The document discusses NoSQL databases as an alternative to traditional SQL databases. It provides an overview of NoSQL databases, including their key features, data models, and popular examples like MongoDB and Cassandra. Some key points:
- NoSQL databases were developed to overcome limitations of SQL databases in handling large, unstructured datasets and high volumes of read/write operations.
- NoSQL databases come in various data models like key-value, column-oriented, and document-oriented. Popular examples discussed are MongoDB and Cassandra.
- MongoDB is a document database that stores data as JSON-like documents. It supports flexible querying. Cassandra is a column-oriented database developed by Facebook that is highly scalable
This document discusses NoSQL databases and compares MongoDB and Cassandra. It begins with an introduction to NoSQL databases and why they were created. It then describes the key features and data models of NoSQL databases including key-value, column-oriented, document, and graph databases. Specific details are provided about MongoDB and Cassandra, including their data structure, query operations, examples of usage, and enhancements. The document provides an in-depth overview of NoSQL databases and a side-by-side comparison of MongoDB and Cassandra.
This document provides an introduction to NoSQL databases. It discusses that NoSQL databases are non-relational, do not require a fixed table schema, and do not require SQL for data manipulation. It also covers characteristics of NoSQL such as not using SQL for queries, partitioning data across machines so JOINs cannot be used, and following the CAP theorem. Common classifications of NoSQL databases are also summarized such as key-value stores, document stores, and graph databases. Popular NoSQL products including Dynamo, BigTable, MongoDB, and Cassandra are also briefly mentioned.
This document discusses data migration in schemaless NoSQL databases. It begins by defining NoSQL databases and comparing them to traditional relational databases. It then covers aggregate data models and the concepts of schemalessness and implicit schemas in NoSQL databases. The main focus is on data migration when an implicit schema changes, including principles, strategies, and test options for ensuring data matches the new implicit schema in applications.
A Comparative Study of NoSQL and Relational Database.pdfJennifer Roman
The document compares relational databases and NoSQL databases. It discusses their key features such as scalability, cost, data volume handling, availability, and performance. Relational databases are better for consistency but struggle with scalability, availability and handling large volumes of data. NoSQL databases are better suited for modern web and big data applications as they offer better performance, scalability and can handle large volumes of data, though they lack standardization and have weaker security. The choice of database depends on the nature and requirements of the application. Both database models have strengths and weaknesses and will continue to co-exist to support different application needs.
The document discusses NoSQL databases as an alternative to SQL databases that is better suited for large volumes of data where performance is critical. It explains that NoSQL databases sacrifice consistency for availability and partition tolerance. Some common types of NoSQL databases are document stores, key-value stores, column stores, and graph databases. NoSQL databases can scale out easily across multiple servers and provide features like automatic sharding and replication that help with distributing data and workload. However, NoSQL databases still lack maturity, support, and administration tools compared to SQL databases.
This document provides an overview of a syllabus for a course on NoSQL databases. It discusses the evolution and fundamentals of NoSQL, various data distribution models, and explores different NoSQL data models like key-value, document, and graph databases. It also covers topics like MapReduce, CAP theorem, and different types of NoSQL databases compared to relational databases.
NoSQL databases allow for a variety of data models like key-value, document, columnar and graph formats. NoSQL stands for "not only SQL" and provides an alternative to relational databases. It is useful for large distributed datasets and prioritizes performance and scalability over rigid data consistency. Common NoSQL databases include key-value stores like Redis and Riak, document databases like MongoDB and CouchDB, wide-column stores like Cassandra and HBase, and graph databases like Neo4j and Titan.
This document discusses NoSQL databases and compares MongoDB and Cassandra. It begins with an introduction to NoSQL databases and why they were created. It then describes the key features and data models of NoSQL databases including key-value, column-oriented, document, and graph databases. Specific details are provided about MongoDB and Cassandra, including their data structure, query operations, examples of usage, and enhancements. The document provides an in-depth overview of NoSQL databases and a side-by-side comparison of MongoDB and Cassandra.
This document provides an introduction to NoSQL databases. It discusses that NoSQL databases are non-relational, do not require a fixed table schema, and do not require SQL for data manipulation. It also covers characteristics of NoSQL such as not using SQL for queries, partitioning data across machines so JOINs cannot be used, and following the CAP theorem. Common classifications of NoSQL databases are also summarized such as key-value stores, document stores, and graph databases. Popular NoSQL products including Dynamo, BigTable, MongoDB, and Cassandra are also briefly mentioned.
This document discusses data migration in schemaless NoSQL databases. It begins by defining NoSQL databases and comparing them to traditional relational databases. It then covers aggregate data models and the concepts of schemalessness and implicit schemas in NoSQL databases. The main focus is on data migration when an implicit schema changes, including principles, strategies, and test options for ensuring data matches the new implicit schema in applications.
A Comparative Study of NoSQL and Relational Database.pdfJennifer Roman
The document compares relational databases and NoSQL databases. It discusses their key features such as scalability, cost, data volume handling, availability, and performance. Relational databases are better for consistency but struggle with scalability, availability and handling large volumes of data. NoSQL databases are better suited for modern web and big data applications as they offer better performance, scalability and can handle large volumes of data, though they lack standardization and have weaker security. The choice of database depends on the nature and requirements of the application. Both database models have strengths and weaknesses and will continue to co-exist to support different application needs.
The document discusses NoSQL databases as an alternative to SQL databases that is better suited for large volumes of data where performance is critical. It explains that NoSQL databases sacrifice consistency for availability and partition tolerance. Some common types of NoSQL databases are document stores, key-value stores, column stores, and graph databases. NoSQL databases can scale out easily across multiple servers and provide features like automatic sharding and replication that help with distributing data and workload. However, NoSQL databases still lack maturity, support, and administration tools compared to SQL databases.
This document provides an overview of a syllabus for a course on NoSQL databases. It discusses the evolution and fundamentals of NoSQL, various data distribution models, and explores different NoSQL data models like key-value, document, and graph databases. It also covers topics like MapReduce, CAP theorem, and different types of NoSQL databases compared to relational databases.
NoSQL databases allow for a variety of data models like key-value, document, columnar and graph formats. NoSQL stands for "not only SQL" and provides an alternative to relational databases. It is useful for large distributed datasets and prioritizes performance and scalability over rigid data consistency. Common NoSQL databases include key-value stores like Redis and Riak, document databases like MongoDB and CouchDB, wide-column stores like Cassandra and HBase, and graph databases like Neo4j and Titan.
Challenges Management and Opportunities of Cloud DBAinventy
Research Inventy provides an outlet for research findings and reviews in areas of Engineering, Computer Science found to be relevant for national and international development, Research Inventy is an open access, peer reviewed international journal with a primary objective to provide research and applications related to Engineering. In its publications, to stimulate new research ideas and foster practical application from the research findings. The journal publishes original research of such high quality as to attract contributions from the relevant local and international communities.
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.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
This document summarizes a research paper on graph storage databases in NoSQL. It discusses big data and the need for alternative databases to handle large, diverse datasets. It defines the key aspects of big data including volume, velocity, variety and complexity. It also describes different types of NoSQL databases, focusing on the basic structure of graph databases. Graph databases use nodes and relationships to model connected data. The document compares several graph database systems and discusses advantages like performance and flexibility as well as disadvantages like complexity. It outlines several applications of graph databases in areas like social networks and logistics.
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.
The rising interest in NoSQL technology over the last few years resulted in an increasing number of evaluations and comparisons among competing NoSQL technologies From survey we create a concise and up-to-date comparison of NoSQL engines, identifying their most beneficial use from the software engineer point of view.
MongoDB is a document-oriented NoSQL database that uses JSON-like documents with optional schemas. It provides high performance, high availability, and easy scalability. MongoDB is also called "humongous" because it is designed to store and handle large volumes of data. Some key advantages of MongoDB include its ability to handle large, unstructured data sets and provide agile development with quick code iterations.
Databases are organized collections of data that allow for efficient data access and management. There are different types of databases including relational databases, NoSQL databases, object-oriented databases, and graph databases. Databases have evolved over time from flat file systems to hierarchical, network, relational, and modern cloud-based systems. A database management system provides tools for creating, accessing, and managing databases and ensures security, integrity, and consistency of stored data.
Analysis and evaluation of riak kv cluster environment using basho benchStevenChike
This document analyzes and evaluates the performance of the Riak KV NoSQL database cluster using the Basho-bench benchmark tool. Experiments were conducted on a 5-node Riak KV cluster to test throughput and latency under different workloads, data sizes, and operations (read, write, update). The results found that Riak KV can handle large volumes of data and various workloads effectively with good throughput, though latency increased with larger data sizes. Overall, Riak KV is suitable for distributed big data environments where high availability, scalability and fault tolerance are important.
A database is an organized collection of data stored and accessed electronically. A database management system (DBMS) is software that allows users to define, create, maintain and control access to the database. Well-known DBMSs include MySQL, Oracle, SQL Server and IBM DB2. A DBMS manages storage, security, querying and integrity of the data in the database. The most popular database model since the 1980s has been the relational model which represents data in tables related through keys.
This document discusses NoSQL databases and compares them to relational databases. It begins by explaining that NoSQL databases were developed to address scalability issues in relational databases. The document then categorizes NoSQL databases into four main types: key-value stores, column-oriented databases, document stores, and graph databases. For each type, popular examples are provided (e.g. DynamoDB, Cassandra, MongoDB) along with descriptions and use cases. The advantages of NoSQL databases over relational databases are also briefly touched on.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
NOSQL in big data is the not only structure langua.pdfajajkhan16
This presentation discusses the limitations of relational database management systems (RDBMS) in handling large datasets and introduces NoSQL databases as an alternative. It begins by defining RDBMS and describing issues with scaling RDBMS to big data through techniques like master-slave architecture and sharding. It then defines NoSQL databases, explaining why they emerged and classifying them into key-value, columnar, document, and graph models. The presentation concludes that both RDBMS and NoSQL databases have advantages, suggesting a polyglot approach is optimal to handle different data storage needs.
The document discusses the rise of NoSQL databases. It notes that NoSQL databases are designed to run on clusters of commodity hardware, making them better suited than relational databases for large-scale data and web-scale applications. The document also discusses some of the limitations of relational databases, including the impedance mismatch between relational and in-memory data structures and their inability to easily scale across clusters. This has led many large websites and organizations handling big data to adopt NoSQL databases that are more performant and scalable.
HYBRID DATABASE SYSTEM FOR BIG DATA STORAGE AND MANAGEMENTIJCSEA Journal
Relational database systems have been the standard storage system over the last forty years. Recently,
advancements in technologies have led to an exponential increase in data volume, velocity and variety
beyond what relational databases can handle. Developers are turning to NoSQL which is a non- relational
database for data storage and management. Some core features of database system such as ACID have
been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and
management of extremely voluminous data of diverse components known as big data, such that the two
models are integrated in one system to eliminate the limitations of the individual systems. The system is
implemented in MongoDB which is a NoSQL database and SQL. The results obtained, revealed that having
these two databases in one system can enhance storage and management of big data bridging the gap
between relational and NoSQL storage approach.
HYBRID DATABASE SYSTEM FOR BIG DATA STORAGE AND MANAGEMENTIJCSEA Journal
This document proposes a hybrid database system that integrates a NoSQL database (MongoDB) and a relational database (MySQL) to address the limitations of each individual system for big data storage and management. It discusses the properties of big data, reviews the approaches of relational and NoSQL databases, highlights their strengths and weaknesses, and then describes the proposed hybrid system that categorizes data as structured or unstructured and stores it in the appropriate database to leverage the benefits of both models. The system is designed to enhance big data storage and management by bridging the gaps between relational and NoSQL approaches.
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.
NoSQL databases were developed to address the challenges of building modern applications, including massive amounts of rapidly changing data, agile development processes, and global scaling. NoSQL databases are scalable, provide high performance, and support dynamic schemas, auto-sharding, replication, and integrated caching without the complexity of relational databases. Key features include flexible data models, easy horizontal scaling, and automatic data distribution and failover.
This document contains 21 references related to NoSQL databases. It includes references to articles that discuss why NoSQL databases are needed, the characteristics and advantages of NoSQL databases over SQL databases, examples of NoSQL data models, and real-world use cases of MongoDB and Cassandra. The references cover topics like the CAP theorem, non-relational data models, and query operators for MongoDB.
Challenges Management and Opportunities of Cloud DBAinventy
Research Inventy provides an outlet for research findings and reviews in areas of Engineering, Computer Science found to be relevant for national and international development, Research Inventy is an open access, peer reviewed international journal with a primary objective to provide research and applications related to Engineering. In its publications, to stimulate new research ideas and foster practical application from the research findings. The journal publishes original research of such high quality as to attract contributions from the relevant local and international communities.
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.
A Study on Graph Storage Database of NOSQLIJSCAI Journal
This document summarizes a research paper on graph storage databases in NoSQL. It discusses big data and the need for alternative databases to handle large, diverse datasets. It defines the key aspects of big data including volume, velocity, variety and complexity. It also describes different types of NoSQL databases, focusing on the basic structure of graph databases. Graph databases use nodes and relationships to model connected data. The document compares several graph database systems and discusses advantages like performance and flexibility as well as disadvantages like complexity. It outlines several applications of graph databases in areas like social networks and logistics.
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.
The rising interest in NoSQL technology over the last few years resulted in an increasing number of evaluations and comparisons among competing NoSQL technologies From survey we create a concise and up-to-date comparison of NoSQL engines, identifying their most beneficial use from the software engineer point of view.
MongoDB is a document-oriented NoSQL database that uses JSON-like documents with optional schemas. It provides high performance, high availability, and easy scalability. MongoDB is also called "humongous" because it is designed to store and handle large volumes of data. Some key advantages of MongoDB include its ability to handle large, unstructured data sets and provide agile development with quick code iterations.
Databases are organized collections of data that allow for efficient data access and management. There are different types of databases including relational databases, NoSQL databases, object-oriented databases, and graph databases. Databases have evolved over time from flat file systems to hierarchical, network, relational, and modern cloud-based systems. A database management system provides tools for creating, accessing, and managing databases and ensures security, integrity, and consistency of stored data.
Analysis and evaluation of riak kv cluster environment using basho benchStevenChike
This document analyzes and evaluates the performance of the Riak KV NoSQL database cluster using the Basho-bench benchmark tool. Experiments were conducted on a 5-node Riak KV cluster to test throughput and latency under different workloads, data sizes, and operations (read, write, update). The results found that Riak KV can handle large volumes of data and various workloads effectively with good throughput, though latency increased with larger data sizes. Overall, Riak KV is suitable for distributed big data environments where high availability, scalability and fault tolerance are important.
A database is an organized collection of data stored and accessed electronically. A database management system (DBMS) is software that allows users to define, create, maintain and control access to the database. Well-known DBMSs include MySQL, Oracle, SQL Server and IBM DB2. A DBMS manages storage, security, querying and integrity of the data in the database. The most popular database model since the 1980s has been the relational model which represents data in tables related through keys.
This document discusses NoSQL databases and compares them to relational databases. It begins by explaining that NoSQL databases were developed to address scalability issues in relational databases. The document then categorizes NoSQL databases into four main types: key-value stores, column-oriented databases, document stores, and graph databases. For each type, popular examples are provided (e.g. DynamoDB, Cassandra, MongoDB) along with descriptions and use cases. The advantages of NoSQL databases over relational databases are also briefly touched on.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
NOSQL in big data is the not only structure langua.pdfajajkhan16
This presentation discusses the limitations of relational database management systems (RDBMS) in handling large datasets and introduces NoSQL databases as an alternative. It begins by defining RDBMS and describing issues with scaling RDBMS to big data through techniques like master-slave architecture and sharding. It then defines NoSQL databases, explaining why they emerged and classifying them into key-value, columnar, document, and graph models. The presentation concludes that both RDBMS and NoSQL databases have advantages, suggesting a polyglot approach is optimal to handle different data storage needs.
The document discusses the rise of NoSQL databases. It notes that NoSQL databases are designed to run on clusters of commodity hardware, making them better suited than relational databases for large-scale data and web-scale applications. The document also discusses some of the limitations of relational databases, including the impedance mismatch between relational and in-memory data structures and their inability to easily scale across clusters. This has led many large websites and organizations handling big data to adopt NoSQL databases that are more performant and scalable.
HYBRID DATABASE SYSTEM FOR BIG DATA STORAGE AND MANAGEMENTIJCSEA Journal
Relational database systems have been the standard storage system over the last forty years. Recently,
advancements in technologies have led to an exponential increase in data volume, velocity and variety
beyond what relational databases can handle. Developers are turning to NoSQL which is a non- relational
database for data storage and management. Some core features of database system such as ACID have
been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and
management of extremely voluminous data of diverse components known as big data, such that the two
models are integrated in one system to eliminate the limitations of the individual systems. The system is
implemented in MongoDB which is a NoSQL database and SQL. The results obtained, revealed that having
these two databases in one system can enhance storage and management of big data bridging the gap
between relational and NoSQL storage approach.
HYBRID DATABASE SYSTEM FOR BIG DATA STORAGE AND MANAGEMENTIJCSEA Journal
This document proposes a hybrid database system that integrates a NoSQL database (MongoDB) and a relational database (MySQL) to address the limitations of each individual system for big data storage and management. It discusses the properties of big data, reviews the approaches of relational and NoSQL databases, highlights their strengths and weaknesses, and then describes the proposed hybrid system that categorizes data as structured or unstructured and stores it in the appropriate database to leverage the benefits of both models. The system is designed to enhance big data storage and management by bridging the gaps between relational and NoSQL approaches.
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.
NoSQL databases were developed to address the challenges of building modern applications, including massive amounts of rapidly changing data, agile development processes, and global scaling. NoSQL databases are scalable, provide high performance, and support dynamic schemas, auto-sharding, replication, and integrated caching without the complexity of relational databases. Key features include flexible data models, easy horizontal scaling, and automatic data distribution and failover.
This document contains 21 references related to NoSQL databases. It includes references to articles that discuss why NoSQL databases are needed, the characteristics and advantages of NoSQL databases over SQL databases, examples of NoSQL data models, and real-world use cases of MongoDB and Cassandra. The references cover topics like the CAP theorem, non-relational data models, and query operators for MongoDB.
Microsoft wants to enter the movie industry but lacks knowledge. The author analyzed recent box office data and found:
1) Animated musicals budgeted $75-200M and released in June/November or live-action superhero films budgeted $200-400M released in April/May tend to succeed.
2) Hiring composers for animated musicals and directors who worked on top-grossing superhero films is recommended.
3) This analysis of factors like genre, budget, and release time can help Microsoft maximize revenue from its initial movie productions.
The discrete logarithm problem (DLP) is the basis for elliptic curve cryptography (ECC) and differs from the integer factorization problem in RSA. In ECC over a finite field, the DLP is to find the exponent that computes one point on the elliptic curve as a multiple of another point, given the curve equation and two points. In RSA, the problem is to find the prime factors of a composite integer. While general algorithms exist to solve both, the DLP in ECC providing equivalent security to RSA requires smaller key sizes, making ECC more efficient.
A student named Saqib Ullah submitted a request to the registrar for a refund of his semester dues. He had paid his full fees for the previous semester without any deduction due to a fear of not being allowed to take exams. This semester, he received an EHSAAS scholarship which resulted in a negative balance of 36,000. As it is his last semester, he is requesting a refund of the amount the university owes him.
Empowering Excellence Gala Night/Education awareness Dubaiibedark
The primary goal is to raise funds for our cause, which is to help support educational programs for underprivileged children in Dubai. The gala also aims to increase awareness of our mission and foster a sense of community among attendees
Satta Matta Matka-satta matta matka 143,satta matta matka 420,satta matta matka fix open matka 420 786 matka 420 target matka Sona Matka 420 final ank time matka 420 matka boss 420 fix satta matta matka Kalyan panel chart kalyan night chart kalyan jodi chart kalyan chart
Dp Boss ,Satta Matka ,Indian Matka, Kalyan Matka,Matka 420,Satta Matta Matka 143 , Matka Guessing, India Matka, Indian Satta, Dp Boss Matka Guessing India Satta
Kalyan Panel Chart ,Kalyan Matka Panel Chart ,Kalyan Jodi Chart Kalyan Chart Kalyan Matka, Kalyan Satta Kalyan Panna , Patti Chart, Kalyan Guessing
Kalyan Jodi Chart,Satta Matka Guessing - Kalyan Matka 420 - Satta Matta Matka 143 - Indian Matka - Indian Satta - Satta Matka Chart - Satta Matka 143 - Ka Matka - Dp Boss Net - Fix Fix Fix Satta Namber - Satta Batta - Tara Matka - Satta Live - Kalyan Open - Golden Matka - Satta Guessing - Kalyan Night Chart - Satta Result - Kalyan Chart - Kalyan Panel Chart - Satta 1438 - Kalyan Jodi Chart -Satta - Matka - Satta Batta SATTA MATKA-KALYAN PANEL CHART | KALYAN MATKA | KALYAN RESULT | KALYAN CHART | KALYAN SATTA | KALYAN SATTA MATKA | KALYAN PANEL CHART | KALYAN MATKA LIVE RESULT | KALYAN LIVE | SATTA MATKA | MATKA RESULT | ALL MATKA RESULT | MAIN BAZAR MATKA | MAIN BAZAR RESULT | MAIN BAZAR CHART | RAJDHANI CHART RAJDHANI NIGHT CHART | RAJDHANI NIGHT | SATTA MATTA MATKA 143 | MATKA 420 | MATKA GUESSING | SATTA GUESSING | MATKA BOSS OTG | INDIAN MATKA | INDIAN SATTA | INDIA MATKA | INDIA SATTA | MATKA | SATTA BATTA | DP BOSS | INDIA MATKA 786 | FIX FIX FIX SATTA NAMBER | FIX FIX FIX OPEN | MATKA BOSS 440
Satta Matka, Kalyan Matka, Satta , Matka, India Matka ,Satta Matka 420, Satta Matka Guessing, India Satta,Matka Jodi Fix ,Kalyan Satta Guessing, Fix Fix Fix Satta Nambar,Kalyan Chart, Kalyan Panel Chart, Kalyan Jodi Chart,Satta Matka Chart,Satta Matka Jodi Fix, Indian Matka 420 786,Satta Matta Matka 143
KALYAN MATKA | MATKA RESULT | KALYAN MATKA TIPS | SATTA MATKA | MATKA.COM | MATKA PANA JODI TODAY | BATTA SATKA | MATKA PATTI JODI NUMBER | MATKA RESULTS | MATKA CHART | MATKA JODI | SATTA COM | FULL RATE GAME | MATKA GAME | MATKA WAPKA | ALL MATKA RESULT LIVE ONLINE | MATKA RESULT | KALYAN MATKA RESULT | DPBOSS MATKA 143 | ΜΑΙΝ ΜΑΤΚΑ❾❸❹❽❺❾❼❾❾⓿
➒➌➎➏➑➐➋➑➐➐ Satta Matka Dpboss Matka Guessing Indian MatkaKALYAN MATKA | MATKA RESULT | KALYAN MATKA TIPS | SATTA MATKA | MATKA.COM | MATKA PANA JODI TODAY | BATTA SATKA | MATKA PATTI JODI NUMBER | MATKA RESULTS | MATKA CHART | MATKA JODI | SATTA COM | FULL RATE GAME | MATKA GAME | MATKA WAPKA | ALL MATKA RESULT LIVE ONLINE | MATKA RESULT | KALYAN MATKA RESULT | DPBOSS MATKA 143 | MAIN MATKA
DP boss matka results IndiaMART Kalyan guessing➑➌➋➑➒➎➑➑➊➍
SATTA MATKA SATTA FAST RESULT KALYAN TOP MATKA RESULT KALYAN SATTA MATKA FAST RESULT MILAN RATAN RAJDHANI MAIN BAZAR MATKA FAST TIPS RESULT MATKA CHART JODI CHART PANEL CHART FREE FIX GAME SATTAMATKA ! MATKA MOBI SATTA
Satta Matka | Satta Matta Matka 143 | Fix Matka | Indian Satta | Kalyan Chart | Fix Fix Fix Satta Namber | Kalyan Satta | Kalyan Matka | Kalyan Panel Chart | Kalyan Jodi Chart | Satta Result | Satta Live | Satta Guessing | Satta King | Satta 143 | Rajdhani Satta Result | Matka Guessing | Sona Matka | Matka 420 | Kalyan Open | Matka Boss | Ka Matka | Dp Boss Matka | Matka Tips Today | Kalyan Today | Matka Result | India Matka
#satta #matka #kalyantoday #taramatka #matkaboss #matka420 #indiaMatka
#sattamattamatka143 #sattamatka #indianMatka #kalyanchart #kalyanmatka #kalyanjodichart #sattabatta #matkaguessing
#indianmatka #matkafixjodi
AskXX Pitch Deck Course: A Comprehensive Guide
Introduction
Welcome to the Pitch Deck Course by AskXX, designed to equip you with the essential knowledge and skills required to create a compelling pitch deck that will captivate investors and propel your business to new heights. This course is meticulously structured to cover all aspects of pitch deck creation, from understanding its purpose to designing, presenting, and promoting it effectively.
Course Overview
The course is divided into five main sections:
Introduction to Pitch Decks
Definition and importance of a pitch deck.
Key elements of a successful pitch deck.
Content of a Pitch Deck
Detailed exploration of the key elements, including problem statement, value proposition, market analysis, and financial projections.
Designing a Pitch Deck
Best practices for visual design, including the use of images, charts, and graphs.
Presenting a Pitch Deck
Techniques for engaging the audience, managing time, and handling questions effectively.
Resources
Additional tools and templates for creating and presenting pitch decks.
Introduction to Pitch Decks
What is a Pitch Deck?
A pitch deck is a visual presentation that provides an overview of your business idea or product. It is used to persuade investors, partners, and customers to take action. It is a concise communication tool that helps to clearly and effectively present your business concept.
Why are Pitch Decks Important?
Concise Communication: A pitch deck allows you to communicate your business idea succinctly, making it easier for your audience to understand and remember your message.
Value Proposition: It helps in clearly articulating the unique value of your product or service and how it addresses the problems of your target audience.
Market Opportunity: It showcases the size and growth potential of the market you are targeting and how your business will capture a share of it.
Key Elements of a Successful Pitch Deck
A successful pitch deck should include the following elements:
Problem: Clearly articulate the pain point or challenge that your business solves.
Solution: Showcase your product or service and how it addresses the identified problem.
Market Opportunity: Describe the size, growth potential, and target audience of your market.
Business Model: Explain how your business will generate revenue and achieve profitability.
Team: Introduce key team members and their relevant experience.
Traction: Highlight the progress your business has made, such as customer acquisitions, partnerships, or revenue.
Ask: Clearly state what you are asking for, whether it’s investment, partnership, or advisory support.
Content of a Pitch Deck
Pitch Deck Structure
A pitch deck should have a clear and structured flow to ensure that your audience can follow the presentation.
Progress Report - Qualcomm AI Workshop - AI available - everywhereAI summit 1...Holger Mueller
Qualcomm invited analysts and media for an AI workshop, held at Qualcomm HQ in San Diego, June 26th. My key takeaways across the different offerings is that Qualcomm us using AI across its whole portfolio. Remarkable to other analyst summits was 50% of time being dedicated to demos / hands on exeriences.
8328958814KALYAN MATKA | MATKA RESULT | KALYAN MATKA TIPS | SATTA MATKA | MATKA➑➌➋➑➒➎➑➑➊➍
8328958814KALYAN MATKA | MATKA RESULT | KALYAN MATKA TIPS | SATTA MATKA | MATKA.COM | MATKA PANA JODI TODAY | BATTA SATKA | MATKA PATTI JODI NUMBER | MATKA RESULTS | MATKA CHART | MATKA JODI | SATTA COM | FULL RATE GAME |
Satta matka DP boss matka Kalyan result India matka
Report 1.0.docx
1. Abstract:
Growing firms confront a variety of obstacles and possibilities that need long-term planning. What worked a year ago
may no longer be the greatest strategy today.Similarly, while developing a new enterprise.NET application, you must
considercarefully and select the best technology for the job. If you make the wrong decision, it will have a long-term
impact on your entire firm. Choosing the correct database technology is one ofthe factors that will influence how your
application is designed (1). With the growth of the Internet and cloud computing, databases must be able to store and
process large amounts of data efficiently, as well as provide high performance while reading and writing, therefore
the classic relational database is facing several new issues. Using a relational database to store and query dynamic
userdata has proven insufficient, particularly in large-scale and high-concurrency applications such as search engines
and social networking sites. In this instance, a NoSQL database was constructed. This paper discusses the history,
fundamental features,and data model of NoSQL and finally some renowned NoSQL databases are being discussed to
help corporate firms decide which database to use (2).
Key words: NoSQL; Key-value; Column Oriented; document; database; Big Data
1. Introduction:
The fundamental question ofwhy do we need NoSQL database when we can use SQL database can be answered using
the analogy of different types ofscrewdrivers. Almost all screwdrivers do the same job i.e., tightening and loosing the
screws. So why don’t we use the best one eliminated the rest? Standard/slotted screws are less expensive to produce.
They're difficult to use with electric screwdrivers, though,since the driverkeeps falling out.Phillips-head screwdrivers
are preferable for this, although they can peel out if the metal isn't strong enough. And so forth. Each type of screw
has a purpose and is beneficial for a variety of applications.It's the same with a lot of otherforms of technology.There
are tradeoffs between complexity and ease, cost to create vs. cost to use, and so on (3).
Likewise different types of databases are used for different purposes:
A relational database is intended to meet the needs ofthe data. You create a schema that ensures well-formed
data. When you know that the data is properly structured, it is easy to create queries (4).
NoSQL databases are more flexible, allowing you to begin storing data in a variety of formats. These are
more versatile in terms of data format, but there is no assurance ofdata consistency therefore writing queries
is more difficult. Furthermore, the database architecture is centered on yourquery needs rather than the data
requirements. So, you must first assess the queries that must be executed (5).
With the evolution of database technology over the ages many different database systems have emerged. With the
advancements in internet technologies and hardware components, the databases are now high in demand than ever
before for the following reasons:
Connectivity and centralization:
Most DBMS only support one form of database,such as SQL, NoSQL, or Key-value. This means that you'd require a
graphical userinterface (GUI) for each of these databases in order to handle them as an administrator. A good DBMS
should help consolidate all of these databases and operate as a data warehouse.
Processing in memory:
An in-memory database stores all data in a computer's main memory, or RAM. A typical database pulls information
from hard discs. When it comes to data processing,in-memory databases have a significant performance advantage.
They are significantly quicker than traditional databases because they utilize fewer CPU instructions and eliminate
the time required to read data from a drive.
Performance and distributed computing:
2. When it comes to RDBMS, huge parallel computing has numerous major advantages. A. It can help to calculate a
greater quantity of data simultaneously, making it considerably quicker. B. Users are not required to access the
database in the same network environment.
Secure and IAM-enabled
Any DBMS must have Identity Access Management. Despite the fact that most systems are not inherently
collaborative. This implies a significant knowledge gap between the administrator and the end users. The finest
solutions should facilitate cooperation while maintaining a high level of security.
Excellent UI/UX
The majority of DBMS pay no attention to UI/UX. The best should appeal to customers from all backgrounds,since
data consumers nowadays might range from company managers to data analysts.A great user interface should assist
achieve significantly higher efficiency than an IT-dependent solution (6).
Although relational databases have a prominent position in the data storage sector,when confronted with the following
criteria, they have certain inherent limitations:
Poor Interface:
SQL databases have very complexinterface which makes it difficult for the user to interact.
Cost Inefficient:
SQL databases cost huge amount which make themvery expensive.
Partial Control:
Due to some best kept secret business policies SQL databases grants limited control to its users.
Security:
Due to huge amount of sensitive data being stored on SQL databases it is at huge risk concerning security (7).
A variety of different types of databases have emerged to address the aforementioned demands. Because these novel
databases differ significantly from typical relational databases, they are referred to as "NoSQL" databases (8).
2. Characteristics, data model, and categorization
2.1. Features of NoSQL Database
The main advantages ofNoSQL database are following: 1) Support for Multiple Data Models; 2) Easily Scalable via
Peer-to-Peer Architecture; 3) Flexibility: Versatile Data Handling; 4) Distribution Capabilities; 5) Zero Downtime
(9).
One of the key characteristics of NoSQL database and probably the reason of its development is how to handle
diverse data structures before writing it Thus, NoSQL refers to a wide range of databases that can readily store and
manage enormous amounts of semi-structured and unstructured data. They can manage a large volume of reads and
writes while also expanding data horizontally. Even if there is no logical grouping, NoSQL allows us to arrange any
type of stored data. It provides several benefits through the use of various types of databases,such as hybrid cache
stores, graph databases, and so on (10).
3. I've (for the sake of research) recently experimented with NoSQL databases such as MongoDB, Cassand ra, and
CouchDB. NoSQL is not designed for server-side querying, as far as I am aware. There is no join procedure, thus
embedding records into one collection is ineffective. It is not suited for use in commercial transactions.Aggregation
framework for these NoSQL databases currently lacks performance adjustment. And so forth. It is suitable for
applications that do not require numerous transactions but require a large amount of data storage. Also, there is no
common, standardized query language which is the industry standard as there is in SQL for relational databases.
NoSQL databases have been existed since the 1960s, but it wasn't until the early twenty-first century that
businesses began to embrace them, particularly to manage huge data and real-time online and cloud
applications. Since then, the NoSQL database has grown in popularity and utility, albeit relational databases
remain useful (11).
2.2. Data Model
Data models describe how a database's logical structure is represented. Data Models are basic elements in a DBMS
for introducing abstraction. Data models determine how data is linked to one anotherand how it is handled and stored
inside the system. The very first data model might be flat data-models, in which all data is held on the same plane.
Earlier data models were not as scientific; therefore, they were prone to duplication and update abnormalities (12).
The following are the NoSQL database fields and the standard data model:
2.2.1. Key-Valued:
The key-value database may be viewed as an associative array, which is a generalization of a standard array, i.e.
A["anything"]= "something," where "anything" is the key and "something" is the value. This is a strong notion; for
example, an in-memory key-value database such as Redis may act as a distributed hash table, expanding the RAM
concept. You may use this simple yet effective program to pool (cluster) the RAM of numerous commodity PCs to
build a massive main-memory to offer your application a significant speed increase. Of course, there are additional
factors to consider, but let's avoid the gory specifics (13).
2.2.2. Column Family:
Column Family databases may be viewed as C structure objects with an arbitrary number of fields. This class includes
Cassandra and HBase. These can alternatively be viewed as "relaxed" variations of typical RDBMS databases with
the primary purpose of scalability. This work is suitable for processing column-oriented data.
2.2.3. Document:
The structure of a document database and a key-value database is quite similar; however, the value of a document
database is semantic and is saved in JSON or XML format. Furthermore, document databases may typically have a
Secondary Index to value to help with the higher application, although key-value databases cannot (14).
2.2.4. Graph:
Graph databases model the most complicated data by making data relationships first-class residents of the system.
These databases can perform fast graph focused queries such as path traversals by optimally modelling the data as
graphs, multi-graphs, or hyper-graphs. So, they are fantastic for friend-of-a-friend questions. Some of them,
interestingly, employ key-value databases as an underlying technology to improve speed and accomplish scalability.
Some good examples include Neo4j and Hypergraph db.
4. 2.3. CAP Theorem and NoSQL
According to the CAP theorem, a distributed system cannot be consistent, available, and partition tolerant at the
same time. When we needed to store more data or expand our processing power in the past, we had two options:
grow vertically (buy more powerful computers) or further optimize the existing code base. However, with
advancements in parallel processing and distributed systems, it is more typical to extend horizontally, or to have
several computers performing the same work in parallel. In the Apache project, we can already witness a slew of
data manipulation tools like as Spark, Hadoop, Kafka, Zookeeper, and Storm. However, in order to properly select
the tool of choice, a fundamental understanding of the CAP Theorem is required. The CAP Theorem states that a
distributed database system can only have two of the three characteristics: consistency, availability, and partition
tolerance (15).
The classification of NoSQL according to CAP theorem is as follows (16):
Consistency and availability (CA):
Part of the database is unconcerned with partition tolerance and instead relies on the replication strategy to assure
data consistency and availability. CA-related systems include the standard relational database, Vertica (column -
oriented), Aster Data (Relational), Greenplum (Relational), and others.
Consistency and partition tolerance (CP):
Such a database system stores data on remote nodes while also ensuring data consistency, although support is
insufficient for availability. BigTable (Column-oriented), Hypertable (Column-oriented), HBase (Column-oriented),
MongoDB (Document), Terrastore (Document), Redis (Key-value), Scalaris (Key-value), MemcacheDB (Key-
value), Berkeley DB are the primary CP systems (Key-value).
Availability and partition tolerance (AP):
Such systems provide availability and partition tolerance largely through consistency, as demonstrated by AP's
system: Voldemort (Key-value), Tokyo Cabinet (Key-value), KAI (Key-value), CouchDB (Document-oriented),
SimpleDB (Document-oriented), Riak (Document-oriented), Riak (Document-oriented), Riak ( (Document-
oriented).
5. 3. Analysis of popular NoSQL databases
3.1. MongoDB
MongoDB is an open source NOSQL database written in C++ language.
3.1.1. Structure and Data model
MongoDB (17) is a document database,not a relational database management system. That is, records are saved as
documents (usually JSON) rather than tuples. As a result, data modelling for MongoDB necessitates a more object -
oriented approach than data modelling for an RDBMS. Both composition and association relations, for example, can
be described using mapping tables in an RDBMS; it is up to the application to enforce the exact nature of the
connection - composition or association. However, in the case of a document database such as MongoDB, a
composition relation must be enforced at the document level using nested/contained objects.
3.1.2. Query operators
Query operations in mongo dB can be used for the following purposes (18):
Comparison:
For e.g., “$eq” matches values that are equal to a specified value; “$gt” matches values that are greater than a
specified value; “$lt” matches values that are less than a specified value, etc.
Logical operations:
For e.g., “$and” joins query clauses with a logical AND returns all documents that match the conditions of both
clauses; “$not” inverts the effect of a query expression and returns documents that do not match the query expression;
“$nor” joins query clauses with a logical NOR returns all documents that fail to match both clauses; “$or” joins
query clauses with a logical OR returns all documents that match the conditions of either clause.
Element:
For e.g., “$exists” Matches documents that have the specified field; “$type” selects documents if a field is of
the specified type.
Evaluation:
For e.g., “$mod” performs a modulo operation on the value of a field and selects documents with a specified result.
Geospatial:
For e.g., “geoIntersects” selects geometries that intersect with a GeoJSON geometry. The 2dsphere index supports
$geoIntersects.
Array
For e.g., “$all” matches arrays that contain all elements specified in the query.
Bitwise
For e.g., “$bitsAllClear” matches numeric or binary values in which a set of bit positions all have a value of 0.
6. 3.1.3. Examples of query operations
In MongoDB, you must first create a collection before you can create a table. The collection works much the same
way that we would build a table when putting data into a relational database.
To insert the document in the MongoDB collection, you have to use the insert() method.
3.1.4. Common Usage and market
Some of the most common mongo dB usage aur as follows (19):
Mobility and Scaling:
MongoDB is highly scalable and adaptable, making it ideal for dealing with a wide range of settings.
Real time data integration:
Data has a lot of value when it is condensed and aggregated into a single perspective, and MongoDB plays an
important part in that.
Product Catalog:
7. There are many attributes to products which are easily stored as an object using MongoDB and can be used to
understand the customer better in the digital experience.
Some of the world’s renowned companies that use mongo dB includes: eBay, MetLife, Shutterfly, Aadhar, EA
among many others.
3.1.5. Enhancements in the platform
MongoDB is a highly helpful and easy solution, however for more complicated projects, there may be certain
specialized needs that necessitate the usage of a different technology.It increases the complexity of the architecture,
yet it may be helpful to the world, the features, and the simplicity with which the features are implemented. Also, it
could be more stable. It would be preferable if it were more user-friendly, like Oracle is. In Oracle, for example,
building an index is straightforward. It's difficult to achieve that with MongoDB. Performance might be improved.
It's quick and reliable, but you can't put every application you want on MongoDB.
3.2. Cassandra
Cassandra (20) is an opensource database of Facebook.
3.2.1. Structure and Data model
Its qualities are as follows: 1) the schema is extremely flexible and does not require the creation of a database schema
at the outset, and adding or deleting fields is quite simple. 2) Support range queries, i.e., range queries for Key; 3)
High scalability: a single point of failure does not affect the entire cluster, and linear extension is supported.
Cassandra is a distributed database systemmade up of several database nodes; a write operation is replicated to other
nodes, and a read request is routed to a specific node. Scalability may be achieved by simply adding nodes to a
Cassandra cluster. Cassandra also has a complex data structure and a sophisticated query language.
3.2.2. Query Operation and example
The Cassandra Update query is used to update the Cassandra table's data. If no results are given after changing data,
this indicates that the data was properly changed; otherwise, an error will be issued.The 'Set' clause modifies column
values while the 'Where' clause filters data.
Syntax:
Update KeyspaceName.TableName
Set ColumnName1=new Column1Value,
ColumnName2=new Column2Value,
ColumnName3=new Column3Value,
.
.
.
Where ColumnName=ColumnValue
Here is a snapshot of the database before data was updated.
After running the following query:
Update University.Student
Set name='Hayden'
8. Where rollno=1;
Here is the screenshot that shows the database state after updating data.
3.2.3. Common usage and market
Cassandra was used to power the new Actionable Analytics product at Ooyala (21), a company that provides an end-
to-end online video platform to corporate companies. All video engagement and analytics reports delivered to clients
are aggregated across many dimensions and sliceable by the user's geo (country/region/city), the site/URL where a
video is embedded, various advertising parameters, and a variety of additional tags.
Cassandra offered the scalability and better write throughput required to support the new analytics offering without
the requirement to develop their sharding and replication on top of MySQL. Ooyala manage a big volume of analytics
data (> 1B data points per month) with millions of hours of video supplied each month.
3.2.4. Enhancement in the product
Cassandra's secondary index is somewhat troublesome and might be improved. Cassandra can improve by having a
fuller ecosystemintegrator. Companies, for example, required to deploy extra tools in some maintenance operations
to execute duties that were not provided alongside Cassandra. Another limitation of the product is that you cannot
drop writes like you do in MongoDB and MySQL, where you may link tables. Cassandra does not provide joins
across tables; thus, you must use another tool for this. You must read all of the data and store it in memory before
adding the joins. That is an area where they can improve.
4. Conclusion
Both SQL and NoSQL database formats store data, but how they do so vary. Outline the application to choose the
best database architecture for data structures such schema, relation, scalability, and data size. Migrating from one to
the other is expensive and time-consuming, thus the differences should be addressed while designing software. This
paper begins by comparing traditional databases with that of NoSQL databases. Then it explains the underlying
working of NoSQL database and then finally two different NoSQL databases are discussed. Each type was database
has its own pros and cons. NoSQL is a recent technology and many of its dimensions are yet in a phase of
development. This paper provides a comprehensive overview to the businesses of the kind of database they should
use based on their functionalities.