This ppt explain about choosing your NoSQL database. This also contains factors which needs to be consider while choosing NoSQL database. Thanks Arun Chandrasekaran(https://www.linkedin.com/profile/view?id=AAMAAAQKxWsB9tkk7s2ll2T2BvLvR9QDv_OdJXs&trk=hp-identity-name) for helping me.
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
A Comparison between Relational Databases and NoSQL Databasesijtsrd
Databases are used for storing and managing large amounts of data. Relational model is useful when it comes to reliability but when it comes to the modern applications dealing with large amounts of data and the data is unstructured; non-relational models are usable. NoSQL databases are used to store large amounts of data. NoSQL databases are non-relational, distributed, open source and are horizontally scalable. This paper provides the comparison of the relational model with NoSQL Behjat U Nisa"A Comparison between Relational Databases and NoSQL Databases" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11214.pdf http://www.ijtsrd.com/computer-science/database/11214/a-comparison-between-relational-databases-and-nosql-databases/behjat-u-nisa
This ppt explain about choosing your NoSQL database. This also contains factors which needs to be consider while choosing NoSQL database. Thanks Arun Chandrasekaran(https://www.linkedin.com/profile/view?id=AAMAAAQKxWsB9tkk7s2ll2T2BvLvR9QDv_OdJXs&trk=hp-identity-name) for helping me.
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
A Comparison between Relational Databases and NoSQL Databasesijtsrd
Databases are used for storing and managing large amounts of data. Relational model is useful when it comes to reliability but when it comes to the modern applications dealing with large amounts of data and the data is unstructured; non-relational models are usable. NoSQL databases are used to store large amounts of data. NoSQL databases are non-relational, distributed, open source and are horizontally scalable. This paper provides the comparison of the relational model with NoSQL Behjat U Nisa"A Comparison between Relational Databases and NoSQL Databases" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11214.pdf http://www.ijtsrd.com/computer-science/database/11214/a-comparison-between-relational-databases-and-nosql-databases/behjat-u-nisa
Secure Transaction Model for NoSQL Database Systems: Reviewrahulmonikasharma
NoSQL cloud database frameworks would consist new sorts of databases that would construct over many cloud hubs and would be skilled about storing and transforming enormous information. NoSQL frameworks need to be progressively utilized within substantial scale provisions that require helter skelter accessibility. What’s more effectiveness for weaker consistency? Consequently, such frameworks need help for standard transactions which give acceptable and stronger consistency. This task proposes another multi-key transactional model which gives NoSQL frameworks standard for transaction backing and stronger level from claiming information consistency. Those methodology is to supplement present NoSQL structural engineering with an additional layer that manages transactions. The recommended model may be configurable the place consistency, accessibility Furthermore effectiveness might make balanced In view of requisition prerequisites. The recommended model may be approved through a model framework utilizing MongoDB. Preliminary examinations show that it ensures stronger consistency Furthermore supports great execution.
The webinar was conducted by Bhuvan Gandhi and Vishwas Ganatra on 22-23 August, 2020. It was powered by Encode - The coding club of PDPU.
Bhuvan Gandhi - https://github.com/bmg02/database-workshop-encode
Vishwas Ganatra - https://github.com/vishwasganatra/Encode-database-workshop
SURVEY ON IMPLEMANTATION OF COLUMN ORIENTED NOSQL DATA STORES ( BIGTABLE & CA...IJCERT JOURNAL
NOSQL is a database provides a mechanism for storage and retrieval of data that is modeled for huge amount of data which is used in big data and Cloud Computing . NOSQL systems are also called "Not only SQL" to emphasize that they may support SQL-like query languages. A basic classification of NOSQL is based on data model; they are like column, Document, Key-Value etc. The objective of this paper is to study and compare the implantation of various column oriented data stores like Bigtable, Cassandra.
Comparative study of no sql document, column store databases and evaluation o...ijdms
In the last decade, rapid growth in mobile applications, web technologies, social media generating
unstructured data has led to the advent of various nosql data stores. Demands of web scale are in
increasing trend everyday and nosql databases are evolving to meet up with stern big data requirements.
The purpose of this paper is to explore nosql technologies and present a comparative study of document
and column store nosql databases such as cassandra, MongoDB and Hbase in various attributes of
relational and distributed database system principles. Detailed study and analysis of architecture and
internal working cassandra, Mongo DB and HBase is done theoretically and core concepts are depicted.
This paper also presents evaluation of cassandra for an industry specific use case and results are
published.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
Secure Transaction Model for NoSQL Database Systems: Reviewrahulmonikasharma
NoSQL cloud database frameworks would consist new sorts of databases that would construct over many cloud hubs and would be skilled about storing and transforming enormous information. NoSQL frameworks need to be progressively utilized within substantial scale provisions that require helter skelter accessibility. What’s more effectiveness for weaker consistency? Consequently, such frameworks need help for standard transactions which give acceptable and stronger consistency. This task proposes another multi-key transactional model which gives NoSQL frameworks standard for transaction backing and stronger level from claiming information consistency. Those methodology is to supplement present NoSQL structural engineering with an additional layer that manages transactions. The recommended model may be configurable the place consistency, accessibility Furthermore effectiveness might make balanced In view of requisition prerequisites. The recommended model may be approved through a model framework utilizing MongoDB. Preliminary examinations show that it ensures stronger consistency Furthermore supports great execution.
The webinar was conducted by Bhuvan Gandhi and Vishwas Ganatra on 22-23 August, 2020. It was powered by Encode - The coding club of PDPU.
Bhuvan Gandhi - https://github.com/bmg02/database-workshop-encode
Vishwas Ganatra - https://github.com/vishwasganatra/Encode-database-workshop
SURVEY ON IMPLEMANTATION OF COLUMN ORIENTED NOSQL DATA STORES ( BIGTABLE & CA...IJCERT JOURNAL
NOSQL is a database provides a mechanism for storage and retrieval of data that is modeled for huge amount of data which is used in big data and Cloud Computing . NOSQL systems are also called "Not only SQL" to emphasize that they may support SQL-like query languages. A basic classification of NOSQL is based on data model; they are like column, Document, Key-Value etc. The objective of this paper is to study and compare the implantation of various column oriented data stores like Bigtable, Cassandra.
Comparative study of no sql document, column store databases and evaluation o...ijdms
In the last decade, rapid growth in mobile applications, web technologies, social media generating
unstructured data has led to the advent of various nosql data stores. Demands of web scale are in
increasing trend everyday and nosql databases are evolving to meet up with stern big data requirements.
The purpose of this paper is to explore nosql technologies and present a comparative study of document
and column store nosql databases such as cassandra, MongoDB and Hbase in various attributes of
relational and distributed database system principles. Detailed study and analysis of architecture and
internal working cassandra, Mongo DB and HBase is done theoretically and core concepts are depicted.
This paper also presents evaluation of cassandra for an industry specific use case and results are
published.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
What is NoSQL? How does it come to the picture? What are the types of NoSQL? Some basics of different NoSQL types? Differences between RDBMS and NoSQL. Pros and Cons of NoSQL.
What is MongoDB? What are the features of MongoDB? Nexus architecture of MongoDB. Data model and query model of MongoDB? Various MongoDB data management techniques. Indexing in MongoDB. A working example using MongoDB Java driver on Mac OSX.
The Biggest Cyber and Physical Security Threats to Critical Infrastructure FM...Fas (Feisal) Mosleh
The Biggest Cyber and Physical Security Threats to Critical Infrastructure by Fas Mosleh, ex-HP, ex-IBM, ex-Broadcom. Discusses how critical infrastructure can be compromised by physical and security threats. Critical infrastructure refers to the systems, facilities, and networks that are essential to the functioning of a society and its economy. These are the assets that, if damaged or disrupted, could have a significant impact on public health and safety, economic security, and national security. Social engineering: This involves manipulating people into divulging sensitive information or taking actions that compromise security. Phishing is a primary example of such manipulation and is still one of the most prevalent types of attack. According to the 2021 Data Breach Investigations Report by Verizon, phishing was involved in 36% of all data breaches, making it the top threat action in the report. Phishing attacks are also becoming increasingly sophisticated and targeted, with attackers using social engineering tactics to trick victims into divulging sensitive information or downloading malware. This can include impersonating trusted individuals or organizations, creating convincing fake websites or emails, and using urgent or threatening language to pressure victims into taking action.
According to the 2021 State of the Phish Report by Proofpoint, 75% of organizations surveyed reported being targeted by phishing attacks in 2020, and 59% of those attacks were successful in compromising at least one user account or system. The report also found that COVID-19 related phishing attacks were particularly prevalent in 2020, taking advantage of the pandemic to trick victims into providing personal information or downloading malware.
5. Distributed denial of service (DDoS) attacks: These attacks flood a system with traffic, overwhelming it and causing it to crash or become unavailable.
6. Advanced persistent threats (APTs): APTs are sophisticated, long-term attacks that target specific organizations and can involve multiple stages of infiltration and exfiltration.
According to the 2023 CrowdStrike Global Threat Report, An uptick in social engineering tactics targeting human interactions – Tactics such as vishing direct victims to download malware and SIM swapping to circumvent multi-factor authentication (MFA).
WHITE PAPER - The Importance of CIP in the Energy Sector v2.0.pdfFas (Feisal) Mosleh
NERC CIP outline for energy utilities. The growing energy sector must understand how to improve its critical infrastructure protection as outlined by the NERC CIP standards in North America.
https://youtu.be/EbFj7I_K37Q
Juldee IP and tech monetization v4 by ex-Hewlett-Packard Director of IP Fas M...Fas (Feisal) Mosleh
Ever wondered how to best leverage your innovation – technology, patents and IP – to create additional value for your venture? You can increase your business valuation, create a revenue stream or generate a small windfall for your business by monetizing technology and patents along the way. IP and technology M&A can help create value whether you are early stage, mid stage or later stage.
Syndicated Patent Deals = Supercharging the buying and selling of patents by ...Fas (Feisal) Mosleh
The syndicated buying of patents to achieve strategic business goals. By Feisal Mosleh, patent and IP strategist, ex HP Director, Patent sales, IP group. This article lays out the framework that many companies have used and are using to buy IP assets in an aggregated manner to maximize their benefits.... Some of the world’s largest corporations joined forces to acquire patent portfolios in the high-profile Nortel and Novell deals. Consortium buying also has advantages for small and mediumsized entities looking to purchase or sell patents...
Introduction to IP and technology licensing for technology executives by Fas ...Fas (Feisal) Mosleh
Introduction to Concepts in IP and Technology Licensing. This presentation sets out the basics of technology and IP licensing for CTOs, Engineering, Technology execs and CEOs to understand how to strategically leverage their intellectual property for the benefit if their business. The basics of technology and IP licensing also point the way to IP monetization.
Innovation & disruption hp talk april 2010 juldee versionFas (Feisal) Mosleh
A presentation on Innovation. Given to Hewlett-Packard headquarter and labs employees. Contains many examples of innovation undertaken by technology companies including Netflix, Amazon, EBay, Toyota, Blackberry, YouTube, Daimler Benz, Google, Lexus, Audi, Nokia, etc. Go to www.Juldee.com for more info.
As part of the Kanzatec Creative Venturing Series, we held Part 3 - Creative Funding in Silicon Valley
Did you know that there are many creative ways to fund your company that don't need Venture
Capital or Angel Investments. Join us to learn what other sources are available to you and how to
take advantages of these resources, such as government loans, leveraging your intellectual property and crowdsourcing.
Creative exits v3 10 20-2013 for distribution Fas Mosleh at OPEN Networking E...Fas (Feisal) Mosleh
We explore the many ways that you can get some kind of valuable payout before you move on to your next project. We will discuss several interesting ways of ensuring that you and your investors can walk away with something in your pockets. When is the best time to plan out an exit strategy? We examine exits contemplated in the early stages through the later stages.
Have you ever wondered what it is you need to do to increase the valuation of your company to get the best payout when you exit? This panel will discuss many ways of upping your valuation and how to start the process in the early days as well as improving it in the latter days of the life of the company.
For example, adding high profile industry experienced people to your team, buying or developing really interesting patents, creating channels, a brand, relationships that matter. Other ways include increasing your customer base and creating products that fit the gaps which larger competitors possess. When an acquiring company looks at you, what constitutes your value drivers?
Join a distinguished panel comprising entrepreneurs and acquirers as well as financial experts who can give you an idea of how to best increase your company’s valuation.
Why acquire patents in high tech?
Develop an IP strategy that encompasses a patent acquisition strategy. An effective patent strategy needs to consider the options of monetization, including licensing and sale as well as a cogent make and buy approach.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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.
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).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
2. A Brief Intro to NoSQL Page 2
Contents
Introduction:.................................................................................................................................................3
What is NoSQL?.........................................................................................................................................3
What about SQL? ......................................................................................................................................4
A review of RDBMS...................................................................................................................................4
Examples of relational databases: ............................................................................................................5
Simplicity is a driver of SQL vs. NoSQL......................................................................................................5
Different types of NoSQL Databases.........................................................................................................6
Document..............................................................................................................................................6
Key-value...............................................................................................................................................6
Wide-column or columnar....................................................................................................................6
Graph ....................................................................................................................................................7
Object-oriented.....................................................................................................................................7
The users of NoSQL...................................................................................................................................7
Who Uses NoSQL Databases?...............................................................................................................7
The Advantages of NoSQL Databases.......................................................................................................8
Being “Agile” and the need for Flexibility and Speed...............................................................................8
Performance and cost reduction was a driver of NoSQL..........................................................................8
Can they store relational information well?.........................................................................................8
Transactional Use Cases vs. Analytical Use Cases...................................................................................11
Source:Stichdata .....................................................................................................................................11
Analytical Needs..................................................................................................................................12
The Top NoSQL Databases......................................................................................................................12
Enterprise requirements of a data warehouse .......................................................................................13
The emerging Transactional NoSQL movement .....................................................................................13
3. A Brief Intro to NoSQL Page 3
Introduction:
What is NoSQL?
NoSQL refers to any database that is not using SQL or the commonly found relational model
that arranges data discretely into tables comprising columns and rows.
Common examples of NoSQL databases are the key-value store, document databases, column-
oriented databases, and graph databases.
Typical properties of NoSQL DB’s:
NoSQL is commonly associated with more flexible deployment and structure as well as
faster read and write performance.
NoSQL databases are rarely ACID1
compliant, and may or may not offer query languages
to pull and manipulate data. NoSQL is increasingly used to support big data level
analytics.
NoSQL databases allow developers to store huge amounts of unstructured data, giving
them a lot of flexibility.
They are also used for scaling to very large data sets.
1
Atomicity Consistency Isolation Durability - The presence of these four can ensure that a database transaction is completed in a timely
manner. When databases possess these components, they are said to be ACID-compliant.
4. A Brief Intro to NoSQL Page 4
What about SQL?
Almost all relational databases use a form of SQL as their query language, and most of them
adhere to the ACID set of properties to ensure reliable transactions: atomicity, consistency,
isolation, and durability2
.
Relational databases store and manage data in a traditional table format, with each piece of data
organized into a row and a column.
Columns hold the data of a single type or field, like first name, order number, or the image link
of a product logo. Rows create the relationship between these data points.
For example, rows can associate a first name to a last name and then to a user name, email
address, and customer ID. Businesses use relational databases to maintain the data from their
applications and ensure they always have access to critical customer information, product data,
social data, and financial particulars like purchases, fulfillment, revenue, and expenses.
A review of RDBMS
Relational databases are also called relational database management systems (RDBMS) or
structured query language (SQL) databases. An RDBMS is based on SQL that allows users to
update, query, and administer a relational database.
SQL is typically the standard programming language used to access a relational database.
Relational databases software can read SQL and use SQL syntax. SQL’s syntax is very simple,
and as such, it is one of the simplest programming languages in the industry and is used to easily
access and query relational databases; users can search for a range of interconnected data with
ease.
Relational databases software facilitates the creation, maintenance, and usage of these tables.
RDBMS solutions store large volumes of data and allow access to structured data sets efficiently
and flexibly.
2
This set of properties has been the defining feature of relational databases which, simply put, ensures that all
transactions are accurate, up-to-date, and reliable.
5. A Brief Intro to NoSQL Page 5
Examples of relational databases:
Microsoft SQL
Oracle Database
IBM DB2
MySQL (Open source )
Amazon RDS (Relational Database Service)
Amazon Aurora (MySQL and PostgreSQL-compatible open source DB).
PostgreSQL (Open source object-relational DB)
SAP HANA (Converges transactions and analytics on one in-memory platform, running on
premise or in the cloud)
IBM Informix
MariaDB (Open source DB with full ACID)
SQLite (Self-contained, serverless, zero-configuration, transactional SQL database engine)
Teradata Vantage
Azure SQL (Relational database-as-a service using the Microsoft SQL Server Engine.)
Oracle TimesTen (Runs in the application tier and stores all data in main memory.)
Simplicity is a driver of SQL vs. NoSQL
Relational DB systems can range from desktop applications that create a small database on your
laptop or phone to large enterprise-grade data stores running on your premises or in the cloud.
Relational databases are usually chosen due to their simplicity in comparison to NoSQL
databases, such as object-oriented databases, document databases, and graph databases.
6. A Brief Intro to NoSQL Page 6
Different types of NoSQL Databases
NoSQL comprises multiple types of databases, each designed for a different use case or data
type. The main types are document, key-value, wide-column, object-oriented and graph.
For example a document database stores long-form web content (web pages, documents). They
provide flexible schemas and scale easily with large amounts of data and high user loads.
Document
Document databases store related data together in documents, a semi-structured schema that
maintains a level of reportability by keeping associated metadata within the data itself.
Document databases house data together that are relevant to each other, and don’t require a
standard schema across documents. Additionally, these documents can reference other
documents, giving the document an element of structured depth. Document databases are useful
for data that are strongly related but non-standard across tuples3
.
Key-value
If all you need is to render a value that can be easily found by its key, then a key-value store is
the quickest and most scalable approach. The drawback is a much more limited querying ability,
so it doesn’t work well for analytic data. That said, rendering a user’s email address based on the
username or caching web data is a simple and fast solution in a key-value store.
Key-value stores save data as discrete couplets of name and value associated together with a key.
No key necessarily needs the same structure, so data is simply accumulated instead of sorted into
tables.
Wide-column or columnar
Column-oriented databases are key-value stores that impose more structure on their data. Key-
value pairs (or columns) are associated together into families and tables. Unlike a relational
database, the data within the tables and families are not consistent but the overlying structure
allows greater potential for associating data together in hierarchies.
3
A tuple is one of 4 built-in data types in Python used to store collections of data, the other 3 are List, Set, and Dictionary, all with different
qualities and usage. Tuples are used to store multiple items in a single variable. A tuple is a collection which is ordered and unchangeable.
7. A Brief Intro to NoSQL Page 7
Graph
The first challenge for selecting a database is finding the best structure for the data you’ll be
storing. Sometimes there is a natural fit—for example, airline flight information fits very well in
a graph database as this mimics real-life patterns—while long-form web content can usually slot
into document databases easily (hence the name).
Graph databases utilize topographical schemas to map data as if it were a physical structure of
nodes and edges. Usually a node represents a particular record with associated data, and edges
represent relationships between nodes (along with whatever data particular to the relationship).
When much of your data consists of relationships between data points, graph databases are a
good choice. Graph databases break data down into nodes and relationships, storing properties
on each. Because any node can have unlimited relationships with other nodes with a trivial effect
on performance, these are optimal for relationship-oriented data such as social networks.
Object-oriented
Object-oriented databases help organize data models and are typically used when needing to
structure large, complex data sets. These tools utilize query languages to retrieve information and
create tables to be set with information.
The users of NoSQL
Who Uses NoSQL Databases?
Data scientists – Relational databases are the more traditional storage option, where all data is
filed in rows and columns. With the ever growing complexity of data, many data scientists now
prefer NoSQL databases, which allow for greater flexibility because they do not force the user to
the row-and-column format.
Those that need to collect extra large data sets in real time should look into big data processing
and distribution systems. These tools are built to scale for businesses that are constantly
collecting enormous amounts of data. Pulling data sets may be more challenging with big data
processing and distribution systems, but the insights received may be more valuable due to the
granularity of the data.
Database administrators – Non-relational, or NoSQL, databases have recently grown in
popularity because they are easier to implement, have greater flexibility, and tend to have faster
data retrieval times. They are cheaper and easier to scale, but don’t have the same levels of
standardization and reporting tools.
8. A Brief Intro to NoSQL Page 8
Non-native databases are the most common, but allow users outside the company to insert and
retrieve data. Some people believe this enhances data by providing increased, more human
knowledge. These tools typically serve niche purposes for specific applications.
The Advantages of NoSQL Databases
Create a flexible and dynamic data model to store and access data rapidly and flexibly
Handle large volumes of data at high speed with a scale-out architecture.
Scale database operations without overhauling data schema or strategy and lower
performance; Enable easy updates to schemas and fields.
Store unstructured, semi-structured, or structured data.
Optimize IT infrastructure resources by the more efficient use of storage resources
Achieve big data levels of information storage particularly for non-structured data
Support business applications with higher availability
Be more developer-friendly
Take advantage of the cloud to deliver zero downtime
Being “Agile” and the need for Flexibility and Speed
In the 2000’s, with the ascent of the Agile methodology, programmers recognized the need to
rapidly adapt to changing requirements. They needed the ability to iterate quickly and make
changes throughout the software stack, from presentation and logic all the way to the database
model. NoSQL databases gave them this flexibility.
Performance and cost reduction was a driver of NoSQL
The name "NoSQL" was coined in the early 21st century, though NoSQL databases were around
even in the 1960’s. The growing needs of Web 2.0 companies included the need to handle sub
second response times from huge numbers of users and improving developer productivity to
reduce development costs; this drove the development and usage of NoSQL DBs.
In the mid 2000s, the cost of storage started decreasing dramatically and the need to reduce data
duplication, to keep storage costs down, by using a complex, difficult-to-manage data model
simply became less necessary. NoSQL databases emerged to control the rising costs of
developers and tend to the ever increasing need to handle vast amounts of users simultaneously
accessing data and expecting sub second response times. SQL alone, could not always cope with
the crushing scale of 10’s of millions of demanding users, as applications became more global.
A NoSQL database simply provides a mechanism for storage and retrieval of data that is modeled in a
manner different from the simple tabular relations used in relational databases.
Can they store relational information well?
9. A Brief Intro to NoSQL Page 9
Many believe that NoSQL databases or non-relational databases don’t store relationship data well. But
this is not correct, because NoSQL databases just store relationship data differently than relational
databases do. When compared with SQL databases, many find modeling relationship data in NoSQL
databases to be easier than in SQL 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.
10. A Brief Intro to NoSQL Page 10
Source: Guru99
“Different databases are designed to solve different problems. Using a single database engine for all
of the requirements usually leads to non- performant solutions; storing transactional data, caching
session information, traversing graph of customers and the products their friends bought are
essentially different problems.”
― Pramod J. Sadalage, NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence
11. A Brief Intro to NoSQL Page 11
Source: Apptunix
Transactional Use Cases vs. Analytical Use Cases
Source:Stichdata
12. A Brief Intro to NoSQL Page 12
Analytical Needs
Data warehouse technology has advanced significantly in the past few years. An entire category called
analytic databases has arisen to specifically address the needs of organizations who want to build very
high-performance data warehouses. Analytic databases are purpose-built to analyze extremely large
volumes of data very quickly and often perform 100-1,000 times faster than transactional databases in
these tasks.
The Top NoSQL Databases
Source: MongoDB
13. A Brief Intro to NoSQL Page 13
Enterprise requirements of a data warehouse
1. Performance. The data warehouse needs to able to ingest data and analyze enormous
quantities of data extremely quickly.
2. Scalability. As you grow, more data will be piped in and more users will need to run
analyses. The data warehouse must be able to keep pace with your growth.
3. Compatibility. SQL is the most widely-used query interface with a massive ecosystem
of both users and tools. SQL compatibility should be considered a top priority for any
data warehouse technology.
4. Analytic functionality. Analysts often need to perform more complicated calculations
than are supported in traditional SQL syntax, including regressions and predictive
analytics.
The emerging Transactional NoSQL movement
The NoSQL database revolution started with the publication of the Google BigTable and
Amazon Dynamo papers in 2006 and 2007. These original designs focused on horizontal write
scalability producing better performance than SQL databases. However, they compromised the
ACID properties, lacking consistency and durability. Therefore, NoSQL became synonymous
with “Non-Relational” and “Non-Transactional”.
Failures in consistency could lead to a value that was either never committed in the database or
was completely out of order. Given these fundamental limitations, developers continued to use
monolithic SQL databases for business-critical workloads and NoSQL was relegated to less
business-critical workloads.
However, big changes happened in the NoSQL world from 2018 to 2020. For instance, multiple
old and new NoSQL databases alike, embraced one or more flavors of ACID transactions.
Examples are:
Amazon DynamoDB
https://aws.amazon.com/about-aws/whats-new/2018/11/announcing-amazon-dynamodb-support-for-
transactions/
Microsoft Azure Cosmos DB
(Azure Cosmos DB supports full ACID compliant transactions with snapshot isolation for operations
within the same logical partition key)
https://devblogs.microsoft.com/cosmosdb/introducing-transactionalbatch-in-the-net-sdk/
MongoDB (MongoDB is ACID-compilant at the document level.)