This document compares associative information systems like Atomic DB to relational and other NoSQL databases for managing big data problems. It discusses how Atomic DB uses a natural, multidimensional model compared to the unnatural 2D model of relational databases. It also argues that while big IT companies promote big data solutions as necessary, customers should carefully consider the total cost of ownership versus the real financial benefits. The document suggests that new database technologies often overpromise and underdeliver, and that NoSQL databases are the latest "puppies" being sold by big IT that will require significant support costs. It questions whether these solutions will truly provide more value than their costs.
AtomicDB addresses several common business challenges that companies face with data management. It aggregates disparate databases into a single source to provide a unified view of business data, and decreases storage needs by 60-90% through eliminating duplicates. AtomicDB provides real-time access and reporting on data, allowing new reports to be instantly created without programming. It aims to solve challenges like long ETL processes, report run times, and IT costs through its novel database approach.
The document discusses a new data modeling architecture called the Atomic Information Resource (AIR) data model, which is the basis of the AtomicDB database management system. The AIR model replaces database tables and records with atomic information resources that are not bound by data structures and know their own context and relationships. It also describes how the model was conceptualized based on earlier patented works and demonstrates how concepts, models, and data can be modeled and stored in AtomicDB without the limitations of traditional table-based approaches. The key advantage is that data sets are not duplicated and the same data can be referenced by multiple concepts.
The document discusses the rise of NoSQL databases as an alternative to traditional relational databases. It provides a brief history of NoSQL, noting that new types of applications and data led developers to look for databases that offer more flexibility and scalability. It also describes the main types of NoSQL databases - key-value stores, graph stores, column stores, and document stores - and discusses some of the advantages of NoSQL databases like flexibility, scalability, availability and lower costs.
This deck talks about the basic overview of NoSQL technologies, implementation vendors/products, case studies, and some of the core implementation algorithms. The presentation also describes a quick overview of "Polyglot Persistency", "NewSQL" like emerging trends.
The deck is targeted to beginners who wants to get an overview of NoSQL databases.
What is NoSQL? How does it come to the picture? What are the types of NoSQL? Some basics of different NoSQL types? Differences between RDBMS and NoSQL. Pros and Cons of NoSQL.
What is MongoDB? What are the features of MongoDB? Nexus architecture of MongoDB. Data model and query model of MongoDB? Various MongoDB data management techniques. Indexing in MongoDB. A working example using MongoDB Java driver on Mac OSX.
The document discusses designing an application to import biological data files into a database table to allow for analysis of large datasets without memory issues, including developing modules to preprocess data files, import data into tables while handling different column orders and splitting data across multiple tables based on column limits, and providing features like undo/redo and standard analysis functions. The application "Database migration and management tool" (DBSERVER) was developed to address these issues and allow researchers to work more comfortably with large biological datasets.
How Semantics Solves Big Data ChallengesDATAVERSITY
Today, organizations want both IT simplicity and innovation, but reliance on traditional databases only leads to more complexity, longer development cycles, and more silos. In fact, organizations report that the #1 impediment to big data success is having too many silos. In this webinar, we will discuss how a new database technology, semantics, solves this problem by providing a new approach to modeling data that focuses on relationships and context, making it easier for data to be understood, searched, and shared. With semantics, world-leading organizations are integrating disparate data faster and easier and building smarter applications with richer analytic capabilities—benefits that we look forward to diving into during the webinar.
The document discusses the ongoing revolution in database technology driven by factors like increasing data volumes, new workloads, and market forces. It provides a history of databases from the pre-relational era to today's relational and post-relational databases. The discussion covers topics around challenges with existing database concepts, the impedance mismatch between databases and applications, and different types of NoSQL databases and database workloads.
AtomicDB addresses several common business challenges that companies face with data management. It aggregates disparate databases into a single source to provide a unified view of business data, and decreases storage needs by 60-90% through eliminating duplicates. AtomicDB provides real-time access and reporting on data, allowing new reports to be instantly created without programming. It aims to solve challenges like long ETL processes, report run times, and IT costs through its novel database approach.
The document discusses a new data modeling architecture called the Atomic Information Resource (AIR) data model, which is the basis of the AtomicDB database management system. The AIR model replaces database tables and records with atomic information resources that are not bound by data structures and know their own context and relationships. It also describes how the model was conceptualized based on earlier patented works and demonstrates how concepts, models, and data can be modeled and stored in AtomicDB without the limitations of traditional table-based approaches. The key advantage is that data sets are not duplicated and the same data can be referenced by multiple concepts.
The document discusses the rise of NoSQL databases as an alternative to traditional relational databases. It provides a brief history of NoSQL, noting that new types of applications and data led developers to look for databases that offer more flexibility and scalability. It also describes the main types of NoSQL databases - key-value stores, graph stores, column stores, and document stores - and discusses some of the advantages of NoSQL databases like flexibility, scalability, availability and lower costs.
This deck talks about the basic overview of NoSQL technologies, implementation vendors/products, case studies, and some of the core implementation algorithms. The presentation also describes a quick overview of "Polyglot Persistency", "NewSQL" like emerging trends.
The deck is targeted to beginners who wants to get an overview of NoSQL databases.
What is NoSQL? How does it come to the picture? What are the types of NoSQL? Some basics of different NoSQL types? Differences between RDBMS and NoSQL. Pros and Cons of NoSQL.
What is MongoDB? What are the features of MongoDB? Nexus architecture of MongoDB. Data model and query model of MongoDB? Various MongoDB data management techniques. Indexing in MongoDB. A working example using MongoDB Java driver on Mac OSX.
The document discusses designing an application to import biological data files into a database table to allow for analysis of large datasets without memory issues, including developing modules to preprocess data files, import data into tables while handling different column orders and splitting data across multiple tables based on column limits, and providing features like undo/redo and standard analysis functions. The application "Database migration and management tool" (DBSERVER) was developed to address these issues and allow researchers to work more comfortably with large biological datasets.
How Semantics Solves Big Data ChallengesDATAVERSITY
Today, organizations want both IT simplicity and innovation, but reliance on traditional databases only leads to more complexity, longer development cycles, and more silos. In fact, organizations report that the #1 impediment to big data success is having too many silos. In this webinar, we will discuss how a new database technology, semantics, solves this problem by providing a new approach to modeling data that focuses on relationships and context, making it easier for data to be understood, searched, and shared. With semantics, world-leading organizations are integrating disparate data faster and easier and building smarter applications with richer analytic capabilities—benefits that we look forward to diving into during the webinar.
The document discusses the ongoing revolution in database technology driven by factors like increasing data volumes, new workloads, and market forces. It provides a history of databases from the pre-relational era to today's relational and post-relational databases. The discussion covers topics around challenges with existing database concepts, the impedance mismatch between databases and applications, and different types of NoSQL databases and database workloads.
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.
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.
Data Bases - Introduction to data scienceFrank Kienle
Lecture: Introduction to Data Science
given 2017 at Technical University of Kaiserslautern, Germany
Lecturer: Frank Kienle, Head of AI and Data Science, Camelot ITLab
Topic: introduction to data bases
This document provides an overview of NoSQL databases and MongoDB. It states that NoSQL databases are more scalable and flexible than relational databases. MongoDB is described as a cross-platform, document-oriented database that provides high performance, high availability, and easy scalability. MongoDB uses collections and documents to store data in a flexible, JSON-like format.
The document discusses MapReduce and the Hadoop framework. It provides an overview of how MapReduce works, examples of problems it can solve, and how Hadoop implements MapReduce at scale across large clusters in a fault-tolerant manner using the HDFS distributed file system and YARN resource management.
This document provides an overview of SQL and NoSQL databases. It discusses how relational databases using SQL emerged as the dominant data storage approach but faced challenges in scaling to big data workloads. NoSQL databases were developed to address these scaling needs by using non-relational data models like key-value, document, and column-oriented structures that are better suited to distributed architectures. The document outlines the history and characteristics of SQL and relational databases and how NoSQL databases address needs like scalability that drove their emergence in the big data era.
The METL process extracts meta data from a source system, transforms it to describe a different database structure, and loads it into a target system. This allows data to be accessed across systems with different structures without changing the source. Specifically, it extracts meta data from a legacy banking system, transforms it to work with a business intelligence tool, and loads it so the tool can query the legacy data through an SQL interface without knowing the source's structure. The METL process facilitates data sharing across different systems in a non-invasive way to prolong legacy systems' lives and provide access to production data.
This document provides information about big data and its characteristics. It discusses the different types of data that comprise big data, including structured, semi-structured, and unstructured data. It also addresses some of the challenges of big data, such as its increasing volume and the need to process it in real-time for applications like online promotions and healthcare monitoring. Traditional data warehouse architectures may not be well-suited for big data applications.
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 document discusses data representation in C# and ADO.NET. It begins by explaining that C# objects are similar to Java objects but with properties instead of getter/setter methods. It then covers how to create a class with properties in C# and use objects. The document also discusses encapsulation in ADO.NET and how it handles connecting to databases. It provides steps for connecting to a database, creating a data adapter and dataset, binding controls to display data, and adding code to populate the dataset and allow navigation between records.
Jboss Teiid is a data virtualization and federation system that provides a uniform API for accessing data. It allows for data from different sources like SQL and NoSQL databases, unstructured data, and web services to be virtually integrated. Teiid extracts metadata from multiple data sources through virtual databases (VDBs), enabling federation. The consumer API is simple JDBC usage. Teiid is fully integrated with Jboss and customizable for performance and extensible through custom binders.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable, and distributed processing of large data sets across commodity hardware. The core of Hadoop consists of HDFS for storage and MapReduce for processing data in parallel on multiple nodes. The Hadoop ecosystem includes additional projects that extend the functionality of the core components.
Introduction to database with ms access.hetvii07HetviBhagat
A database is usually controlled by a database management system (DBMS). MS Access is a popular DBMS that allows users to create and manage databases. The document discusses various components of a database such as tables, queries, forms and reports. It provides information on how to create an MS Access database, add tables, enter data, create relationships between tables, write queries to extract data, and build forms and reports. The key aspects covered are data modeling using entity relationship diagrams, normalizing data to reduce redundancy, and performing common database operations like importing, exporting and analyzing data in MS Access.
Object relational and extended relational databasesSuhad Jihad
This document discusses object-relational and extended relational databases. It begins with an introduction and agenda. It then covers database design for ORDBMS, including complex data types, structured types, type inheritance, and array/multiset types. It discusses creating and querying collection-valued attributes. Finally, it covers nesting and unnesting relations to transform between normalized and denormalized forms. The key topics covered in 3 sentences or less are: database design for ORDBMS supports objects, classes, and inheritance; structured types allow user-defined complex attributes; type inheritance and subtables allow modeling specialization hierarchies; and arrays and multisets allow modeling ordered and unordered collections as attributes.
Gopi has over 3 years of experience implementing data warehousing projects with Teradata. He has a B.Tech in Electrical and Electronics Engineering from Prakasam Engineering College. His skills include loading data into Teradata from flat files using FastLoad scripts and working with Teradata utilities like BTEQ, Fast Load, Multi Load, and Tpump. He has worked on two projects - a financial data reporting system for Black hawk Network and a customer enterprise data warehouse for Verizon UK, where he was responsible for ETL development, scripting, query optimization, and more.
This document provides an overview of NoSQL databases. It discusses that NoSQL databases offer more flexibility, higher performance, scalability, and choices compared to relational databases. The four main types of NoSQL databases are column family stores, key-value stores, document stores, and graph stores. Each has their own advantages and disadvantages for storing and querying data.
The document summarizes Aginity's efforts to build a 10 terabyte database application using $5,682.10 worth of commodity hardware. They constructed a 9-box server farm with off-the-shelf components to test leading database systems like MapReduce, in-database analytics, and MPP on a scale that previously would have cost $2.2 million. The goal was to build similar big data capabilities on a smaller budget for their research lab to experiment with different technologies.
On Friday, September 25th Devin Hopps lead us through a presentation on an Introduction to Big Data and how technology has evolved to harness the power of Big Data.
Big Data is a Big Scam Most of the Time! (MySQL Connect Keynote 2012)Daniel Austin
This document summarizes a keynote address on big data myths. It discusses that big data refers to problems of large volumes and high rates of change, and NoSQL is one proposed solution but not synonymous with big data. It also discusses that the CAP theorem is more about tradeoffs between consistency and availability. Finally, it introduces the YESQL project which aims to build a globally distributed SQL database that does not fail, lose data, or sacrifice consistency while supporting transactions and scaling linearly.
Big Data Presentation - Data Center Dynamics Sydney 2014 - Dez BlanchfieldDez Blanchfield
The document discusses the rise of big data and its impact on data centers. It defines what big data is and what it is not, providing examples of big data sources and uses. It also explores how the concept of a data center is evolving, as they must adapt to support new big data workloads. Traditional data center designs are no longer sufficient and distributed, modular, and software-defined approaches are needed to efficiently manage large and growing volumes of data.
DevOps for Data Engineers - Automate Your Data Science Pipeline with Ansible,...Mihai Criveti
- The document discusses automating data science pipelines with DevOps tools like Ansible, Packer, and Kubernetes.
- It covers obtaining data, exploring and modeling data, and how to automate infrastructure setup and deployment with tools like Packer to build machine images and Ansible for configuration management.
- The rise of DevOps and its cultural aspects are discussed as well as how tools like Packer, Ansible, Kubernetes can help automate infrastructure and deploy machine learning models at scale in production environments.
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.
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.
Data Bases - Introduction to data scienceFrank Kienle
Lecture: Introduction to Data Science
given 2017 at Technical University of Kaiserslautern, Germany
Lecturer: Frank Kienle, Head of AI and Data Science, Camelot ITLab
Topic: introduction to data bases
This document provides an overview of NoSQL databases and MongoDB. It states that NoSQL databases are more scalable and flexible than relational databases. MongoDB is described as a cross-platform, document-oriented database that provides high performance, high availability, and easy scalability. MongoDB uses collections and documents to store data in a flexible, JSON-like format.
The document discusses MapReduce and the Hadoop framework. It provides an overview of how MapReduce works, examples of problems it can solve, and how Hadoop implements MapReduce at scale across large clusters in a fault-tolerant manner using the HDFS distributed file system and YARN resource management.
This document provides an overview of SQL and NoSQL databases. It discusses how relational databases using SQL emerged as the dominant data storage approach but faced challenges in scaling to big data workloads. NoSQL databases were developed to address these scaling needs by using non-relational data models like key-value, document, and column-oriented structures that are better suited to distributed architectures. The document outlines the history and characteristics of SQL and relational databases and how NoSQL databases address needs like scalability that drove their emergence in the big data era.
The METL process extracts meta data from a source system, transforms it to describe a different database structure, and loads it into a target system. This allows data to be accessed across systems with different structures without changing the source. Specifically, it extracts meta data from a legacy banking system, transforms it to work with a business intelligence tool, and loads it so the tool can query the legacy data through an SQL interface without knowing the source's structure. The METL process facilitates data sharing across different systems in a non-invasive way to prolong legacy systems' lives and provide access to production data.
This document provides information about big data and its characteristics. It discusses the different types of data that comprise big data, including structured, semi-structured, and unstructured data. It also addresses some of the challenges of big data, such as its increasing volume and the need to process it in real-time for applications like online promotions and healthcare monitoring. Traditional data warehouse architectures may not be well-suited for big data applications.
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 document discusses data representation in C# and ADO.NET. It begins by explaining that C# objects are similar to Java objects but with properties instead of getter/setter methods. It then covers how to create a class with properties in C# and use objects. The document also discusses encapsulation in ADO.NET and how it handles connecting to databases. It provides steps for connecting to a database, creating a data adapter and dataset, binding controls to display data, and adding code to populate the dataset and allow navigation between records.
Jboss Teiid is a data virtualization and federation system that provides a uniform API for accessing data. It allows for data from different sources like SQL and NoSQL databases, unstructured data, and web services to be virtually integrated. Teiid extracts metadata from multiple data sources through virtual databases (VDBs), enabling federation. The consumer API is simple JDBC usage. Teiid is fully integrated with Jboss and customizable for performance and extensible through custom binders.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable, and distributed processing of large data sets across commodity hardware. The core of Hadoop consists of HDFS for storage and MapReduce for processing data in parallel on multiple nodes. The Hadoop ecosystem includes additional projects that extend the functionality of the core components.
Introduction to database with ms access.hetvii07HetviBhagat
A database is usually controlled by a database management system (DBMS). MS Access is a popular DBMS that allows users to create and manage databases. The document discusses various components of a database such as tables, queries, forms and reports. It provides information on how to create an MS Access database, add tables, enter data, create relationships between tables, write queries to extract data, and build forms and reports. The key aspects covered are data modeling using entity relationship diagrams, normalizing data to reduce redundancy, and performing common database operations like importing, exporting and analyzing data in MS Access.
Object relational and extended relational databasesSuhad Jihad
This document discusses object-relational and extended relational databases. It begins with an introduction and agenda. It then covers database design for ORDBMS, including complex data types, structured types, type inheritance, and array/multiset types. It discusses creating and querying collection-valued attributes. Finally, it covers nesting and unnesting relations to transform between normalized and denormalized forms. The key topics covered in 3 sentences or less are: database design for ORDBMS supports objects, classes, and inheritance; structured types allow user-defined complex attributes; type inheritance and subtables allow modeling specialization hierarchies; and arrays and multisets allow modeling ordered and unordered collections as attributes.
Gopi has over 3 years of experience implementing data warehousing projects with Teradata. He has a B.Tech in Electrical and Electronics Engineering from Prakasam Engineering College. His skills include loading data into Teradata from flat files using FastLoad scripts and working with Teradata utilities like BTEQ, Fast Load, Multi Load, and Tpump. He has worked on two projects - a financial data reporting system for Black hawk Network and a customer enterprise data warehouse for Verizon UK, where he was responsible for ETL development, scripting, query optimization, and more.
This document provides an overview of NoSQL databases. It discusses that NoSQL databases offer more flexibility, higher performance, scalability, and choices compared to relational databases. The four main types of NoSQL databases are column family stores, key-value stores, document stores, and graph stores. Each has their own advantages and disadvantages for storing and querying data.
The document summarizes Aginity's efforts to build a 10 terabyte database application using $5,682.10 worth of commodity hardware. They constructed a 9-box server farm with off-the-shelf components to test leading database systems like MapReduce, in-database analytics, and MPP on a scale that previously would have cost $2.2 million. The goal was to build similar big data capabilities on a smaller budget for their research lab to experiment with different technologies.
On Friday, September 25th Devin Hopps lead us through a presentation on an Introduction to Big Data and how technology has evolved to harness the power of Big Data.
Big Data is a Big Scam Most of the Time! (MySQL Connect Keynote 2012)Daniel Austin
This document summarizes a keynote address on big data myths. It discusses that big data refers to problems of large volumes and high rates of change, and NoSQL is one proposed solution but not synonymous with big data. It also discusses that the CAP theorem is more about tradeoffs between consistency and availability. Finally, it introduces the YESQL project which aims to build a globally distributed SQL database that does not fail, lose data, or sacrifice consistency while supporting transactions and scaling linearly.
Big Data Presentation - Data Center Dynamics Sydney 2014 - Dez BlanchfieldDez Blanchfield
The document discusses the rise of big data and its impact on data centers. It defines what big data is and what it is not, providing examples of big data sources and uses. It also explores how the concept of a data center is evolving, as they must adapt to support new big data workloads. Traditional data center designs are no longer sufficient and distributed, modular, and software-defined approaches are needed to efficiently manage large and growing volumes of data.
DevOps for Data Engineers - Automate Your Data Science Pipeline with Ansible,...Mihai Criveti
- The document discusses automating data science pipelines with DevOps tools like Ansible, Packer, and Kubernetes.
- It covers obtaining data, exploring and modeling data, and how to automate infrastructure setup and deployment with tools like Packer to build machine images and Ansible for configuration management.
- The rise of DevOps and its cultural aspects are discussed as well as how tools like Packer, Ansible, Kubernetes can help automate infrastructure and deploy machine learning models at scale in production environments.
Debunking "Purpose-Built Data Systems:": Enter the Universal DatabaseStavros Papadopoulos
Purpose-built databases and platforms have actually created more complexity, effort, and unnecessary reinvention. The status quo is a big mess. TileDB took the opposite approach.
In this presentation, Stavros, the original creator of TileDB, shared the underlying principles of the TileDB universal database built on multi-dimensional arrays, making the case for it as a true first in the data management industry.
A look back at how the practice of data science has evolved over the years, modern trends, and where it might be headed in the future. Starting from before anyone had the title "data scientist" on their resume, to the dawn of the cloud and big data, and the new tools and companies trying to push the state of the art forward. Finally, some wild speculation on where data science might be headed.
Presentation given to Seattle Data Science Meetup on Friday July 24th 2015.
Big Data Analytics: Finding diamonds in the rough with AzureChristos Charmatzis
In this session it will presented main workflows and technologies of getting value from Big Data stored in our Enterprise using Azure.
- When we have a Big Data problem
- Finding the best solution for our Big Data
- Working inside the Data Team
- Extract the true value of our data.
Data Engineer's Lunch #85: Designing a Modern Data StackAnant Corporation
What are the design considerations that go into architecting a modern data warehouse? This presentation will cover some of the requirements analysis, design decisions, and execution challenges of building a modern data lake/data warehouse.
Big Data brings big promise and also big challenges, the primary and most important one being the ability to deliver Value to business stakeholders who are not data scientists!
Introduction to Big Data An analogy between Sugar Cane & Big DataJean-Marc Desvaux
Big data is large and complex data that exceeds the processing capacity of conventional database systems. It is characterized by high volume, velocity, and variety of data. An enterprise can leverage big data through an analytical use to gain new insights, or through enabling new data-driven products and services. An analogy compares an enterprise's big data architecture to a sugar cane factory that acquires, organizes, analyzes, and generates business intelligence from big data sources to create value for the organization. NoSQL databases are complementary to rather than replacements for relational databases in big data solutions.
This document provides an introduction to big data, including definitions and key concepts. It discusses the evolution of computing systems and data storage. Big data is defined as large and complex data sets that are difficult to process using traditional methods due to the volume, variety, velocity, and veracity of the data. Examples of big data sources and applications are provided. Finally, different approaches for analyzing big data are described, including MapReduce, Hadoop, real-time analytics using databases, and complex event processing.
Data massage: How databases have been scaled from one to one million nodesUlf Wendel
A workshop from the PHP Summit 2013, Berlin.
Join me on a journey to scaling databases from one to one million nodes. The adventure begins in the 1960th and ends with Google Spanner details from a Google engineer's talk given as late as November 25th, 2013!
Contents: Relational systems and caching (briefly), what CAP means, Overlay networks, Distributed Hash Tables (Chord), Amazon Dynamo, Riak 2.0 including CRDT, BigTable (Distributed File System, Distributed Locking Service), HBase (Hive, Presto, Impala, ...), Google Spanner and how their unique TrueTime API enables ACID, what CAP really means to ACID transactions (and the NoSQL marketing fuzz), the latest impact of NoSQL on the RDBMS world. There're quite a bit of theory in the talk, but that's how things go when you walk between Distributed Systems Theory and Theory of Parallel and Distributed Databases, such as.... Two-Phase Commit, Two-Phase Locking, Virtual Synchrony, Atomic Broadcast, FLP Impossibility Theorem, Paxos, Co-Location and data models...
Making Sense of NoSQL and Big Data Amidst High ExpectationsRackspace
1) There is a lot of hype around NoSQL and Big Data technologies but they provide value for specific problems involving large, varied datasets with high rates of change.
2) NoSQL databases are useful for problems that don't require a relational data model and involve huge datasets, while SQL databases remain critical for transaction processing and maintaining relationships between structured data.
3) Organizations should choose technologies based on their specific business requirements and understand each technology's strengths rather than favoring "cool" technologies.
Architecting for Big Data: Trends, Tips, and Deployment OptionsCaserta
Joe Caserta, President at Caserta Concepts addressed the challenges of Business Intelligence in the Big Data world at the Third Annual Great Lakes BI Summit in Detroit, MI on Thursday, March 26. His talk "Architecting for Big Data: Trends, Tips and Deployment Options," focused on how to supplement your data warehousing and business intelligence environments with big data technologies.
For more information on this presentation or the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Introductory Big Data presentation given during one of our Sizing Servers Lab user group meetings. The presentation is targeted towards an audience of about 20 SME employees. It also contains a short description of the work packages for our BIg Data project proposal that was submitted in March.
The document discusses modern big data trends and technologies. It covers topics like the role of data engineers, architectures like data mesh and lambda architectures, technologies like SQL, Apache Spark, and serverless computing, maturity of data governance and platforms, and innovations in areas like AI-driven analytics and data lake houses. The target audience is managers and engineers to provide an outlook on the latest developments in big data.
This document provides an overview of big data, including its definition, size and growth, characteristics, analytics uses and challenges. It discusses operational vs analytical big data systems and technologies like NoSQL databases, Hadoop and MapReduce. Considerations for selecting big data technologies include whether they support online vs offline use cases, licensing models, community support, developer appeal, and enabling agility.
Similar to Virtue desk atomic-db vs relational vs everything (20)
How to Manage Reception Report in Odoo 17Celine George
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
2. A Brief Comparison of Associative
Information Systems with other
NoSQL solutions for Managing Big
Data Problems
Introduc*on
h:p://www.virtue-‐desk.com
Wednesday, August 28, 13
3. The
NEW
WORLD
The
“FLAT”
Rela*onal
DB
World
VS.
the
“ROUND”
Associa*ve
World
2
Dimensional
–
Un-‐Natural (N)
Dimensional
-‐
Natural
Wednesday, August 28, 13
4. (The
BIG
LIE)
Big
IT
says
you
need
Big
Data
solu*ons
to
help
you
find
value
hidden
in
your
data.
The
most
important
ques*on
to
ask
is
about
the
Total
Cost
of
Ownership,
(including
all
the
design,
consul*ng,
set-‐up,
development,
implementa*on,
evolu*on
and
maintenance
services)
vs.
the
Real
($)
Benefit
to
be
a:ained.
“Will
it
Deliver
more
$
value
to
my
organiza*on
than
it
will
Cost
me?”
If
you
don’t
get
a
guarantee,
(or
your
money
cheerfully
refunded),
or
at
least
an
answer,
perhaps
you
shouldn’t
buy
in.
Wednesday, August 28, 13
5. Atomic
DB vs NoSQL
Big
Data?
Big
Issues?
Big
Bucks!!!
Once
upon
a
*me,
customers
were
complaining
about
not
ge]ng
enough
value
for
their
money
spent
on
IT.
Sure
they
needed
it
to
run
their
business,
but
any
good
business
man
will
eventually
ask
“Where
is
my
return
on
this
investment?”
Apparently
Big
IT
listened.
The
Big
Systems
they’d
delivered
weren’t
performing
up
to
spec.
Too
much
data,
too
fast,
too
complex,
So
...
Big
Deal
to
the
rescue!
When
the
customer
is
unhappy,
confuse
them
with
a
vast
array
of
new
stuff,
for
which
they
have
no
in-‐house
exper*se
and
promise
them
the
mythical
keys
to
that
hidden
treasure
chest
of
magical
insight,
concealed
by
circumstance
in
the
many
haystacks
of
data,
just
wai*ng
to
be
found
by
complicated
new
technology,
filled
to
the
brim
with
the
latest
buzz
words.
Wednesday, August 28, 13
6. Atomic
DB vs NoSQL
Big
Promises?
Big
Projects?
Big
Disappointments
!!!
Just
like
Big
Promises
of
the
past,
Knowledge
Management,
Business
Intelligence,
Data
Warehouses,
Data
Fusion,
System
Federa*on,
Y2K,
Asset
Management,
and
every
expensive
genera*on
of
Big
IT
Systems
ever
produced,
those
promises
of
“EVERYTHING
You
Need
and
Want”
in
the
next
completely
new
and
be:er
collec*on
of
Buzz
Word
filled
products
has
always
been
a
Big
IT
sales
strategy.
Unfortunately
the
Big
Promises
did
not
and
do
not
get
delivered
!!!!
Every
new
technology
always
comes
like
a
puppy,
wrapped-‐up
in
some
irresis*ble
features,
but
laden
with
a
life*me
of
care,
feeding,
training,
cleanup
and
support.
Big
IT
always
stands
to
gain
billions
with
each
new
wave
of
puppies.
Customers
each
stand
to
lose
millions
with
each
Big
Failure
“Big
Data”
is
the
new
Big
Buzz
word.
And
NoSQL
systems
are
the
new
puppies.
And
Customers
are
once
again
being
‘encouraged’
to
Buy-‐in.
Wednesday, August 28, 13
7. Atomic
DB vs NoSQL
Big
Problems?
Big
Decisions?
Big
Responsibility
!!!
So
get
ready
for
the
next
Big
Wave
of
Big
IT
hype
and
promo*on:
You’re
problems
are
Big,
so
Big,
so
count
on
the
Big
Experts,
who
now
have
a
new
game:
“Free
Soiware!”,
(open
source)
to
accompany
their
license-‐laden
Enterprise
systems,
all
requiring
extensive
Big
IT
services
and
support
in
order
to
make
everything
work
together,
…
eventually,
…
we
hope
...
Since
the
exis*ng
RDBMS-‐based
Enterprise
systems
are
performance-‐shy,
and
hold
only
a
subset
of
the
Big
Data
required
to
drive
the
modern
organiza*on,
new
and
be:er
Big
Data
solu*ons
are
required
to
augment
those
expensive
silos
and
get
results
be:er
and
faster
than
they
ever
could
deliver
as
stand-‐alone
monuments
to
inefficiency.
A
new
breed
of
data
warehouse
has
hit
town
and
it
looks
like
the
next
Big
Thing.
Now
every
manager
is
being
condi*oned
to
think
in
terms
of
Big
Data,
and
see
NoSQL
as
the
wonder-‐filled
solu*on
to
the
problems
of
running
a
business
in
the
digital
age
of
Informa*on
Overload.
Now
if
only
it
would
work
as
promised…
And
not
cost
a
fortune.
So,
What
to
Choose?
There’s
so
many
op*ons…
Wednesday, August 28, 13
8. Atomic
DB vs NoSQL
Difference
1
Complexity
of
Querying
Wednesday, August 28, 13
9. • 100,000
organiza*ons
globally
• 1,000,000
databases
• 10,000,000
tables
• 100,000,000
queries
SQL
/NoSQL
–
let’s
suppose
All the databases in the world All the tables, triple, KV and document stores in the world
All the companies in the world
1,000,000 10,000,000
All the queries in the world
100,000,000
•Assuming
only
100,000,000
queries
globally,
(one
can
es*mate
many
more),
and
‘x’
hours
per
query,
that’s
a
lot
of
person
hours
•Each
query
can
work
only
with
the
table(s)
it
was
designed
for
•Every
database
is
incompa*ble
with
every
other
database
•For
each
and
every
query,
a
database
specialist
needs
to
write
it.
100,000
Wednesday, August 28, 13
10. Atomic
DB
• Each
Atomic
DB
Query
is
compa*ble
with
every
Atomic
DB
Informa*on
store
• Every
Item
in
a
Atomic
DB
Informa*on
store
can
reference
and
be
referenced
by
any
Item
in
its
own
and
any
other
Atomic
DB
Informa*on
store
• Mul*-‐store
mapping
is
an
inherent
capability
of
every
Atomic
DB
system
• No
IT
professionals
required
to
query
any
Atomic
DB
Informa*on
store
All the organizations in the world 100,000 of significance
All the Associative systems in the world
All the Atomic DB queries in the world 5 universal queries, generic to all data sets
Only 1 Atomic DB system required per organization
Wednesday, August 28, 13
11. Atomic
DB vs NoSQL
Difference
2
Complexity
of
Implementa*on
Wednesday, August 28, 13
12. NO-
Number
of
disparate
tools,
systems
and
exper*se
needed
to
set-‐
up
and
operate:
NoSQL
requires:
Schema
Layouts,
Spec
Produc*on,
RDF
Specialists,
Special
Data
Stores,
DB
Administrators
and
other
DB
specific
specialists,
SQL,
OWL,
&
SPARQL
programmers,
Ontology
and
Taxonomy
Specialists,
Extrac*on
Tools,
Data
Scien*sts,
ETL,
Data
Modelers,
Integra*on
Tools,
Migra*on
Tools,
Data
Cleansing
Tools,
Modeling
Tools,
Object,
Class
and
Hierarchy
(UML)
Managers,
Data
Universe
Builders,
Open
Source
system
managers,
version
control,
migra*on
and
release
managers,
installa*on
specialists,
applica*on
specialists,
and
MORE…
Wednesday, August 28, 13
13. ATOMIC
Number
of
disparate
tools,
systems
and
exper*se
needed
to
set-‐
up
and
operate:
Atomic
DB
requires:
IAMCore
ManageIT
Business
Analyst
Customer
Wednesday, August 28, 13
14. Atomic
DB vs. NoSQL
Difference
3
Capacity
for
Complexity
Wednesday, August 28, 13
15. NoSQL
• K-V Stores … Amazon Dynamo, …
• Column-oriented … Google Big Table, Hadoop, …
• Document DB … Mark Logic, Mongo DB, …
• Graph DB … Neo4J, Titan, …
• RDBMS … SQL Server, MySQL, …
All available ‘Big Data’ solutions are Name-Space and storage structure bound.
Only graph databases can handle high complexity of relationships in the data because
they are open (often indexed) triple stores but all contextualization has to be handled at
run-time and extracted / derived from the data.
Relational systems can handle moderate complexity but need many columns and many
tables with FK links abounding to represent even a moderate degree of complexity.
The other ‘Big Data’ solutions are extremely limited in the complexity they handle. They
usually are dedicated to a single purpose or application.
Wednesday, August 28, 13
16. ATOMIC
Relavance
Associa*ve
Informa*on
Systems
have
no
Name-‐Space
or
storage
structure
binding;
each
data
element
is
just
an
a:ribute
of
its
Token-‐Space
iden*ty.
Relavance
Associa*ve
Informa*on
Systems
are
mul*-‐Dimensional,
mul*-‐data
informa*on
stores,
designed
from
incep*on
to
manage
rela*onship
complexity
of
any
degree.
Its
storage
model
is
a
4-‐D
128
bit
vector
space.
There
are
no
restric*ve
limita*ons
on
the
number
of
associa*ve
dimensions
or
levels.
Each
system
can
scale
to
reference
(super-‐index)
/
hold
(aggregate)
1018
items,
each
with
‘n’
rela*onships
in
any
of
‘m’
rela*onship
dimensions.
All
data
elements
and
their
rela*onships
are
fully
contextualized
upon
inges*on
so
that
everything
is
always
grouped
and
reference-‐able
in
as
many
ways
as
there
are
contexts.
Wednesday, August 28, 13
18. Atomic
DB vs. NoSQL
Difference
4
Cost
of
Implementa*on
Wednesday, August 28, 13
19. Atomic
DB vs. NoSQL
Moderately Complex ‘Big Data’ System implementation involving
multi-data (RDBMS, Structured and Unstructured Text) requires:
Days to Weeks
Small Team of:
Business Analysts
UI Specialists
One technology base
Months to Years
Large team(s) of:
Technology and Domain Experts,
Implementation Specialists, Project
Managers, Component Specialists,
UI Specialists, Consultants…
Many technologies and components
Wednesday, August 28, 13
20. Atomic
DB vs. NoSQL
Difference
5
•
Maintenance
•
Support
and
•
System
Evolu*on
Requirements
Wednesday, August 28, 13
21. Atomic
DB vs. NoSQL
Moderately Complex ‘Big Data’ System maintenance, support and evolution:
1 administrator,
Small Team of:
Business Analysts
Hours to Days:
Requirements Gathering,
Map and Add new Data Sets,
Add new Workflow models.
UI Adaptation and Validation.
System stays up and usable.
Many administrators and experts,
Large team(s) of:
Technology and Domain Experts
Weeks to Months:
Requirements Gathering, Planning, Data
Extraction, Specification Production,
Implementation Project Management, Regression
testing, Validation, Deployment, Training, Change
Management, …
Version Migration downtime.
System Evolution to meet New Requirements
Maintenance and Support
Wednesday, August 28, 13
22. THE
“UPGRADE”
CYCLE
“$”
Oracle
Microsoi
IBM
DB2
Atomic-‐DB “Because
we
are
ATOMIC
in
Nature..
There
is
no
Upgrade
Cycle...”
Wednesday, August 28, 13
23. *
Cost
of
custom
research
service
depends
on
project
scope
Development
Comparison
Cost
Comparison Rela*onal
(SQL)
Associa*ve
Schema
Development
/
Database
Design X X
Schema
Mapping
/Table
Layout
/
Query
development X
Data
Integra*on
and
Development X X
Applica*on
Class
Libraries X X
Data
Encapsula*on X
Materialized
Views X
Performance
Organiza*on X
Table
Segmenta*on X
Meta-‐Data
Management X
Referen*al
Integrity
Checks X
Query
Evolu*on X
Configura*on
Management x
Applica*on
User
Interface
Development X X
Wednesday, August 28, 13
24. Atomic
DB vs. NoSQL
Difference
6
Our
API
Wednesday, August 28, 13
26. Atomic
DB vs. NoSQL
Difference
7
Our
Capacity
Wednesday, August 28, 13
27. • An
exabyte
is
1018
or
1,000,000,000,000,000,000
bytes.
• One
exabyte
(abbreviated
"EB")
is
equal
to
1,000
petabytes
and
precedes
the
ze:abyte
unit
of
measurement
• The
exabyte
unit
of
measure
measurement
is
so
large,
it
is
not
used
to
measure
the
capacity
of
data
storage
devices.
Even
the
storage
capacity
of
the
largest
cloud
storage
centers
is
measured
in
petabytes,
which
is
a
frac*on
of
one
exabyte.
Instead,
Exabytes
are
used
to
measure
the
sum
of
mul*ple
storage
networks
or
the
amount
of
data
transferred
over
the
Internet
in
a
certain
amount
of
*me.
For
example,
several
hundred
Exabytes
of
data
are
transferred
over
the
Internet
Associa*ve
Capacity
Reference
1
gigabyte
1
terabyte
1
Petabyte
1
Exabyte
When
we
consider
the
Environment
&
System
Actual
capacity
is
1036
Wednesday, August 28, 13
28. INTRODUCING
ATOMIC-‐DB
The
only
Completely
“Associa*ve”
Database
in
the
World…
Wednesday, August 28, 13
29. Atomic
DB vs. NoSQL
Difference
8
Our
Business
Advantages
Wednesday, August 28, 13
30. • Summarizing
our
technology
is
a
complex
task
as
we
are
discussing
a
PARDIGM
shii
in
the
way
data
is
both
Stored
and
Retrieved.
• A
few
Key
points
• 100X
faster
than
SQL
on
READS
-‐
CASE
SENSATIVE(if
required)
• 10X
faster
on
WRITES
-‐
LITTLE
or
NO
SUPPORT
STAFF
• 1/3
the
DISK
SPACE
usage
-‐
OBJECT
ORIENTED
DESIGN
• NO
QUERIES
to
WRITE
-‐
80%
reduc*on
in
DEVELOPMENT
TIME.
• NO
TABLES
-‐
50-‐75%
reduc*on
is
Development
costs
• NO
INDEXES
-‐
only
6
INSTRUCTIONS
in
the
API
• NO
VIEWS
-‐
one
line
of
code
access
to
your
data
• NO
WHITESPACE
-‐
Associate
Anything
to
Anything
• NO
DUPLICATES
-‐
DOD
verified
Security
Model
• 1
to
100+
concurrent
SOURCES
of
disparate
DATA
(ORACLE,
MSSQL,
MSSQL,
ACCESS,
DB2,EXCEL,
Flat
FILES(csv)
)
Key
Benefits
of
Atomic
DB
Wednesday, August 28, 13
31. Atomic
DB vs. NoSQL
Difference
9
Our
Performance
Advantages
Wednesday, August 28, 13
32. SYSTEM
:
(1)
4
CORE
INTEL
processor
,
4GB
RAM,
(1)
5400
RPM
500GB
Drive
Here
are
some
calculaMons
to
set
the
stage:
A
record
with
50
columns
of
data
represents
2500
triples,
if
you
include
both
direcMons,
(which
we
do).
Because
every
possible
associaMve
path
is
maintained,
discovery
of
all
associaMons
is
implicit
from
every
data
point.
We
assimilate
1
million
records
of
50
columns
of
data
in
typically
<
30
minutes
(best
case
10
minutes,
avg
20
minutes)
That's
the
equivalent
of
1,000,000
*
2500
triples
or
2.5
billion
triples
in
30
minutes,
worst
case
performance.
2.5
billion
triples
in
1800
seconds
(30
minutes
*
60
seconds
per
minute),
is
1.389
million
triples
per
second.
Because
of
the
proprietary
way
we
reference
and
store
informaMon
as
composite
mulM-‐dimensional
informaMon
atoms,
we
are
able
to
produce
the
funcMonal
equivalent
of
2.5
billion
triples
in
less
than
30
minutes,
operaMng
with
a
sustained
throughput
of
30,000
composite
'atomic'
transacMons
per
second
(world
record
=
18,000)
Since
we
don't
store
the
triples
as
triples,
yet
maintain
the
equivalent
'associaMve'
capability
triples
have,
we
can
get
a
huge
assimilaMon
performance
equivalent
benefit
over
triple
stores,
with
a
be`er,
faster
and
more
efficient
retrieval
and
storage.
Some
Metrics
Let’s
set
the
Stage www.tpc.org
Wednesday, August 28, 13
33. MORE
ATOMIC-‐DB
“Unlike
other
systems
where
a
Structure
is
built
to
STORE
data,
here
the
“Data”
is
the
Structure….
“
Wednesday, August 28, 13
36. • Jean
Michel
LeTennier
jm@virtue-‐desk.com
– 917-‐751-‐3131
• James
Murphy
james@virtue-‐desk.com
– 646-‐408-‐4385
• Andre
De
Castro
andre@virtue-‐desk.com
– 917-‐548-‐9810
– h:p://www.virtue-‐desk.com
Contact
Informa*on
Wednesday, August 28, 13