iForum 2015: SQL vs. NoSQL
•Download as PPTX, PDF•
2 likes•1,756 views
Если раньше при старте нового проекта нам нужно было выбрать одну из доступных на тот момент SQL баз данных, то за последние 5 лет ситуация кардинально изменилась. Теперь выбор стал гораздо сложнее. SQL или NoSQL? Сloud или on-premises? Если SQL/NoSQL - то какая именно? А может использовать и то и другое? В данном докладе мы постараемся представить общий обзор доступных сегодня решений для хранения данных и определиться с критериями выбора.
Report
Share
Report
Share
Recommended
SQL vs. NoSQL. It's always a hard choice.
This will be an interesting and sometimes fun session with a small demo. This session will answer some of your questions and force you to think about new questions. It will not be very technical, so it's ok for choose another more technical session from the schedule :) But if will decide to come, I can assure you, that you will not be disappointed. We will do a thought experiment with one famous public high-loaded website, will look at advantages and disadvantages of SQL and NoSQL databases, and will choose the best database engine for it.
SQL vs NoSQL: Big Data Adoption & Success in the Enterprise
Overview of SQL vs NoSQL. When to use NoSQL vs structured databases. Shows roadmap and considerations for defining success of implementation of Big Data in the enterprise. This presentation also provides a quick overview of the different types of Big-Data databases
NoSQL vs SQL (by Dmitriy Beseda, JS developer and coach Binary Studio Academy)
It is first lecture from noSQL course for students of Lviv Polytechnic National University. Check out our educational portal: http://academy.binary-studio.com/
SQL vs NoSQL
This document compares SQL and NoSQL databases. It discusses the history of SQL databases and how NoSQL databases were developed to address modern demands like scaling, fault tolerance, and conflicting queries arriving at different nodes. The document notes that NoSQL databases generally offer more flexible data models and eventual consistency, but at the cost of less predictable behavior compared to SQL databases.
Sql vs nosql
The document compares SQL and NoSQL databases, discussing their characteristics and use cases. It provides details on SQL databases, the scalability arguments for NoSQL, and tradeoffs of SQL features like ACID compliance in NoSQL systems. It also outlines several major types of NoSQL databases like key-value, column-oriented, document, and graph stores, comparing their data models, performance, scalability, and functionality. The document advises considering factors like the problem, costs, programming needs, and performance requirements when choosing a database type.
MS DevDay - SQLServer 2014 for Developers
Presentation about hidden treasures inside SQL Server 2014. It was 30 min presentation about 4 features: Cardinality Estimator, Query and Plan Fingerprints, Delayed Durability and TempDB performance.
NOSQL vs SQL
The document compares NoSQL and SQL databases. It notes that NoSQL databases are non-relational and have dynamic schemas that can accommodate unstructured data, while SQL databases are relational and have strict, predefined schemas. NoSQL databases offer more flexibility in data structure, but SQL databases provide better support for transactions and data integrity. The document also discusses differences in queries, scaling, and consistency between the two database types.
SQL vs NoSQL
This document discusses SQL versus NoSQL databases. NoSQL databases are better suited for internet-scale applications with massive amounts of data and users due to their ability to provide high availability, high performance, and horizontal scalability. NoSQL databases sacrifice strict ACID properties for looser eventual consistency in order to better serve highly distributed systems. SQL remains preferable when strict ACID properties are required. The document provides examples of MongoDB and concludes that the right database choice depends on the specific needs and use case.
Recommended
SQL vs. NoSQL. It's always a hard choice.
This will be an interesting and sometimes fun session with a small demo. This session will answer some of your questions and force you to think about new questions. It will not be very technical, so it's ok for choose another more technical session from the schedule :) But if will decide to come, I can assure you, that you will not be disappointed. We will do a thought experiment with one famous public high-loaded website, will look at advantages and disadvantages of SQL and NoSQL databases, and will choose the best database engine for it.
SQL vs NoSQL: Big Data Adoption & Success in the Enterprise
Overview of SQL vs NoSQL. When to use NoSQL vs structured databases. Shows roadmap and considerations for defining success of implementation of Big Data in the enterprise. This presentation also provides a quick overview of the different types of Big-Data databases
NoSQL vs SQL (by Dmitriy Beseda, JS developer and coach Binary Studio Academy)
It is first lecture from noSQL course for students of Lviv Polytechnic National University. Check out our educational portal: http://academy.binary-studio.com/
SQL vs NoSQL
This document compares SQL and NoSQL databases. It discusses the history of SQL databases and how NoSQL databases were developed to address modern demands like scaling, fault tolerance, and conflicting queries arriving at different nodes. The document notes that NoSQL databases generally offer more flexible data models and eventual consistency, but at the cost of less predictable behavior compared to SQL databases.
Sql vs nosql
The document compares SQL and NoSQL databases, discussing their characteristics and use cases. It provides details on SQL databases, the scalability arguments for NoSQL, and tradeoffs of SQL features like ACID compliance in NoSQL systems. It also outlines several major types of NoSQL databases like key-value, column-oriented, document, and graph stores, comparing their data models, performance, scalability, and functionality. The document advises considering factors like the problem, costs, programming needs, and performance requirements when choosing a database type.
MS DevDay - SQLServer 2014 for Developers
Presentation about hidden treasures inside SQL Server 2014. It was 30 min presentation about 4 features: Cardinality Estimator, Query and Plan Fingerprints, Delayed Durability and TempDB performance.
NOSQL vs SQL
The document compares NoSQL and SQL databases. It notes that NoSQL databases are non-relational and have dynamic schemas that can accommodate unstructured data, while SQL databases are relational and have strict, predefined schemas. NoSQL databases offer more flexibility in data structure, but SQL databases provide better support for transactions and data integrity. The document also discusses differences in queries, scaling, and consistency between the two database types.
SQL vs NoSQL
This document discusses SQL versus NoSQL databases. NoSQL databases are better suited for internet-scale applications with massive amounts of data and users due to their ability to provide high availability, high performance, and horizontal scalability. NoSQL databases sacrifice strict ACID properties for looser eventual consistency in order to better serve highly distributed systems. SQL remains preferable when strict ACID properties are required. The document provides examples of MongoDB and concludes that the right database choice depends on the specific needs and use case.
SQL & NoSQL
This document provides an overview of SQL and NoSQL databases. It defines SQL as a language used to communicate with relational databases, allowing users to query, manipulate, and retrieve data. NoSQL databases are defined as non-relational and allow for flexible schemas. The document compares key aspects of SQL and NoSQL such as data structure, querying, scalability and provides examples of popular SQL and NoSQL database systems. It concludes that both SQL and NoSQL databases will continue to be important with polyglot persistence, using the best database for each storage need.
Sql vs NO-SQL database differences explained
This document compares SQL and NoSQL databases. It outlines key differences between the two types of databases such as their data structures (tables vs documents/key-value pairs), schemas (strict vs dynamic), scalability (vertical vs horizontal), and query languages (SQL vs unstructured). Examples of popular SQL databases discussed are MySQL, MS-SQL Server, and Oracle. Examples of NoSQL databases discussed are MongoDB, CouchDB, and Redis. The document provides an overview of each example database's features and benefits.
Sql vs. NoSql
The document discusses SQL versus NoSQL databases. It notes that while SQL has been stable for relational databases, it may not be the best fit for new big data workloads that involve huge datasets in multiple formats that need easy scaling. NoSQL databases provide more flexibility in storage and easier scaling capabilities that are better suited for big data. The document recommends learning NoSQL and preparing to migrate data and code from SQL to a NoSQL model.
Rdbms vs. no sql
This document compares RDBMS and NoSQL databases. RDBMS uses SQL and follows ACID properties, storing data in tables and columns. NoSQL databases are non-relational, distributed, and horizontally scalable. Common NoSQL databases include MongoDB, Cassandra, and HBase. NoSQL databases sacrifice consistency for availability and partition tolerance as described by CAP theorem.
SQL vs NoSQL
Explaining the most important distinctions and discuss the best SQL and NoSQL database systems available.
NoSQL Databases
This document provides an overview of different database types including relational, NoSQL, document, key-value, graph, and column family databases. It discusses the history and drivers behind the development of NoSQL databases, as well as concepts like horizontal scaling, the CAP theorem, and eventual consistency. Specific databases are also summarized, including MongoDB, Redis, Neo4j, and HBase.
Selecting best NoSQL
The document discusses factors to consider when selecting a NoSQL database management system (DBMS). It provides an overview of different NoSQL database types, including document databases, key-value databases, column databases, and graph databases. For each type, popular open-source options are described, such as MongoDB for document databases, Redis for key-value, Cassandra for columnar, and Neo4j for graph databases. The document emphasizes choosing a NoSQL solution based on application needs and recommends commercial support for production systems.
Introduction to Azure DocumentDB
A quick introduction to Azure DocumentDB - a JSON document store that's Platform as a Service on Microsoft Azure
SQL or NoSQL, that is the question!
The document discusses SQL versus NoSQL databases. It provides background on SQL databases and their advantages, then explains why some large tech companies have adopted NoSQL databases instead. Specifically, it describes how companies like Amazon, Facebook, and Google have such massive amounts of data that traditional SQL databases cannot adequately handle the scale, performance, and flexibility needs. It then summarizes some popular NoSQL databases like Cassandra, Hadoop, MongoDB that were developed to solve the challenges of scaling to big data workloads.
NoSql Data Management
NoSQL, as many of you may already know, is basically a database used to manage huge sets of unstructured data, where in the data is not stored in tabular relations like relational databases. Most of the currently existing Relational Databases have failed in solving some of the complex modern problems like:
• Continuously changing nature of data - structured, semi-structured, unstructured and polymorphic data.
• Applications now serve millions of users in different geo-locations, in different timezones and have to be up and running all the time, with data integrity maintained
• Applications are becoming more distributed with many moving towards cloud computing.
NoSQL plays a vital role in an enterprise application which needs to access and analyze a massive set of data that is being made available on multiple virtual servers (remote based) in the cloud infrastructure and mainly when the data set is not structured. Hence, the NoSQL database is designed to overcome the Performance, Scalability, Data Modelling and Distribution limitations that are seen in the Relational Databases.
NoSQL Slideshare Presentation
This document provides an overview of NoSQL databases and HBase. It discusses why NoSQL databases are gaining popularity due to trends in data and architecture. It also summarizes the CAP theorem and how different databases balance consistency, availability and partition tolerance. The document describes research activities including evaluating HBase for telco usage and performing bulk processing tests on HBase. It finds that while HBase can scale horizontally, managing compaction storms and small files is challenging.
What is NoSQL and CAP Theorem
This document discusses NoSQL and the CAP theorem. It begins with an introduction of the presenter and an overview of topics to be covered: What is NoSQL and the CAP theorem. It then defines NoSQL, provides examples of major NoSQL categories (document, graph, key-value, and wide-column stores), and explains why NoSQL is used, including to handle large, dynamic, and distributed data. The document also explains the CAP theorem, which states that a distributed data store can only satisfy two of three properties: consistency, availability, and partition tolerance. It provides examples of how to choose availability over consistency or vice versa. Finally, it concludes that both SQL and NoSQL have valid use cases and a combination
SQL vs NoSQL, an experiment with MongoDB
A simple experiment with MongoDB compared to Oracle classic RDBMS database: what are NoSQL databases, when to use them, why to choose MongoDB and how we can play with it.
SQL vs. NoSQL
The relational database has been the dominant database model for many years. However, a new model called NoSQL is gaining significant attention. NoSQL DBs are non-relational data stores that have been employed in various scenarios, where traditional RDBMS features matter less, and the improved performance of storing or retrieving relatively simple data sets matters most. The relational and the NoSQL database model are each good for specific applications. Depending on the problem to solve, a NoSQL or a relational model can be advantageous. In this session we present some typical use cases and how they can be solved with both NoSQL and the RDMBS databases. Will there be clear a winner or is there room for both NoSQL and RDMBS in the future?
Introduction to NoSQL
The document summarizes a meetup about NoSQL databases hosted by AWS in Sydney in 2012. It includes an agenda with presentations on Introduction to NoSQL and using EMR and DynamoDB. NoSQL is introduced as a class of databases that don't use SQL as the primary query language and are focused on scalability, availability and handling large volumes of data in real-time. Common NoSQL databases mentioned include DynamoDB, BigTable and document databases.
Introduction to NoSQL
158ltd.com gives a rapid introduction to NoSQL databases: where they came from, the nature of the data models they use, and the different way you have to think about consistency.
Introduction to NoSQL Databases
This document provides an overview and introduction to NoSQL databases. It begins with an agenda that explores key-value, document, column family, and graph databases. For each type, 1-2 specific databases are discussed in more detail, including their origins, features, and use cases. Key databases mentioned include Voldemort, CouchDB, MongoDB, HBase, Cassandra, and Neo4j. The document concludes with references for further reading on NoSQL databases and related topics.
No SQL- The Future Of Data Storage
NoSQL database is the first alternative to relational databases, with scalability, availability, and fault tolerance being key deciding factors.
Elephants vs. Dolphins: Comparing PostgreSQL and MySQL for use in the DoD
- PostgreSQL and MySQL are both relational database management systems that store data in tables and views and allow users to interact with data using SQL. However, PostgreSQL supports additional features like native NoSQL capabilities, geospatial extensions, and a wider range of programming languages.
- While PostgreSQL and MySQL have similar basic functionality, PostgreSQL has a broader mission beyond the relational model and aims to support multiple data models and a fuller implementation of the SQL standard.
- For projects that may utilize more advanced features over their lifespan, such as NoSQL, geospatial analysis, or custom data types, PostgreSQL may be the better long-term choice compared to MySQL.
Non relational databases-no sql
This document provides an overview of non-relational (NoSQL) databases. It discusses the history and characteristics of NoSQL databases, including that they do not require rigid schemas and can automatically scale across servers. The document also categorizes major types of NoSQL databases, describes some popular NoSQL databases like Dynamo and Cassandra, and discusses benefits and limitations of both SQL and NoSQL databases.
NoSQLDatabases
The document discusses the history and concepts of NoSQL databases. It notes that traditional single-processor relational database management systems (RDBMS) struggled to handle the increasing volume, velocity, variability, and agility of data due to various limitations. This led engineers to explore scaled-out solutions using multiple processors and NoSQL databases, which embrace concepts like horizontal scaling, schema flexibility, and high performance on commodity hardware. Popular NoSQL database models include key-value stores, column-oriented databases, document stores, and graph databases.
How and when to use NoSQL
This document discusses NoSQL databases and when they should be used. It describes what NoSQL databases are, when to consider using one over a relational database, and introduces DynamoDB as an AWS NoSQL solution. Specific topics covered include the differences between relational and NoSQL data models, common use cases for NoSQL databases, and how to access and query DynamoDB tables.
More Related Content
What's hot
SQL & NoSQL
This document provides an overview of SQL and NoSQL databases. It defines SQL as a language used to communicate with relational databases, allowing users to query, manipulate, and retrieve data. NoSQL databases are defined as non-relational and allow for flexible schemas. The document compares key aspects of SQL and NoSQL such as data structure, querying, scalability and provides examples of popular SQL and NoSQL database systems. It concludes that both SQL and NoSQL databases will continue to be important with polyglot persistence, using the best database for each storage need.
Sql vs NO-SQL database differences explained
This document compares SQL and NoSQL databases. It outlines key differences between the two types of databases such as their data structures (tables vs documents/key-value pairs), schemas (strict vs dynamic), scalability (vertical vs horizontal), and query languages (SQL vs unstructured). Examples of popular SQL databases discussed are MySQL, MS-SQL Server, and Oracle. Examples of NoSQL databases discussed are MongoDB, CouchDB, and Redis. The document provides an overview of each example database's features and benefits.
Sql vs. NoSql
The document discusses SQL versus NoSQL databases. It notes that while SQL has been stable for relational databases, it may not be the best fit for new big data workloads that involve huge datasets in multiple formats that need easy scaling. NoSQL databases provide more flexibility in storage and easier scaling capabilities that are better suited for big data. The document recommends learning NoSQL and preparing to migrate data and code from SQL to a NoSQL model.
Rdbms vs. no sql
This document compares RDBMS and NoSQL databases. RDBMS uses SQL and follows ACID properties, storing data in tables and columns. NoSQL databases are non-relational, distributed, and horizontally scalable. Common NoSQL databases include MongoDB, Cassandra, and HBase. NoSQL databases sacrifice consistency for availability and partition tolerance as described by CAP theorem.
SQL vs NoSQL
Explaining the most important distinctions and discuss the best SQL and NoSQL database systems available.
NoSQL Databases
This document provides an overview of different database types including relational, NoSQL, document, key-value, graph, and column family databases. It discusses the history and drivers behind the development of NoSQL databases, as well as concepts like horizontal scaling, the CAP theorem, and eventual consistency. Specific databases are also summarized, including MongoDB, Redis, Neo4j, and HBase.
Selecting best NoSQL
The document discusses factors to consider when selecting a NoSQL database management system (DBMS). It provides an overview of different NoSQL database types, including document databases, key-value databases, column databases, and graph databases. For each type, popular open-source options are described, such as MongoDB for document databases, Redis for key-value, Cassandra for columnar, and Neo4j for graph databases. The document emphasizes choosing a NoSQL solution based on application needs and recommends commercial support for production systems.
Introduction to Azure DocumentDB
A quick introduction to Azure DocumentDB - a JSON document store that's Platform as a Service on Microsoft Azure
SQL or NoSQL, that is the question!
The document discusses SQL versus NoSQL databases. It provides background on SQL databases and their advantages, then explains why some large tech companies have adopted NoSQL databases instead. Specifically, it describes how companies like Amazon, Facebook, and Google have such massive amounts of data that traditional SQL databases cannot adequately handle the scale, performance, and flexibility needs. It then summarizes some popular NoSQL databases like Cassandra, Hadoop, MongoDB that were developed to solve the challenges of scaling to big data workloads.
NoSql Data Management
NoSQL, as many of you may already know, is basically a database used to manage huge sets of unstructured data, where in the data is not stored in tabular relations like relational databases. Most of the currently existing Relational Databases have failed in solving some of the complex modern problems like:
• Continuously changing nature of data - structured, semi-structured, unstructured and polymorphic data.
• Applications now serve millions of users in different geo-locations, in different timezones and have to be up and running all the time, with data integrity maintained
• Applications are becoming more distributed with many moving towards cloud computing.
NoSQL plays a vital role in an enterprise application which needs to access and analyze a massive set of data that is being made available on multiple virtual servers (remote based) in the cloud infrastructure and mainly when the data set is not structured. Hence, the NoSQL database is designed to overcome the Performance, Scalability, Data Modelling and Distribution limitations that are seen in the Relational Databases.
NoSQL Slideshare Presentation
This document provides an overview of NoSQL databases and HBase. It discusses why NoSQL databases are gaining popularity due to trends in data and architecture. It also summarizes the CAP theorem and how different databases balance consistency, availability and partition tolerance. The document describes research activities including evaluating HBase for telco usage and performing bulk processing tests on HBase. It finds that while HBase can scale horizontally, managing compaction storms and small files is challenging.
What is NoSQL and CAP Theorem
This document discusses NoSQL and the CAP theorem. It begins with an introduction of the presenter and an overview of topics to be covered: What is NoSQL and the CAP theorem. It then defines NoSQL, provides examples of major NoSQL categories (document, graph, key-value, and wide-column stores), and explains why NoSQL is used, including to handle large, dynamic, and distributed data. The document also explains the CAP theorem, which states that a distributed data store can only satisfy two of three properties: consistency, availability, and partition tolerance. It provides examples of how to choose availability over consistency or vice versa. Finally, it concludes that both SQL and NoSQL have valid use cases and a combination
SQL vs NoSQL, an experiment with MongoDB
A simple experiment with MongoDB compared to Oracle classic RDBMS database: what are NoSQL databases, when to use them, why to choose MongoDB and how we can play with it.
SQL vs. NoSQL
The relational database has been the dominant database model for many years. However, a new model called NoSQL is gaining significant attention. NoSQL DBs are non-relational data stores that have been employed in various scenarios, where traditional RDBMS features matter less, and the improved performance of storing or retrieving relatively simple data sets matters most. The relational and the NoSQL database model are each good for specific applications. Depending on the problem to solve, a NoSQL or a relational model can be advantageous. In this session we present some typical use cases and how they can be solved with both NoSQL and the RDMBS databases. Will there be clear a winner or is there room for both NoSQL and RDMBS in the future?
Introduction to NoSQL
The document summarizes a meetup about NoSQL databases hosted by AWS in Sydney in 2012. It includes an agenda with presentations on Introduction to NoSQL and using EMR and DynamoDB. NoSQL is introduced as a class of databases that don't use SQL as the primary query language and are focused on scalability, availability and handling large volumes of data in real-time. Common NoSQL databases mentioned include DynamoDB, BigTable and document databases.
Introduction to NoSQL
158ltd.com gives a rapid introduction to NoSQL databases: where they came from, the nature of the data models they use, and the different way you have to think about consistency.
Introduction to NoSQL Databases
This document provides an overview and introduction to NoSQL databases. It begins with an agenda that explores key-value, document, column family, and graph databases. For each type, 1-2 specific databases are discussed in more detail, including their origins, features, and use cases. Key databases mentioned include Voldemort, CouchDB, MongoDB, HBase, Cassandra, and Neo4j. The document concludes with references for further reading on NoSQL databases and related topics.
No SQL- The Future Of Data Storage
NoSQL database is the first alternative to relational databases, with scalability, availability, and fault tolerance being key deciding factors.
Elephants vs. Dolphins: Comparing PostgreSQL and MySQL for use in the DoD
- PostgreSQL and MySQL are both relational database management systems that store data in tables and views and allow users to interact with data using SQL. However, PostgreSQL supports additional features like native NoSQL capabilities, geospatial extensions, and a wider range of programming languages.
- While PostgreSQL and MySQL have similar basic functionality, PostgreSQL has a broader mission beyond the relational model and aims to support multiple data models and a fuller implementation of the SQL standard.
- For projects that may utilize more advanced features over their lifespan, such as NoSQL, geospatial analysis, or custom data types, PostgreSQL may be the better long-term choice compared to MySQL.
Non relational databases-no sql
This document provides an overview of non-relational (NoSQL) databases. It discusses the history and characteristics of NoSQL databases, including that they do not require rigid schemas and can automatically scale across servers. The document also categorizes major types of NoSQL databases, describes some popular NoSQL databases like Dynamo and Cassandra, and discusses benefits and limitations of both SQL and NoSQL databases.
What's hot (20)
Elephants vs. Dolphins: Comparing PostgreSQL and MySQL for use in the DoD
Elephants vs. Dolphins: Comparing PostgreSQL and MySQL for use in the DoD
Similar to iForum 2015: SQL vs. NoSQL
NoSQLDatabases
The document discusses the history and concepts of NoSQL databases. It notes that traditional single-processor relational database management systems (RDBMS) struggled to handle the increasing volume, velocity, variability, and agility of data due to various limitations. This led engineers to explore scaled-out solutions using multiple processors and NoSQL databases, which embrace concepts like horizontal scaling, schema flexibility, and high performance on commodity hardware. Popular NoSQL database models include key-value stores, column-oriented databases, document stores, and graph databases.
How and when to use NoSQL
This document discusses NoSQL databases and when they should be used. It describes what NoSQL databases are, when to consider using one over a relational database, and introduces DynamoDB as an AWS NoSQL solution. Specific topics covered include the differences between relational and NoSQL data models, common use cases for NoSQL databases, and how to access and query DynamoDB tables.
Einführung in RavenDB
Bei all dem Hype rund um NoSQL übersieht man gerne wie viele einsatzbereite Produkte darauf warten unser Entwicklerleben zu vereinfachen. Eines dieser Produkte ist RavenDB, eine dokumentenorientierte Datenbank mit Transaktionsunterstützung. Im Gegensatz zu vielen NoSQL-Lösungen ist RavenDB voll in die .Net-Welt integriert. So kann man seine Abfragen mittels LINQ formulieren und muss nicht auf JavaScript setzen. Die grosse Erfahrung von Oren Eini bei der Optimierung von NHibernate und Entity Framework wurden in RavenDB aufgenommen. So hilft eine frühzeitige Erkennung von Select N+1 Abfragen spätere Probleme in der Produktion zu verhindern. Neben diesen Eigenheiten wird uns Johnny Graber auch zeigen was es alles für Einsatzmöglichkeiten gibt. Die Flexibilität von NoSQL gepaart mit der Einfachheit von RavenDB ist dabei keineswegs nur etwas für grosse Firmen wie Google, Facebook oder Twitter - der nächste Prototyp kann davon genauso gut profitieren.
A peek into the future
This document discusses trends in database technologies and big data. It begins by questioning whether relational databases are always the most efficient way to store data and whether companies like Facebook use traditional databases. It then covers object-relational mapping (ORM), which provides a bridge between object-oriented programming languages and relational databases. The document also discusses NoSQL databases as an alternative for handling large, unstructured "big data" and provides examples like document stores, key-value stores, and graph databases. It concludes by defining big data and discussing the types, analysis, and market size of big data technologies.
NoSQL
The document discusses NoSQL databases and their advantages compared to SQL databases. It defines NoSQL as any database that is not relational and describes the main categories of NoSQL databases - key-value stores, document databases, wide column stores like BigTable, and graph databases. It also covers common use cases for different NoSQL databases and examples of companies using NoSQL technologies like MongoDB, Cassandra, and HBase.
Add Redis to Postgres to Make Your Microservices Go Boom!
Slides for talk delivered at PostgresOpen 2018 in San Francisco https://postgresql.us/events/pgopen2018/schedule/session/538-add-redis-to-postgres-to-make-your-microservice-go-boom/
Transform your DBMS to drive engagement innovation with Big Data
This document discusses how organizations can save money on database management systems (DBMS) by moving from expensive commercial DBMS to more affordable open-source options like PostgreSQL. It notes that PostgreSQL has matured and can now handle mission critical workloads. The document recommends partnering with EnterpriseDB to take advantage of their commercial support and features for PostgreSQL. It highlights how customers have seen cost savings of 35-80% by switching to PostgreSQL and been able to reallocate funds to new business initiatives.
(DAT204) NoSQL? No Worries: Build Scalable Apps on AWS NoSQL Services
In this session, we discuss the benefits of NoSQL databases and take a tour of the main NoSQL services offered by AWS—Amazon DynamoDB and Amazon ElastiCache. Then, we hear from two leading customers, Expedia and Mapbox, about their use cases and architectural challenges, and how they addressed them using AWS NoSQL services, including design patterns and best practices. You will walk out of this session having a better understanding of NoSQL and its powerful capabilities, ready to tackle your database challenges with confidence.
Introduction to NoSQL and MongoDB
This document provides an introduction to NoSQL and MongoDB. It outlines that NoSQL databases are used to manage unstructured data and overcome limitations of relational databases. MongoDB is introduced as a popular document-oriented NoSQL database that stores data as JSON-like documents. Key features of MongoDB include high performance, scalability, rich query language, and automatic replication for high availability.
My sql competitive update
The document summarizes competitive advantages of MySQL compared to commercial and open source databases. It highlights that commercial databases have much higher total cost of ownership, are more complex, and have licensing restrictions. Express databases are found to have technical restrictions that prevent scaling. While other open source databases lack commercial backing and support. The document provides recommendations and questions to consider when evaluating MySQL against other database options.
Why nosql?
This document discusses NoSQL databases and why they are used. It begins by defining NoSQL as "not only SQL" or "not SQL" databases that do not use a relational schema. The need for NoSQL databases arose in the early 2000s due to difficulties scaling MySQL databases for large amounts of data. NoSQL databases are classified based on their data model, consistency, and performance. They offer flexible data models including structured, unstructured, and semi-structured data. NoSQL databases focus on developer agility by being easy to use and integrate with modern frameworks. While they scale well horizontally, they generally lack ACID compliance and transaction support of relational databases.
Startup Bootcamp - Intro to NoSQL/Big Data by DataZone
Arthur Gimpel gave an introduction to Big Data and NoSQL databases. He defined the 3 V's of Big Data as volume, velocity and variety. He explained different NoSQL database types including key-value, graph and document databases. Key-value databases provide fast access but querying values is challenging. Graph databases are useful for relationships between entities. Document databases commonly store JSON and include popular examples like MongoDB. Choosing the right database is important and replacing them is not cheap.
An Enterprise Architect's View of MongoDB
This document discusses how MongoDB can help enterprises meet modern data and application requirements. It outlines the many new technologies and demands placing pressure on enterprises, including big data, mobile, cloud computing, and more. Traditional databases struggle to meet these new demands due to limitations like rigid schemas and difficulty scaling. MongoDB provides capabilities like dynamic schemas, high performance at scale through horizontal scaling, and low total cost of ownership. The document examines how MongoDB has been successfully used by enterprises for use cases like operational data stores and as an enterprise data service to break down silos.
Introduction to no sql database
NoSQL databases provide alternatives to traditional relational databases. They are designed to be highly scalable, flexible, and available. Common types of NoSQL databases include key-value stores, document databases, column-oriented databases, and graph databases. Companies like Google, Amazon, and LiveJournal have successfully used NoSQL databases like BigTable, DynamoDB, and Memcached to handle large volumes of data and improve performance.
Javascript on Server-Side
Node.js and Express
Typescript
NoSQL and MongoDB
Blocking and Non-Blocking
Basic Server with Express
Typescript in detail
SQL vs NoSQL
Mongoose
When to Use MongoDB...and When You Should Not...
MongoDB is well-suited for applications that require:
- A flexible data model to handle diverse and changing data sets
- Strong performance on mixed workloads involving reads, writes, and updates
- Horizontal scalability to grow with increasing user needs and data volume
Some common use cases that leverage MongoDB's strengths include mobile apps, real-time analytics, content management, and IoT applications involving sensor data. However, MongoDB is less suited for tasks requiring full collection scans under load, high write availability, or joins across collections.
Oracle Week 2016 - Modern Data Architecture
This document discusses modern operational data architectures and the use of both relational and NoSQL databases. It provides an overview of relational databases and their ACID properties. While relational databases dominate the market, they have limitations around scalability, flexibility, and performance. NoSQL databases offer alternatives like horizontal scaling and flexible schemas. Key-value stores are best for caching, sessions, and serving data, while document stores are popular for hierarchical and search use cases. Graph databases excel at link analysis. The document advocates a polyglot persistence approach using multiple database types according to their strengths. It provides examples of search architectures using both database-centric and application-centric distribution approaches.
Webinar: The Future of SQL
NoSQL databases like MongoDB, Elasticsearch, and Cassandra are synonymous with scalability, search, and developer agility. But there’s a downside...having to give up the ease and comfort of SQL.
Or do you?
Join this webcast to learn how the newest databases, like CrateDB and CockroachDB deliver the benefits of NoSQL with the ease of SQL by building SQL engines on top of custom NoSQL technology stacks. Database industry veteran Andy Ellicott, who helped launch Vertica, VoltDB, Cloudant, and now with Crate.io, will provide a no-BS view of current DBMS architectures and predictions for the future of data.
If you’re a DBMS user, this webcast will help you make sense of a very crowded DBMS market and make better-informed decisions for your new tech stacks.
introduction to NOSQL Database
This document provides an introduction to NoSQL databases. It discusses the history and limitations of relational databases that led to the development of NoSQL databases. The key motivations for NoSQL databases are that they can handle big data, provide better scalability and flexibility than relational databases. The document describes some core NoSQL concepts like the CAP theorem and different types of NoSQL databases like key-value, columnar, document and graph databases. It also outlines some remaining research challenges in the area of NoSQL databases.
NoSQL for SQL Server Developers using Couchbase
This document provides an overview of NoSQL for SQL Server developers using Couchbase. It introduces Brant Burnett, the presenter, and his credentials working with Couchbase. The content will compare SQL and NoSQL concepts like databases, data types, queries and indexing. It will also provide a live demo of an ASP.NET Core microservice querying and indexing Couchbase. Finally, the presenter will take questions from the audience.
Similar to iForum 2015: SQL vs. NoSQL (20)
Add Redis to Postgres to Make Your Microservices Go Boom!
Add Redis to Postgres to Make Your Microservices Go Boom!
Transform your DBMS to drive engagement innovation with Big Data
Transform your DBMS to drive engagement innovation with Big Data
(DAT204) NoSQL? No Worries: Build Scalable Apps on AWS NoSQL Services
(DAT204) NoSQL? No Worries: Build Scalable Apps on AWS NoSQL Services
Startup Bootcamp - Intro to NoSQL/Big Data by DataZone
Startup Bootcamp - Intro to NoSQL/Big Data by DataZone
More from Денис Резник
SqlSaturday199 - Deadlocks
The document discusses database locks and transaction isolation levels. It begins by defining shared, exclusive, and update locks. It then explains different isolation levels including read uncommitted, read committed, repeatable read, and serializable. Read uncommitted allows dirty reads while read committed does not. Repeatable read prevents non-repeatable reads and serializable prevents phantom records. The document also covers snapshot isolation and how to avoid deadlocks through proper database design and transaction ordering.
SqlSaturday199 - Columnstore Indexes
In-Memory features is the most perspective trend in the area of high performance. Columnstore Indexes is one of such features, and even with their restrictions, they can accelerate your queries at times! How to get more from this feature? In which situations should we use them? Which internal mechanisms help to achive that? You can get answers on these questions on this session.
SQL Server 2012 Deep Dive (rus)
Talk is called Deep Dive, so be prepared to hold your breath. In this talk we will take a look at the mechanisms of the SQL Server and literally dive into the bowels of SQL Server, going through all the stages of the request processing.
Sql rally 2013 columnstore indexes
SQL Server 2012 introduced columnstore indexes which provide significant performance improvements for data warehouse and analytics queries against large datasets. Columnstore indexes store data by column rather than by row, allowing queries to access only the relevant columns needed. This results in lower I/O and higher data compression compared to row storage. Columnstore indexes also use a new batch processing execution mode which can further improve query performance by processing many rows at once in memory rather than row-by-row. Columnstore indexes require the table to be read-only but provide an easy way to boost query performance for analytics workloads by 10-100x without needing separate data marts or cubes.
TechEd 2012 - Сценарии хранения и обработки данных в windows azure
The document provides an agenda for a Windows Azure keynote presentation. It is divided into several sections including an introduction to Windows Azure, creating applications on Windows Azure, solving problems with Windows Azure, Windows Azure internals, storage and data processing scenarios, authentication using Azure ACS, Service Bus for connecting cloud services to on-premises systems, and several lab sessions on topics like Windows Azure Virtual Machines, diagnosing issues, and deploying Active Directory and SharePoint to Windows Azure. Slide details include storage metrics showing over 4 trillion objects stored and average/peak request rates. There are also client logos and case studies presented.
MS Swit 2012 - Windows 8 Application Lifecycle
As your customers move your app between the foreground and background, Windows manages your app’s usage of critical system resources. Come learn the fundamentals of Process Lifetime Management and how to structure your app to suspend and resume quickly, save app state properly, and behave consistently. We will also highlight ways to keep your app fresh using push notifications and tile interactivity. Understanding these critical concepts will help you deliver a continuous experience that customers expect.
MS Swit 2012 - SQL Server 2012
The new realise of one Microsoft flagman product is approaching – SQL Server. New realizes of SQL Server are not so often, that is why every new realise causes a splash of society activities and wide discussions. We already know the content of the new realise and the product is going through the last stages of testing before the launch. Let’s look, what new things wull bring us new SQL Server.
Масштабирование в SQL Azure - SQL Azure Federations
Масштабируемость приложения стала критическим элементом дизайна после перехода от модели индивидуальных программ к серверной модели работы приложений. Масштабируемые приложения способны справиться с более активным использованием и возросшим объёмом данных. Масштабируемые приложения обладают высокой доступностью и отказоустойчивостью. Масштабируемые приложения управляемы и сопровождаемы. Всё это справедливо по отношению ко всем слоям приложения, в том числе и к базе данных. Масштабирование базы данных - процесс отнюдь не тривиальный и довольно сложный. SQL Azure, со своей концепцией моментального создания и распределения баз данных внутри ЦОД, является хорошей альтернативой одиночному серверу, а SQL Azure Federations выводят масштабирование в SQL Azure на совершенно новый уровень.
Масштабирование в SQL Azure - SQL Azure Federations
Масштабируемость приложения стала критическим элементом дизайна после перехода от модели индивидуальных программ к серверной модели работы приложений. Масштабируемые приложения способны справиться с более активным использованием и возросшим объёмом данных. Масштабируемые приложения обладают высокой доступностью и отказоустойчивостью. Масштабируемые приложения управляемы и сопровождаемы. Всё это справедливо по отношению ко всем слоям приложения, в том числе и к базе данных. Масштабирование базы данных - процесс отнюдь не тривиальный и довольно сложный. SQL Azure, со своей концепцией моментального создания и распределения баз данных внутри ЦОД, является хорошей альтернативой одиночному серверу, а SQL Azure Federations выводят масштабирование в SQL Azure на совершенно новый уровень.
масштабирование в Sql azure
This document discusses federations in SQL Azure. Federations allow scaling out data by partitioning it across multiple databases. The document shows an example of a "CustData" federation with members for customer ID ranges that each contain sample data partitions. It also shows an "Orders_Fed" federation partitioned by customer ID that could include related order data spread across multiple databases. In the future, SQL Azure will support federations natively as a way to scale databases beyond a single server.
More from Денис Резник (19)
TechEd 2012 - Сценарии хранения и обработки данных в windows azure
TechEd 2012 - Сценарии хранения и обработки данных в windows azure
Масштабирование в SQL Azure - SQL Azure Federations
Масштабирование в SQL Azure - SQL Azure Federations
Масштабирование в SQL Azure - SQL Azure Federations
Масштабирование в SQL Azure - SQL Azure Federations
SQL Server StreamIinsight - data processing in real time
SQL Server StreamIinsight - data processing in real time
Recently uploaded
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
#AIFusionBuddyUserExperience
#AIFusionBuddyforBeginners,
#AIFusionBuddyBenefits,
#AIFusionBuddyComparison,
#AIFusionBuddyInstallation,
#AIFusionBuddyRefundPolicy,
#AIFusionBuddyDemo,
#AIFusionBuddyMaintenanceFees,
#AIFusionBuddyNewbieFriendly,
#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks
8 Best Automated Android App Testing Tool and Framework in 2024.pdf
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
OpenMetadata Community Meeting - 5th June 2024
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Empowering Growth with Best Software Development Company in Noida - Deuglo
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
Unveiling the Advantages of Agile Software Development.pdf
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
What is Master Data Management by PiLog Group
PiLog Group's Master Data Record Manager (MDRM) is a sophisticated enterprise solution designed to ensure data accuracy, consistency, and governance across various business functions. MDRM integrates advanced data management technologies to cleanse, classify, and standardize master data, thereby enhancing data quality and operational efficiency.
GreenCode-A-VSCode-Plugin--Dario-Jurisic
Presentation about a VSCode plugin from Dario Jurisic at the GSD Community Stage meetup
SMS API Integration in Saudi Arabia| Best SMS API Service
Discover the benefits and implementation of SMS API integration in the UAE and Middle East. This comprehensive guide covers the importance of SMS messaging APIs, the advantages of bulk SMS APIs, and real-world case studies. Learn how CEQUENS, a leader in communication solutions, can help your business enhance customer engagement and streamline operations with innovative CPaaS, reliable SMS APIs, and omnichannel solutions, including WhatsApp Business. Perfect for businesses seeking to optimize their communication strategies in the digital age.
Graspan: A Big Data System for Big Code Analysis
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Automated software refactoring with OpenRewrite and Generative AI.pptx.pdf
https://2024.springio.net/sessions/automated-software-refactoring-with-openrewrite-and-generative-ai/
GraphSummit Paris - The art of the possible with Graph Technology
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian Companies
Explore the seamless transition to e-invoicing with this comprehensive guide tailored for Saudi Arabian businesses. Navigate the process effortlessly with step-by-step instructions designed to streamline implementation and enhance efficiency.
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
LORRAINE ANDREI_LEQUIGAN_HOW TO USE ZOOM
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Transform Your Communication with Cloud-Based IVR Solutions
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
What is Augmented Reality Image Tracking
Augmented Reality (AR) Image Tracking is a technology that enables AR applications to recognize and track images in the real world, overlaying digital content onto them. This enhances the user's interaction with their environment by providing additional information and interactive elements directly tied to physical images.
Microservice Teams - How the cloud changes the way we work
A lot of technical challenges and complexity come with building a cloud-native and distributed architecture. The way we develop backend software has fundamentally changed in the last ten years. Managing a microservices architecture demands a lot of us to ensure observability and operational resiliency. But did you also change the way you run your development teams?
Sven will talk about Atlassian’s journey from a monolith to a multi-tenanted architecture and how it affected the way the engineering teams work. You will learn how we shifted to service ownership, moved to more autonomous teams (and its challenges), and established platform and enablement teams.
Using Query Store in Azure PostgreSQL to Understand Query Performance
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
Recently uploaded (20)
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
8 Best Automated Android App Testing Tool and Framework in 2024.pdf
8 Best Automated Android App Testing Tool and Framework in 2024.pdf
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
Empowering Growth with Best Software Development Company in Noida - Deuglo
Empowering Growth with Best Software Development Company in Noida - Deuglo
Unveiling the Advantages of Agile Software Development.pdf
Unveiling the Advantages of Agile Software Development.pdf
SMS API Integration in Saudi Arabia| Best SMS API Service
SMS API Integration in Saudi Arabia| Best SMS API Service
Automated software refactoring with OpenRewrite and Generative AI.pptx.pdf
Automated software refactoring with OpenRewrite and Generative AI.pptx.pdf
GraphSummit Paris - The art of the possible with Graph Technology
GraphSummit Paris - The art of the possible with Graph Technology
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian Companies
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian Companies
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Transform Your Communication with Cloud-Based IVR Solutions
Transform Your Communication with Cloud-Based IVR Solutions
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
Microservice Teams - How the cloud changes the way we work
Microservice Teams - How the cloud changes the way we work
Using Query Store in Azure PostgreSQL to Understand Query Performance
Using Query Store in Azure PostgreSQL to Understand Query Performance
iForum 2015: SQL vs. NoSQL
- 1. SQL vs NoSQL It’s Always a Hard Choice Denis Reznik Director of R&D at Intapp Kyiv
- 2. Database History 1960s 1970s 1980s 1990s 2000s Nowadays Object Databases RDMS Commercial Success SQL RDBMS Ingress System R E.F. Codd’s Paper CODASYL IMS NoSQL (Johan Oskarsson) NewSQL (?) Google BigTable Paper Amazon Dynamo Paper
- 3. NoSQL SQL
- 4. Agenda • Criteria #1 • Criteria #2 • Criteria #3 • Criteria #4 • Criteria #5 • Criteria #6 • Criteria #7
- 5. About me • Denis Reznik • Director of R&D at Intapp Kyiv • Microsoft MVP (SQL Server) • Leader of Kyiv SQL Server User Group • Database geek • Community Enthusiast 5 |
- 9. Q&A Web Site (StackOverflow)
- 13. Sample Workload • 4 million users • 8 million questions • 40 million answers • As a network #54 site for traffic in the world • 560 million page views a month • Peak is more like 2600-3000 requests/sec on most weekdays. source: https://www.youtube.com/watch?v=t6kM2EM6so4
- 15. Scalability • Scale up • Very easy to scale • Always have a limit • Hardware is expensive • Scale-out • Relatively tricky scaling • Theoretically infinite scale • Can be done on commodity hardware
- 17. Durability ACID • D – for Durability • Not all NoSQL databases are durable (at least by default)
- 18. Cost Criteria #6
- 19. Cost • Hardware • Big servers are expensive • Small servers are cheep • Small servers can be easily replaced • License • A lot of NoSQL databases are free • There are free RDBMS as well • Scale Out is more useful for free software • Support • MSSQL forgives you more than MySQL
- 23. Real Architecture of StackOverflow source: https://www.youtube.com/watch?v=t6kM2EM6so4
- 24. Summary • Tools • Domain • Workload • Scalability • Durability • Cost • Knowledge • Polyglot Persistence
- 25. More Points • “Respect the problem” (c) • Think about the future workload grows • Think about the future application changes • Database itself, even if it is blazing fast, is not a purpose of not to use cache • Consider cloud database offers • Consider Polyglot Persistence • Consider using service for data access in application • Database load test • A/B testing
- 26. Thank you! Denis Reznik Twitter: @denisreznik Email: denisreznik@live.ru Blog: http://reznik.uneta.com.ua Facebook: https://www.facebook.com/denis.reznik.5 LinkedIn: http://ua.linkedin.com/pub/denis-reznik/3/502/234