Some NoSQL

This document discusses NoSQL data modeling and provides examples of different data modeling approaches for non-relational databases, including document, columnar, graph, and relational models. It covers topics like the role of data modeling, different data domains, schema approaches, normalization vs denormalization, embedding data, and using multiple data models or a "polyglot persistence" approach. Examples are given of one-to-one, one-to-many, and many-to-many relationships and how they can be modeled in a document database.

This event will start with a presentation on “Extensible database APIs and their role in software architecture”, centered around JavaScript. This will be followed by a hands-on interactive workshop. Participants with their own computers will learn how to create a small web application with a database backend, within the session, using only JavaScript. This will be a guided hands-on session using the multi-model NoSQL database ArangoDB and its Foxx JavaScript extension framework. Presenting this workshop will be Max Neunhöffer from https://www.arangodb.com/.

The document provides an overview of NoSQL data modeling concepts and different NoSQL database types including document databases, column-oriented databases, key-value stores, and graph databases. It discusses data modeling approaches for each type and compares databases like MongoDB and CouchDB. The document also covers topics like CAP theorem, eventual consistency, and distributed system techniques from Dynamo.

The document discusses different NoSQL data models including key-value, document, column family, and graph models. It provides examples of popular NoSQL databases that implement each model such as Redis, MongoDB, Cassandra, and Neo4j. The document argues that these NoSQL databases address limitations of relational databases in supporting modern web applications with requirements for scalability, flexibility, and high performance.

Triple stores are finally seeing mainstream use, but what exactly is all this talk about linked data? In this deck, we discuss what the semantic web is and how to map your relational data sets into a triple store database using open source software.

This presentation goes over the advantages of JSON for encoding and some new use cases as a SQL data type.

Linked data platforms are now making it easier than ever to perform data exploration and discovery without having to wait to get the data integrated into the data warehouse. In this presentation, we discuss what linked data is and show a case study on integrating separate source systems so that scientists don't have to learn the source systems structures to get to their data.

This document discusses the need for observability in data pipelines. It notes that real data pipelines often fail or take a long time to rerun without providing any insight into what went wrong. This is because of frequent code, data, dependency, and infrastructure changes. The document recommends taking a production engineering approach to observability using metrics, logging, and alerting tools. It also suggests experiment management and encapsulating reporting in notebooks. Most importantly, it stresses measuring everything through metrics at all stages of data ingestion and processing to better understand where issues occur.

All Things Open 2016 Talk - discussing technologies used to augment traditional data warehousing. Those technologies are: * data vault * anchor modeling * linked data * NoSQL * data virtualization * textual disambiguation

Underscore.js is a JavaScript library for manipulating data and JSON objects. It allows users to narrow down large datasets, sort and group data, and derive new data from existing data. Underscore can be used with other libraries like jQuery and provides powerful features like templating for formatting data. It is open source and can be downloaded from http://underscorejs.org in different versions.

Modeling data in a relational database is easy, we all know how to do it because that's what we've always been taught; But what about NoSQL Document Databases? Document databases take (much) of what you know and flip it upside down. This talk covers some common patterns for modeling data and how to approach things when working with document stores such as Azure DocumentDB

The document discusses different types of databases, including document-oriented, embedded, graph, hypertext, operational, distributed, and flat-file databases. It provides brief descriptions of each type of database, such as that document-oriented databases are designed for storing, retrieving, and managing document data, graph databases use graph structures to represent and store data, and operational databases store detailed organizational operations data. It also includes contact information for an online coding course provider.

The document discusses XML schemas. It explains that an XML schema describes the structure of an XML document and is an alternative to DTDs. It is written in XML and supports data types and namespaces. The document provides examples of simple XML schemas defining elements and attributes, and using restrictions to define acceptable values for elements and attributes.

Intro database talk given to the spokane chapter of build guild. Covers the differences and use cases for sql to nosql database systems, as well as some core concepts such as ACID and CAP.

The document presents an overview of XML and its usage and relationship to databases. It discusses how XML is a markup language used to transport, store, and share data. While XML is useful for small to medium amounts of data, it lacks features of relational databases and is less suitable for large data. The document also covers XML characteristics like being tag-based and user-defined, as well as how XML schema and validation are used.

Welcome to the world of NoSQL. NoSQL market is now expected to reach 4.2 billion dollar business in itself by 2020. If you are still confused by what does this term means then you are not ready for the Big Data world. However, just knowing the term is not enough. Due to the enormous numbers of No SQL platforms out there, one of the key challenges is not how to use them but when to use what. In this webinar session, we will start with a small description of the NoSQL and try to understand why it was introduced after all. Then we will look into the four different types of NoSQL frameworks and some tips on how to choose what. Key Takeaways: 1. Understanding NoSQL 2. SQL to NoSQL: Why the Need is There 3. The Four Main Types of NoSQL 4. How to Make the Best Choice 5. NoSQL User Stories & Deployment of Best Practices

This document provides an overview of NoSQL databases, including why they were created, common characteristics, and classifications. It discusses key concepts like the CAP theorem, BASE vs ACID properties, and gives examples like Cassandra. Cassandra is a distributed, horizontally scalable database designed for high availability. It uses consistent hashing to distribute data and is very fast for writes. The document concludes with tradeoffs between SQL and NoSQL databases and when each may be preferable.

The document provides an overview of NoSQL and MongoDB. It discusses that NoSQL databases were built for large datasets and cloud applications. It covers some of the main types of NoSQL databases like document stores, key-value stores, and column family stores. The document also compares NoSQL to SQL/relational databases, discussing how NoSQL is more flexible and scales horizontally. MongoDB is presented as a popular document-oriented NoSQL database, covering its flexible schema, horizontal scaling, and replication features.

The document provides an overview of NoSQL databases and MongoDB. It discusses: - What NoSQL is and why it was created - The different categories of NoSQL databases, including key-value stores, document databases, column family stores, and graph databases - MongoDB specifically, including its flexible schema, horizontal scalability, replication support, and data modeling approach - Comparisons between relational and NoSQL databases

This document provides an overview of NoSQL databases, including why they are used, common types, and how they work. The key points are: 1) SQL databases do not scale well for large amounts of distributed data, while NoSQL databases are designed for horizontal scaling across servers and partitions. 2) Common types of NoSQL databases include document, key-value, graph, and wide-column stores, each with different data models and query approaches. 3) NoSQL databases sacrifice consistency guarantees and complex queries for horizontal scalability and high availability. Eventual consistency is common, with different consistency models for different use cases.

This document discusses NoSQL databases and how they provide alternatives to SQL databases for managing large datasets. It notes that NoSQL databases are designed for high performance, unlimited scalability, and high availability even on unreliable hardware. The document outlines several types of NoSQL databases, including key-value stores, document stores, and BigTable clones. It argues that NoSQL databases are better suited than SQL databases for applications requiring flexible schemas, large volumes of data, or high write volumes.

This document provides an overview of NoSQL databases. It discusses that NoSQL databases are non-relational and were created to overcome limitations of scaling relational databases. The document categorizes NoSQL databases into key-value stores, document databases, graph databases, XML databases, and distributed peer stores. It provides examples like MongoDB, Redis, CouchDB, and Cassandra. The document also explains concepts like CAP theorem, ACID properties, and reasons for using NoSQL databases like horizontal scaling, schema flexibility, and handling large amounts of data.

this presentation about the ORM methodology and how is works and how to select an ORM for your project

DBA's used to be Relational Database centric for instance managing Microsoft SQL Server or Oracle, in this changing world of polyglot database environments their role has expanded not just into new platforms other than SQL but also new legal governance, modelling techniques, architecture etc. They need to have a base knowledge of Kimball, Inmon, Data Vault, what CAP theorem is, LAMBDA, Big Data, Data Science etc.

NoSql is an approach to database design that enables the storage and querying of data outside the traditional structures found in relational databases

There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.

When things can't be done in such a way that a small change to the db causes many changes and recompilation of your app is this really agile? Do you really believe it or had sworn to it? I use my own technique to minimize or completety eliminate this common problem. Come to see what you might never heard of.

This document provides an overview of relational databases and the emergence of alternative database technologies like NoSQL. It discusses the dominance and stability of relational databases but also some of their limitations for certain use cases. It introduces NoSQL databases and why they emerged, focusing on their scalability and flexibility compared to relational databases. The document describes different types of NoSQL databases and how they handle concepts like schemas, transactions and scaling. It provides examples of when different database types may be more suitable and discusses additional concepts like aggregates, consistency models and sharding.

Introduction Presentation about NoSQL Agenda: - Why NoSQL - What is NoSQL - Distribution Models - The CAP Theorem - NoSQL Types - NoSQL or Relational or Both - Demo!

Domain Driven Design is a software development process that focuses on finding a common language for the involved parties. This language and the resulting models are taken from the domain rather than the technical details of the implementation. The goal is to improve the communication between customers, developers and all other involved groups. Even if Eric Evan's book about this topic was written almost ten years ago, this topic remains important because a lot of projects fail for communication reasons. Relational databases have their own language and influence the design of software into a direction further away from the Domain: Entities have to be created for the sole purpose of adhering to best practices of relational database. Two kinds of NoSQL databases are changing that: Document stores and graph databases. In a document store you can model a "contains" relation in a more natural way and thereby express if this entity can exist outside of its surrounding entity. A graph database allows you to model relationships between entities in a straight forward way that can be expressed in the language of the domain. In this talk I want to look at the way a multi model database that combines a document store and a graph database can help you to model your problems in a way that is understandable for all parties involved, and explain the benefits of this approach for the software development process.

في الفيديو ده بيتم شرح ما هي المشاكل التي انتجت ظهور هذا النوع من قواعد البيانات انواع المشاريع التي يمكن استخدامها بها نبذة عن تاريخها و مزاياها و عيوبها https://youtu.be/I9zgrdCf0fY

NoSQL databases should not be chosen just because a system is slow or to replace RDBMS. The appropriate choice depends on factors like the nature of the data, how the data scales, and whether ACID properties are needed. NoSQL databases are categorized by data model (document, column family, graph, key-value store) which affects querying. Other considerations include scalability based on the CAP theorem and operational factors like the distribution model and whether there is a single point of failure. The best choice depends on the specific requirements and risks losing data if chosen incorrectly.

The document summarizes the history and evolution of non-relational databases, known as NoSQL databases. It discusses early database systems like MUMPS and IMS, the development of the relational model in the 1970s, and more recent NoSQL databases developed by companies like Google, Amazon, Facebook to handle large, dynamic datasets across many servers. Pioneering systems like Google's Bigtable and Amazon's Dynamo used techniques like distributed indexing, versioning, and eventual consistency that influenced many open-source NoSQL databases today.

The four categories of NoSQL databases When to Use NoSQL When NOT to use NoSQL Use cases NoSQL (Each Category)

Enterprise NoSQL Silver bullet or poison pill? discusses the pros and cons of NoSQL databases compared to SQL databases. While SQL databases will remain prevalent, NoSQL databases offer alternative data storage options with different tradeoffs. NoSQL systems typically relax constraints of SQL like schema rigidity in exchange for implementation flexibility, but this comes at the cost of features like joins and global indexes. NoSQL also shifts the system of record away from a single database, requiring applications to handle consistency and creating multiple copies of data to scale.

This document provides an overview of key topics from BTM 382 Database Management including: - The structure and content of the course including chapters on data models, database design, programming, and management. - Descriptions of the relational, entity-relationship, object-oriented, and NoSQL data models and how they have evolved over time. - An explanation of how Big Data challenges are addressed through NoSQL databases which sacrifice consistency for speed. - Guidance on which data model to use based on factors like data complexity, performance needs, and organizational objectives.

This document provides an overview of different data models discussed in chapters 2, 12, and 14 of a database management course. It describes the evolution of data models from hierarchical and network models to the relational model and entity-relationship model. The document also discusses the object-oriented data model, big data challenges, and how NoSQL databases help address those challenges. Key tradeoffs between consistency and speed are explained in the context of CAP theorem and ACID versus BASE properties. The document concludes with guidance on selecting an appropriate data model based on data structure complexity and performance needs.

Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality. Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality. Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality. Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank? ** Episode Overview ** In this first episode of our quality series, Kristen Hansen and the panel discuss: ⦿ What do we mean when we say patent quality? ⦿ Why is patent quality important? ⦿ How to balance quality and budget ⦿ The importance of searching, continuations, and draftsperson domain expertise ⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications https://www.aurorapatents.com/patently-strategic-podcast.html

Password Rotation in 2024 is still Relevant

Invited Remote Lecture to SC21 The International Conference for High Performance Computing, Networking, Storage, and Analysis St. Louis, Missouri November 18, 2021

An invited talk given by Mark Billinghurst on Research Directions for Cross Reality Interfaces. This was given on July 2nd 2024 as part of the 2024 Summer School on Cross Reality in Hagenberg, Austria (July 1st - 7th)

Everything that I found interesting last month about the irresponsible use of machine intelligence

Your comprehensive guide to RPA in healthcare for 2024. Explore the benefits, use cases, and emerging trends of robotic process automation. Understand the challenges and prepare for the future of healthcare automation

As a popular open-source library for analytics engineering, dbt is often used in combination with Airflow. Orchestrating and executing dbt models as DAGs ensures an additional layer of control over tasks, observability, and provides a reliable, scalable environment to run dbt models. This webinar will cover a step-by-step guide to Cosmos, an open source package from Astronomer that helps you easily run your dbt Core projects as Airflow DAGs and Task Groups, all with just a few lines of code. We’ll walk through: - Standard ways of running dbt (and when to utilize other methods) - How Cosmos can be used to run and visualize your dbt projects in Airflow - Common challenges and how to address them, including performance, dependency conflicts, and more - How running dbt projects in Airflow helps with cost optimization Webinar given on 9 July 2024

Jindong Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He, Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, Philip Torr "A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models" arXiv2023 https://arxiv.org/abs/2307.12980

Kief Morris rethinks the infrastructure code delivery lifecycle, advocating for a shift towards composable infrastructure systems. We should shift to designing around deployable components rather than code modules, use more useful levels of abstraction, and drive design and deployment from applications rather than bottom-up, monolithic architecture and delivery.

Have you noticed the OpenSSF Scorecard badges on the official Dart and Flutter repos? It's Google's way of showing that they care about security. Practices such as pinning dependencies, branch protection, required reviews, continuous integration tests etc. are measured to provide a score and accompanying badge. You can do the same for your projects, and this presentation will show you how, with an emphasis on the unique challenges that come up when working with Dart and Flutter. The session will provide a walkthrough of the steps involved in securing a first repository, and then what it takes to repeat that process across an organization with multiple repos. It will also look at the ongoing maintenance involved once scorecards have been implemented, and how aspects of that maintenance can be better automated to minimize toil.

Widya Salim and Victor Ma will outline the causal impact analysis, framework, and key learnings used to quantify the impact of reducing Twitter's network latency.

Today’s digitally connected world presents a wide range of security challenges for enterprises. Insider security threats are particularly noteworthy because they have the potential to cause significant harm. Unlike external threats, insider risks originate from within the company, making them more subtle and challenging to identify. This blog aims to provide a comprehensive understanding of insider security threats, including their types, examples, effects, and mitigation techniques.

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Cybersecurity is a major concern in today's connected digital world. Threats to organizations are constantly evolving and have the potential to compromise sensitive information, disrupt operations, and lead to significant financial losses. Traditional cybersecurity techniques often fall short against modern attackers. Therefore, advanced techniques for cyber security analysis and anomaly detection are essential for protecting digital assets. This blog explores these cutting-edge methods, providing a comprehensive overview of their application and importance.

Stream processing is a crucial component of modern data infrastructure, but constructing an efficient and scalable stream processing system can be challenging. Decoupling compute and storage architecture has emerged as an effective solution to these challenges, but it can introduce high latency issues, especially when dealing with complex continuous queries that necessitate managing extra-large internal states. In this talk, we focus on addressing the high latency issues associated with S3 storage in stream processing systems that employ a decoupled compute and storage architecture. We delve into the root causes of latency in this context and explore various techniques to minimize the impact of S3 latency on stream processing performance. Our proposed approach is to implement a tiered storage mechanism that leverages a blend of high-performance and low-cost storage tiers to reduce data movement between the compute and storage layers while maintaining efficient processing. Throughout the talk, we will present experimental results that demonstrate the effectiveness of our approach in mitigating the impact of S3 latency on stream processing. By the end of the talk, attendees will have gained insights into how to optimize their stream processing systems for reduced latency and improved cost-efficiency.

Revolutionize your transportation processes with our cutting-edge RPA software. Automate repetitive tasks, reduce costs, and enhance efficiency in the logistics sector with our advanced solutions.

This is a powerpoint that features Microsoft Teams Devices and everything that is new including updates to its software and devices for May 2024