This is the fourth webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will introduce you to the aggregation framework.
Back to Basics Webinar 3: Schema Design Thinking in DocumentsMongoDB
This is the third webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will explain the architecture of document databases.
Webinar: Back to Basics: Thinking in DocumentsMongoDB
New applications, users and inputs demand new types of data, like unstructured, semi-structured and polymorphic data. Adopting MongoDB means adopting to a new, document-based data model.
While most developers have internalized the rules of thumb for designing schemas for relational databases, these rules don't apply to MongoDB. Documents can represent rich data structures, providing lots of viable alternatives to the standard, normalized, relational model. In addition, MongoDB has several unique features, such as atomic updates and indexed array keys, that greatly influence the kinds of schemas that make sense.
In this session, Buzz Moschetti explores how you can take advantage of MongoDB's document model to build modern applications.
Development time is wasted as the bulk of the work shifts from adding business features to struggling with the RDBMS. MongoDB, the leading NoSQL database, offers a flexible and scalable solution.
Back to Basics Webinar 5: Introduction to the Aggregation FrameworkMongoDB
This is the fifth webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will introduce you to the aggregation framework.
Beyond the Basics 2: Aggregation Framework MongoDB
The aggregation framework is one of the most powerful analytical tools available with MongoDB.
Learn how to create a pipeline of operations that can reshape and transform your data and apply a range of analytics functions and calculations to produce summary results across a data set.
Back to Basics: My First MongoDB ApplicationMongoDB
This Back to Basics webinar series will introduce you to NoSQL and the MongoDB database. You will find out what MongoDB is, why you would use it, and what you would use it for.
Back to Basics, webinar 2: La tua prima applicazione MongoDBMongoDB
Questo è il secondo webinar della serie Back to Basics che ti offrirà un'introduzione al database MongoDB. In questo webinar ti dimostreremo come creare un'applicazione base per il blogging in MongoDB.
Back to Basics Webinar 3: Schema Design Thinking in DocumentsMongoDB
This is the third webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will explain the architecture of document databases.
Webinar: Back to Basics: Thinking in DocumentsMongoDB
New applications, users and inputs demand new types of data, like unstructured, semi-structured and polymorphic data. Adopting MongoDB means adopting to a new, document-based data model.
While most developers have internalized the rules of thumb for designing schemas for relational databases, these rules don't apply to MongoDB. Documents can represent rich data structures, providing lots of viable alternatives to the standard, normalized, relational model. In addition, MongoDB has several unique features, such as atomic updates and indexed array keys, that greatly influence the kinds of schemas that make sense.
In this session, Buzz Moschetti explores how you can take advantage of MongoDB's document model to build modern applications.
Development time is wasted as the bulk of the work shifts from adding business features to struggling with the RDBMS. MongoDB, the leading NoSQL database, offers a flexible and scalable solution.
Back to Basics Webinar 5: Introduction to the Aggregation FrameworkMongoDB
This is the fifth webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will introduce you to the aggregation framework.
Beyond the Basics 2: Aggregation Framework MongoDB
The aggregation framework is one of the most powerful analytical tools available with MongoDB.
Learn how to create a pipeline of operations that can reshape and transform your data and apply a range of analytics functions and calculations to produce summary results across a data set.
Back to Basics: My First MongoDB ApplicationMongoDB
This Back to Basics webinar series will introduce you to NoSQL and the MongoDB database. You will find out what MongoDB is, why you would use it, and what you would use it for.
Back to Basics, webinar 2: La tua prima applicazione MongoDBMongoDB
Questo è il secondo webinar della serie Back to Basics che ti offrirà un'introduzione al database MongoDB. In questo webinar ti dimostreremo come creare un'applicazione base per il blogging in MongoDB.
Webinar: Getting Started with MongoDB - Back to BasicsMongoDB
Part one an Introduction to MongoDB. Learn how easy it is to start building applications with MongoDB. This session covers key features and functionality of MongoDB and sets out the course of building an application.
Webinaire 2 de la série « Retour aux fondamentaux » : Votre première applicat...MongoDB
Il s'agit du deuxième webinaire de la série « Retour aux fondamentaux » qui a pour but de vous présenter la base de données MongoDB. Dans ce webinaire, nous vous expliquerons comment créer une application de création de blogs dans MongoDB.
MongoDB’s basic unit of storage is a document. Documents can represent rich, schema-free data structures, meaning that we have several viable alternatives to the normalized, relational model. In this talk, we’ll discuss the tradeoff of various data modeling strategies in MongoDB.
Conceptos básicos. Seminario web 5: Introducción a Aggregation FrameworkMongoDB
Este es el quinto seminario web de la serie Conceptos básicos, en la que se realiza una introducción a la base de datos MongoDB. En este seminario web, se analizan los aspectos básicos de Aggregation Framework.
Webinar: Building Your First App with MongoDB and JavaMongoDB
This webinar will walk you through building a simple Java-based application in MongoDB. We’ll cover the basics of MongoDB’s document model, query language, aggregation framework, and deployment architecture.
In this webinar, you will discover:
- How easy it is to start building Java applications with MongoDB
- Key features for manipulating and accessing data
- High availability and scale-out architecture
- WriteConcerns and ReadPreference
Conceptos básicos. Seminario web 4: Indexación avanzada, índices de texto y g...MongoDB
Este es el cuarto seminario web de la serie Conceptos básicos, en la que se realiza una introducción a la base de datos MongoDB. Este seminario se ve en la compatibilidad con índices de texto libre y geoespaciales.
Building a Scalable Inbox System with MongoDB and Javaantoinegirbal
Many user-facing applications present some kind of news feed/inbox system. You can think of Facebook, Twitter, or Gmail as different types of inboxes where the user can see data of interest, sorted by time, popularity, or other parameter. A scalable inbox is a difficult problem to solve: for millions of users, varied data from many sources must be sorted and presented within milliseconds. Different strategies can be used: scatter-gather, fan-out writes, and so on. This session presents an actual application developed by 10gen in Java, using MongoDB. This application is open source and is intended to show the reference implementation of several strategies to tackle this common challenge. The presentation also introduces many MongoDB concepts.
Webinar: Working with Graph Data in MongoDBMongoDB
With the release of MongoDB 3.4, the number of applications that can take advantage of MongoDB has expanded. In this session we will look at using MongoDB for representing graphs and how graph relationships can be modeled in MongoDB.
We will also look at a new aggregation operation that we recently implemented for graph traversal and computing transitive closure. We will include an overview of the new operator and provide examples of how you can exploit this new feature in your MongoDB applications.
This presentation was given at the LDS Tech SORT Conference 2011 in Salt Lake City. The slides are quite comprehensive covering many topics on MongoDB. Rather than a traditional presentation, this was presented as more of a Q & A session. Topics covered include. Introduction to MongoDB, Use Cases, Schema design, High availability (replication) and Horizontal Scaling (sharding).
NoSQL databases only unfold their entire strength when also embracing the their concepts regarding usage and schema design. These slides give some overview of features and concepts of MongoDB.
Back to Basics Webinar 3 - Thinking in DocumentsJoe Drumgoole
Working with a document database requires that you "rewire" your brain. In this talk we discuss denormalisation, object embedding and the use of multiple collections.
Back to Basics Webinar 6: Production DeploymentMongoDB
This is the final webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will guide you through production deployment.
Webinar: Getting Started with MongoDB - Back to BasicsMongoDB
Part one an Introduction to MongoDB. Learn how easy it is to start building applications with MongoDB. This session covers key features and functionality of MongoDB and sets out the course of building an application.
Webinaire 2 de la série « Retour aux fondamentaux » : Votre première applicat...MongoDB
Il s'agit du deuxième webinaire de la série « Retour aux fondamentaux » qui a pour but de vous présenter la base de données MongoDB. Dans ce webinaire, nous vous expliquerons comment créer une application de création de blogs dans MongoDB.
MongoDB’s basic unit of storage is a document. Documents can represent rich, schema-free data structures, meaning that we have several viable alternatives to the normalized, relational model. In this talk, we’ll discuss the tradeoff of various data modeling strategies in MongoDB.
Conceptos básicos. Seminario web 5: Introducción a Aggregation FrameworkMongoDB
Este es el quinto seminario web de la serie Conceptos básicos, en la que se realiza una introducción a la base de datos MongoDB. En este seminario web, se analizan los aspectos básicos de Aggregation Framework.
Webinar: Building Your First App with MongoDB and JavaMongoDB
This webinar will walk you through building a simple Java-based application in MongoDB. We’ll cover the basics of MongoDB’s document model, query language, aggregation framework, and deployment architecture.
In this webinar, you will discover:
- How easy it is to start building Java applications with MongoDB
- Key features for manipulating and accessing data
- High availability and scale-out architecture
- WriteConcerns and ReadPreference
Conceptos básicos. Seminario web 4: Indexación avanzada, índices de texto y g...MongoDB
Este es el cuarto seminario web de la serie Conceptos básicos, en la que se realiza una introducción a la base de datos MongoDB. Este seminario se ve en la compatibilidad con índices de texto libre y geoespaciales.
Building a Scalable Inbox System with MongoDB and Javaantoinegirbal
Many user-facing applications present some kind of news feed/inbox system. You can think of Facebook, Twitter, or Gmail as different types of inboxes where the user can see data of interest, sorted by time, popularity, or other parameter. A scalable inbox is a difficult problem to solve: for millions of users, varied data from many sources must be sorted and presented within milliseconds. Different strategies can be used: scatter-gather, fan-out writes, and so on. This session presents an actual application developed by 10gen in Java, using MongoDB. This application is open source and is intended to show the reference implementation of several strategies to tackle this common challenge. The presentation also introduces many MongoDB concepts.
Webinar: Working with Graph Data in MongoDBMongoDB
With the release of MongoDB 3.4, the number of applications that can take advantage of MongoDB has expanded. In this session we will look at using MongoDB for representing graphs and how graph relationships can be modeled in MongoDB.
We will also look at a new aggregation operation that we recently implemented for graph traversal and computing transitive closure. We will include an overview of the new operator and provide examples of how you can exploit this new feature in your MongoDB applications.
This presentation was given at the LDS Tech SORT Conference 2011 in Salt Lake City. The slides are quite comprehensive covering many topics on MongoDB. Rather than a traditional presentation, this was presented as more of a Q & A session. Topics covered include. Introduction to MongoDB, Use Cases, Schema design, High availability (replication) and Horizontal Scaling (sharding).
NoSQL databases only unfold their entire strength when also embracing the their concepts regarding usage and schema design. These slides give some overview of features and concepts of MongoDB.
Back to Basics Webinar 3 - Thinking in DocumentsJoe Drumgoole
Working with a document database requires that you "rewire" your brain. In this talk we discuss denormalisation, object embedding and the use of multiple collections.
Back to Basics Webinar 6: Production DeploymentMongoDB
This is the final webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will guide you through production deployment.
The storage engine is responsible for managing how data is stored, both in memory and on disk. MongoDB supports multiple storage engines, as different engines perform better for specific workloads.
View this presentation to understand:
What a storage engine is
How to pick a storage engine
How to configure a storage engine and a replica set
Back to Basics Webinar 1: Introduction to NoSQLMongoDB
This is the first webinar of a Back to Basics series that will introduce you to the MongoDB database, what it is, why you would use it, and what you would use it for.
Back to Basics, webinar 4: Indicizzazione avanzata, indici testuali e geospaz...MongoDB
Questo è il quarto webinar della serie Back to Basics che ti offrirà un'introduzione al database MongoDB. Questo webinar guarda supporto all'indice full-text e il supporto geospaziale.
This tutorial will introduce the features of MongoDB by building a simple location-based application using MongoDB. The tutorial will cover the basics of MongoDB’s document model, query language, map-reduce framework and deployment architecture.
The tutorial will be divided into 5 sections:
Data modeling with MongoDB: documents, collections and databases
Querying your data: simple queries, geospatial queries, and text-searching
Writes and updates: using MongoDB’s atomic update modifiers
Trending and analytics: Using mapreduce and MongoDB’s aggregation framework
Deploying the sample application
Besides the knowledge to start building their own applications with MongoDB, attendees will finish the session with a working application they use to check into locations around Portland from any HTML5 enabled phone!
TUTORIAL PREREQUISITES
Each attendee should have a running version of MongoDB. Preferably the latest unstable release 2.1.x, but any install after 2.0 should be fine. You can dowload MongoDB at http://www.mongodb.org/downloads.
Instructions for installing MongoDB are at http://docs.mongodb.org/manual/installation/.
Additionally we will be building an app in Ruby. Ruby 1.9.3+ is required for this. The current latest version of ruby is 1.9.3-p194.
For windows download the http://rubyinstaller.org/
For OSX download http://unfiniti.com/software/mac/jewelrybox/
For linux most users should know how to for their own distributions.
We will be using the following GEMs and they MUST BE installed ahead of time so you can be ahead of the game and safe in the event that the Internet isn’t accommodating.
bson (1.6.4)
bson_ext (1.6.4)
haml (3.1.4)
mongo (1.6.4)
rack (1.4.1)
rack-protection (1.2.0)
rack shotgun (0.9)
sinatra (1.3.2)
tilt (1.3.3)
Prior ruby experience isn’t required for this. We will NOT be using rails for this app.
Speaker: Daniel Coupal
At this point, you may be familiar with the design of MongoDB databases and collections – but what are the frequent patterns you may have to model?
This presentation will add knowledge of how to represent common relationships (1-1, 1-N, N-N) in MongoDB. Going further than relationships, this presentation identifies a set of common patterns, in a similar way to what the Gang of Four did for Object Oriented Design. Finally, this presentation will guide you through the steps of modeling those patterns in MongoDB collections.
In this session, you will learn about:
How to create the appropriate MongoDB collections for some of the patterns discussed.
Differences in relationships vs. the relational database world, and how those differences translate to MongoDB collections.
Common patterns in developing applications with MongoDB, plus a specific vocabulary with which to refer to them.
Webinar: General Technical Overview of MongoDB for Dev TeamsMongoDB
In this talk we will focus on several of the reasons why developers have come to love the richness, flexibility, and ease of use that MongoDB provides. First we will give a brief introduction of MongoDB, comparing and contrasting it to the traditional relational database. Next, we’ll give an overview of the APIs and tools that are part of the MongoDB ecosystem. Then we’ll look at how MongoDB CRUD (Create, Read, Update, Delete) operations work, and also explore query, update, and projection operators. Finally, we will discuss MongoDB indexes and look at some examples of how indexes are used.
As your data grows, the need to establish proper indexes becomes critical to performance. MongoDB supports a wide range of indexing options to enable fast querying of your data, but what are the right strategies for your application?
In this talk we’ll cover how indexing works, the various indexing options, and use cases where each can be useful. We'll dive into common pitfalls using real-world examples to ensure that you're ready for scale.
OSDC 2012 | Building a first application on MongoDB by Ross LawleyNETWAYS
MongoDB – from "humongous" – is an open source, non-relational, document-oriented database. Trading off a few traditional features of databases (notably joins and transactions) in order to achieve much better performance, MongoDB is fast, scalable, and designed for web development. The goal of the MongoDB project is to bridge the gap between key-value stores (which are fast and highly scalable) and traditional RDBMS systems (which provide rich queries and deep functionality).
This talk will introduce the features of MongoDB by walking through how one can building a simple location-based application using MongoDB. The talk will cover the basics of MongoDB's document model, query language, map-reduce framework and deployment architecture.
In this lecture we analyze document oriented databases. In particular we consider why there are the first approach to nosql and what are the main features. Then, we analyze as example MongoDB. We consider the data model, CRUD operations, write concerns, scaling (replication and sharding).
Finally we presents other document oriented database and when to use or not document oriented databases.
Getting Started with Geospatial Data in MongoDBMongoDB
MongoDB supports geospatial data and specialized indexes that make building location-aware applications easy and scalable.
In this session, you will learn the fundamentals of working with geospatial data in MongoDB. We will explore how to store and index geospatial data and best practices for using geospatial query operators and methods. By the end of this session, you should be able to implement basic geolocation functionality in an application.
In this webinar, you will learn:
- Getting geospatial data into MongoDB and how to build geospatial indexes.
- The fundamentals of MongoDB's geospatial query operators and how to design queries that meet the needs of your application.
- Advanced geospatial capabilities with Java geospatial libraries and MongoDB.
This talk will introduce the philosophy and features of the open source, NoSQL MongoDB. We’ll discuss the benefits of the document-based data model that MongoDB offers by walking through how one can build a simple app to store books. We’ll cover inserting, updating, and querying the database of books.
The agenda of the slides are to discuss some basic and in-depth details of MongoDB and NoSQL.
A snapshot of the topics discussed:
- Introduction to NoSQL and MongoDB
- Installation
- Queries
- Indexing
- Schema modeling
- Aggregation
This tutorial is an introduction to MongoDB and NoSQL. The tutorial includes an introduction to MongoDb and NoSQL, installation, queries related to MongoDB and NoSQL, aggregation framework, indexing of MongoDB and NoSQL and schema modelling. The tutorial begins with a section on introduction. This section includes an introduction to NoSQL, its data models like document model, graph model, key value etc. It also includes an introduction to MongoDB and its data model.
The introduction section is then followed by the installation section. This section includes installing MongoDB, default directory, starting MongoDB server, starting Mongo shell and more steps. It also includes adding documents. The next section is about queries related to MongoDB and NoSQL. This section includes query collection which are selecting all documents, find by example, use OR condition, use AND condition, update query. It also includes removing documents.
Then comes a section about aggregation framework. This section includes a brief about aggregation framework process and its samples. The next section is about indexing. This section involves indexing for speeding up of search and sorting, types of indexes like single field, compound field, multiple index etc. The last section of the tutorial is about schema modelling. This section includes schema design factors like rich documents, no mongo joins, no constraints, atomic operation etc.
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
During this talk we'll navigate through a customer's journey as they migrate an existing MongoDB deployment to MongoDB Atlas. While the migration itself can be as simple as a few clicks, the prep/post effort requires due diligence to ensure a smooth transfer. We'll cover these steps in detail and provide best practices. In addition, we’ll provide an overview of what to consider when migrating other cloud data stores, traditional databases and MongoDB imitations to MongoDB Atlas.
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB
MongoDB Kubernetes operator and MongoDB Open Service Broker are ready for production operations. Learn about how MongoDB can be used with the most popular container orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications. A demo will show you how easy it is to enable MongoDB clusters as an External Service using the Open Service Broker API for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB
Humana, like many companies, is tackling the challenge of creating real-time insights from data that is diverse and rapidly changing. This is our journey of how we used MongoDB to combined traditional batch approaches with streaming technologies to provide continues alerting capabilities from real-time data streams.
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe.
This talk covers:
Common components of an IoT solution
The challenges involved with managing time-series data in IoT applications
Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance.
How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts
At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB
Our clients have unique use cases and data patterns that mandate the choice of a particular strategy. To implement these strategies, it is mandatory that we unlearn a lot of relational concepts while designing and rapidly developing efficient applications on NoSQL. In this session, we will talk about some of our client use cases, the strategies we have adopted, and the features of MongoDB that assisted in implementing these strategies.
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB
Encryption is not a new concept to MongoDB. Encryption may occur in-transit (with TLS) and at-rest (with the encrypted storage engine). But MongoDB 4.2 introduces support for Client Side Encryption, ensuring the most sensitive data is encrypted before ever leaving the client application. Even full access to your MongoDB servers is not enough to decrypt this data. And better yet, Client Side Encryption can be enabled at the "flick of a switch".
This session covers using Client Side Encryption in your applications. This includes the necessary setup, how to encrypt data without sacrificing queryability, and what trade-offs to expect.
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB
MongoDB Kubernetes operator is ready for prime-time. Learn about how MongoDB can be used with most popular orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications.
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
When you need to model data, is your first instinct to start breaking it down into rows and columns? Mine used to be too. When you want to develop apps in a modern, agile way, NoSQL databases can be the best option. Come to this talk to learn how to take advantage of all that NoSQL databases have to offer and discover the benefits of changing your mindset from the legacy, tabular way of modeling data. We’ll compare and contrast the terms and concepts in SQL databases and MongoDB, explain the benefits of using MongoDB compared to SQL databases, and walk through data modeling basics so you feel confident as you begin using MongoDB.
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB
Query performance should be the unsung hero of an application, but without proper configuration, can become a constant headache. When used properly, MongoDB provides extremely powerful querying capabilities. In this session, we'll discuss concepts like equality, sort, range, managing query predicates versus sequential predicates, and best practices to building multikey indexes.
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB
Aggregation pipeline has been able to power your analysis of data since version 2.2. In 4.2 we added more power and now you can use it for more powerful queries, updates, and outputting your data to existing collections. Come hear how you can do everything with the pipeline, including single-view, ETL, data roll-ups and materialized views.
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB
MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business.
This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented. In addition, we'll discuss future plans and opportunities and offer ample Q&A time with the engineers on the project.
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB
Virtual assistants are becoming the new norm when it comes to daily life, with Amazon’s Alexa being the leader in the space. As a developer, not only do you need to make web and mobile compliant applications, but you need to be able to support virtual assistants like Alexa. However, the process isn’t quite the same between the platforms.
How do you handle requests? Where do you store your data and work with it to create meaningful responses with little delay? How much of your code needs to change between platforms?
In this session we’ll see how to design and develop applications known as Skills for Amazon Alexa powered devices using the Go programming language and MongoDB.
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB
aux Core Data, appréciée par des centaines de milliers de développeurs. Apprenez ce qui rend Realm spécial et comment il peut être utilisé pour créer de meilleures applications plus rapidement.
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB
Il n’a jamais été aussi facile de commander en ligne et de se faire livrer en moins de 48h très souvent gratuitement. Cette simplicité d’usage cache un marché complexe de plus de 8000 milliards de $.
La data est bien connu du monde de la Supply Chain (itinéraires, informations sur les marchandises, douanes,…), mais la valeur de ces données opérationnelles reste peu exploitée. En alliant expertise métier et Data Science, Upply redéfinit les fondamentaux de la Supply Chain en proposant à chacun des acteurs de surmonter la volatilité et l’inefficacité du marché.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
2. Back to Basics 2016 : Webinar 4
Advanced Indexing –
Text and Geospatial Indexes
Joe Drumgoole
Director of Developer Advocacy, EMEA
@jdrumgoole
V1.1
3. 3
Recap
• Webinar 1 – Introduction to NoSQL
– The different types of NoSQL databases
– What kind of database is MongoDB? A document database.
• Webinar 2 – My First Application
– Creating databases and collections
– CRUD operations
– Indexes and Explain
• Webinar 3 – Schema Design
– Dynamic schema
– Embedding approaches
– Examples
7. 7
Creating a Simple Index
db.coll.createIndex( { fieldName : <Direction> } )
Database Name
Collection Name
Command
Field Name to
be indexed
Ascending : 1
Descending : -1
8. 8
Two Other Kinds of Indexes
• Full Text Index
– Allows searching inside the text of a field ( Lucene, Solr and Elastic
Search)
• Geospatial Index
– Allows searching by location (e.g. people near me)
• These indexes do not use Btrees
9. 9
Full Text Indexes
• An “inverted index” on all the words inside a single field (only one text index per collection)
{ “comment” : “I think your blog post is very interesting
and informative. I hope you will post more
info like this in the future” }
>> db.posts.createIndex( { “comments” : “text” } )
MongoDB Enterprise > db.posts.find( { $text: { $search : "info" }} )
{ "_id" : ObjectId(“…"), "comment" : "I think your blog post is very
interesting and informative. I hope you will post more info like this
in the future" }
MongoDB Enterprise >
11. 11
Dropping Text Indexes
• We drop text indexes by name rather than shape
db.posts.getIndexes()
{
"v" : 1,
"key" : {
"_fts" : "text",
"_ftsx" : 1
},
"name" : "comment_text_text",
"ns" : "test.posts",
"weights" : {
"comment" : 5,
"tags" : 10
},
"default_language" : "english",
"language_override" : "language",
"textIndexVersion" : 3
}
12. 12
Hence
MongoDB Enterprise > db.posts.dropIndex( "comment_text_tags_text" )
{ "nIndexesWas" : 2, "ok" : 1 }
MongoDB Enterprise >
• You can give an index an explict name to make this easier
MongoDB Enterprise > db.posts.createIndex( { "comments" : "text", "tags" :
"text" }, { "name" : "text_index" } )
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
}
13. 13
On The Server
I INDEX [conn275] build index on: test.posts properties: { v: 1, key:
{ _fts: "text", _ftsx: 1 }, name: "comment_text", ns: "test.posts",
weights: { comment: 1 }, default_language: "english",
language_override: "language", textIndexVersion: 3 }}
I INDEX [conn275] building index using bulk method
I INDEX [conn275] build index done. scanned 3 total records. 0 secs
15. 15
Using Weights
• We can assign different weights to different fields in the text index
• E.g. I want to favour tags over comments in searching
• So I increase the weight for the the tags field
>> db.blog.createIndex( { comment: "text",
tags : "text” },
{ weights: { comment: 5,
tags : 10 }} )
• Now searches will favour tags
17. 17
Other Parameters
• Language : Pick the language you want to search in e.g.
– $language : Spanish
• Support case sensitive searching
– $caseSensitive : True (default false)
• Support accented characters (diacritic sensitive search e.g. café
is distinguished from cafe )
– $diacriticSensitive : True (default false)
19. 19
Geospatial Indexes
• MongoDB supports 2D Sphere indexes
• Allows a user to represent location on the earth (which is a sphere)
• Coordinates are stored in GeoJSON format
• The Geospatial index supports subset of the GeoJSON operations
• The index is based on a QuadTree representation
• Index is based on WGS 84 standard
20. 20
Coordinates
• Coordinates are represented as longitude, latitude
• longitude
– Measured from Greenwich meridian in London (0 degrees) locations east
(up to 180 degrees)
– For locations west we specify as negative
• Latitude
– Measured from equator north and south (0 to 90 north, 0 to -90 south)
• Coordinates in MongoDB are stored on Longitude/Latitude order
• Coordinates in Google are stored in Latitude/Longitude order
21. 21
2DSphere Versions
• Three versions of 2dSphere index in MongoDB
• Version 1 : Up to MongoDB 2.4
• Version 2 : From MongoDB 2.6 onwards
• Version 3 : From MongoDB 3.2 onwards
• We will only be talking about Version 3 in this webinar
22. 22
Creating a 2dSphere Index
db.collection.createIndex
( { <location field> : "2dsphere" } )
• Location field must be coordinate or GeoJSON data
25. 25
Use a Simple Dataset to investigate Geo Queries
• Lets search for restaurants in Manhattan
• Using two candidate collections
– https://raw.githubusercontent.com/mongodb/docs-assets/geospatial/neighborhoods.json
– https://raw.githubusercontent.com/mongodb/docs-assets/geospatial/restaurants.json
• Import them into MongoDB
– mongoimport –c neighborhoods –d geo neighborhoods.json
– mongoimport –c restaurants –d geo restaurants.json
27. 27
Restaurant Document
MongoDB Enterprise > db.restaurants.findOne()
{
"_id" : ObjectId("55cba2476c522cafdb053adf"),
"location" : {
"coordinates" : [
-73.98241999999999,
40.579505
],
"type" : "Point"
},
"name" : "Riviera Caterer"
}
MongoDB Enterprise >
You can type this into
google maps but
remember to reverse the
coordinate order
29. 29
Use $geoIntersects to find our Neighborhood
• Assume we are at -73.93414657, 40.82302903
• What neighborhood are we in? Use $geoIntersects
db.neighborhoods.findOne({ geometry:
{ $geoIntersects:
{ $geometry:
{ type: "Point",
coordinates:
[ -73.93414657,
40.82302903 ]}}}})
31. 31
Find All Restaurants within 0.35 km
db.restaurants.find({ location:
{ $geoWithin: { $centerSphere:
[ [ -73.93414657, 40.82302903 ], 5 / 6,378.1 ] }
} })
Distance in km
Divide by radius of earth
to convert to radians
33. 33
Summary of Operators
• $geoIntersect: Find areas or points that overlap or are
adjacent
• $geoWithin: Find areas on points that lie within a specific area
• $geoNear: Returns locations in order from nearest to furthest
away
34. 34
Summary
• Text Indexes : Full text searching of all the text items in a
collection
• Geospatial Indexes : Search by location, by intersection or by
distance from a point
46. {
_id : ObjectId("4c4ba5e5e8aabf3"),
employee_name: "Dunham, Justin",
department : "Marketing",
title : "Product Manager, Web",
report_up: "Neray, Graham",
pay_band: “C",
benefits : [
{ type : "Health",
plan : "PPO Plus" },
{ type : "Dental",
plan : "Standard" }
]
}
Code/Highlight Example
47. Aggregation Framework Agility Backup Big Data Briefcase
Buildings Business Intelligence Camera Cash Register Catalog
Chat Checkmark Checkmark Cloud Commercial Contract
Computer Content Continuous Development Credit Card Customer Success
48. Data Center Data Variety Data Velocity Data Volume Data Warehouse Database
Dialogue Directory Documents Downloads Drivers Dynamic Schema
EDW Integration Faster Time to Market File Transfer Flexible Gear Hadoop
Health Check High Availability Horizontal Scaling Integrating into Infrastructure Internet of Things Iterative Development
49. Life Preserver Line Graph Lock Log Data Lower Cost Magnifying Glass
Man Mobile Phone Meter Monitoring Music New Apps
New Data Types Online Open Source Parachute Personalization Pin
Platform Certification Product Catalog Puzzle Pieces RDBMS Realtime Analytics Rich Querying
50. Life Preserver RSS Scalability Scale Secondary Indexing Steering Wheel
Stopwatch Text Search Tick Data Training Transmission Tower Trophy
Woman World
Editor's Notes
Who I am, how long have I been at MongoDB.
Each item in a Btree node points to a sub-tree containing elements below its key value. Insertions require a read before a write. Writes that split nodes are expensive.