available:
2. Instead of making it compulsory for immediate consistency,
3. BASE-modelled NoSQL databases will ensure the availability of data by spreading
and replicating it across the nodes of the database cluster.
4. Soft State:
5. Due to the lack of immediate consistency, the data values may change over time.
6. Eventually Consistent:
7. The fact that BASE does not obligates immediate consistency but it does not
mean that it never achieves it.
8. However, until it does, the data reads are still possible (even though they might
not reflect reality).
53. S. No Criteria ACID BASE
1. Simplicity Simple Complex
2. Focus Commits Best attempt
3. Maintenance High Low
4. Consistency Of Data Strong Weak/Loose
5. Concurrency scheme Nested Transactions Close to answer
6. Scaling Vertical Horizontal
7. Implementation Easy to implement Difficult to implement
8. Upgrade Harder to upgrade Easy to upgrade
9. Type of database Robust Simple
10. Type of code Simple Harder
11. Time required for completion Less time More time.
12. Examples Oracle, MySQL, SQL Server, etc.
DynamoDB, Cassandra, CouchDB,
SimpleDB etc.
ACID vs BASE
54. CAP Theorem
What is the CAP theorem?
• The CAP Theorem is comprised of three components
(hence its name) as they relate to distributed data stores:
• Consistency. All reads receive the most recent write or an
error.
• Availability. All reads contain data, but it might not be the
most recent.
• Partition Tolerance. The system continues to operate
despite network failures (i.e.; dropped partitions, slow
network connections, or unavailable network connections
between nodes.)
55. NoSQL Database
• NoSQL databases do not require a schema, and don’t enforce relations between tables.
• All its documents are JSON documents, which are complete entities one can readily read and understand.
• They are widely recognized for:
• Ease-of-use
• Scalable performance
• Strong resilience
• Wide availability
Examples of NoSQL databases include:
• Cloud Firestore
• Firebase Real-time DB
• MongoDB
• MarkLogic
• Couchbase
• CloudDB
• Amazon DynamoDB