This document provides an introduction to MongoDB, including what it is, why it may be used, and how its data model works. Some key points:
- MongoDB is a non-relational database that stores data in flexible, JSON-like documents rather than fixed schema tables.
- It offers advantages like dynamic schemas, embedding of related data, and fast performance at large scales.
- Data is organized into collections of documents, which can contain sub-documents to represent one-to-many relationships without joins.
- Queries use JSON-like syntax to search for patterns in documents, and indexes can improve performance.
Intro to MongoDB
Get a jumpstart on MongoDB, use cases, and next steps for building your first app with Buzz Moschetti, MongoDB Enterprise Architect.
@BuzzMoschetti
MongoDB Atlas makes it easy to set up, operate, and scale your MongoDB deployments in the cloud. From high availability to scalability, security to disaster recovery - we've got you covered.
Automated: With MongoDB Atlas, you no longer need to worry about operational tasks such as provisioning, configuration, patching, upgrades, backups, and failure recovery. MongoDB Atlas provides the functionality and reliability you need, at the click of a button.
Flexible: Only MongoDB Atlas combines the critical capabilities of relational databases with the innovations of NoSQL. Radically simplify development and operations by delivering a diverse range of capabilities in a single, managed database platform.
Secure: MongoDB Atlas provides multiple levels of security for your database. These include robust access control, network isolation using Amazon VPC, IP whitelists, encryption of data in-flight using TLS/SSL, and optional encryption of the underlying filesystem.
Scalable: MongoDB Atlas grows with you, all with the click of a button. You can scale up across a range of instance sizes, and scale-out with automatic sharding. And you can do it with zero application downtime.
Highly Available: MongoDB Atlas is designed to offer exceptional uptime. Recovery from instance failures is transparent and fully automated. A minimum of three copies of your data are replicated across availability zones and continuously backed up.
High Performance: MongoDB Atlas provides high throughput and low latency for the most demanding workloads. Consistent, predictable performance eliminates the need for separate caching tiers, and delivers a far better price-performance ratio compared to traditional database software.
Intro to MongoDB
Get a jumpstart on MongoDB, use cases, and next steps for building your first app with Buzz Moschetti, MongoDB Enterprise Architect.
@BuzzMoschetti
MongoDB Atlas makes it easy to set up, operate, and scale your MongoDB deployments in the cloud. From high availability to scalability, security to disaster recovery - we've got you covered.
Automated: With MongoDB Atlas, you no longer need to worry about operational tasks such as provisioning, configuration, patching, upgrades, backups, and failure recovery. MongoDB Atlas provides the functionality and reliability you need, at the click of a button.
Flexible: Only MongoDB Atlas combines the critical capabilities of relational databases with the innovations of NoSQL. Radically simplify development and operations by delivering a diverse range of capabilities in a single, managed database platform.
Secure: MongoDB Atlas provides multiple levels of security for your database. These include robust access control, network isolation using Amazon VPC, IP whitelists, encryption of data in-flight using TLS/SSL, and optional encryption of the underlying filesystem.
Scalable: MongoDB Atlas grows with you, all with the click of a button. You can scale up across a range of instance sizes, and scale-out with automatic sharding. And you can do it with zero application downtime.
Highly Available: MongoDB Atlas is designed to offer exceptional uptime. Recovery from instance failures is transparent and fully automated. A minimum of three copies of your data are replicated across availability zones and continuously backed up.
High Performance: MongoDB Atlas provides high throughput and low latency for the most demanding workloads. Consistent, predictable performance eliminates the need for separate caching tiers, and delivers a far better price-performance ratio compared to traditional database software.
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.
MongoDB is the most famous and loved NoSQL database. It has many features that are easy to handle when compared to conventional RDBMS. These slides contain the basics of MongoDB.
Introduction to Oracle Data Guard BrokerZohar Elkayam
This is an old deck I recently renewed for a customer session. This is the introduction to Oracle Data Guard broker feature, how to deploy it, how to use it and what are its benefits.
This presentation is based on version 11g but most of it is also compatible to Oracle 12c,
Agenda:
- Oracle Data Guard overview
- Dataguard broker introduction
- Configuring and using the data guard
- Live Demos
These are slides from our Big Data Warehouse Meetup in April. We talked about NoSQL databases: What they are, how they’re used and where they fit in existing enterprise data ecosystems.
Mike O’Brian from 10gen, introduced the syntax and usage patterns for a new aggregation system in MongoDB and give some demonstrations of aggregation using the new system. The new MongoDB aggregation framework makes it simple to do tasks such as counting, averaging, and finding minima or maxima while grouping by keys in a collection, complementing MongoDB’s built-in map/reduce capabilities.
For more information, visit our website at http://casertaconcepts.com/ or email us at info@casertaconcepts.com.
NestJS (https://nestjs.com/) is a Node.js framework for building server-side applications. This slide give you a brief introduction of Nest, and shows the examples like Service, Middleware, and Pipe, etc.
Agenda:
MongoDB Overview/History
Workshop
1. How to perform operations to MongoDB – Workshop
2. Using MongoDB in your Java application
Advance usage of MongoDB
1. Performance measurement comparison – real life use cases
3. Doing Cluster setup
4. Cons of MongoDB with other document oriented DB
5. Map-reduce/ Aggregation overview
Workshop prerequisite
1. All participants must bring their laptops.
2. https://github.com/geek007/mongdb-examples
3. Software prerequisite
a. Java version 1.6+
b. Your favorite IDE, Preferred http://www.jetbrains.com/idea/download/
c. MongoDB server version – 2.6.3 (http://www.mongodb.org/downloads - 64 bit version)
d. Participants can install MongoDB client – http://robomongo.org/
About Speaker:
Akbar Gadhiya is working with Ishi Systems as Programmer Analyst. Previously he worked with PMC, Baroda and HCL Technologies.
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.
MongoDB is the most famous and loved NoSQL database. It has many features that are easy to handle when compared to conventional RDBMS. These slides contain the basics of MongoDB.
Introduction to Oracle Data Guard BrokerZohar Elkayam
This is an old deck I recently renewed for a customer session. This is the introduction to Oracle Data Guard broker feature, how to deploy it, how to use it and what are its benefits.
This presentation is based on version 11g but most of it is also compatible to Oracle 12c,
Agenda:
- Oracle Data Guard overview
- Dataguard broker introduction
- Configuring and using the data guard
- Live Demos
These are slides from our Big Data Warehouse Meetup in April. We talked about NoSQL databases: What they are, how they’re used and where they fit in existing enterprise data ecosystems.
Mike O’Brian from 10gen, introduced the syntax and usage patterns for a new aggregation system in MongoDB and give some demonstrations of aggregation using the new system. The new MongoDB aggregation framework makes it simple to do tasks such as counting, averaging, and finding minima or maxima while grouping by keys in a collection, complementing MongoDB’s built-in map/reduce capabilities.
For more information, visit our website at http://casertaconcepts.com/ or email us at info@casertaconcepts.com.
NestJS (https://nestjs.com/) is a Node.js framework for building server-side applications. This slide give you a brief introduction of Nest, and shows the examples like Service, Middleware, and Pipe, etc.
Agenda:
MongoDB Overview/History
Workshop
1. How to perform operations to MongoDB – Workshop
2. Using MongoDB in your Java application
Advance usage of MongoDB
1. Performance measurement comparison – real life use cases
3. Doing Cluster setup
4. Cons of MongoDB with other document oriented DB
5. Map-reduce/ Aggregation overview
Workshop prerequisite
1. All participants must bring their laptops.
2. https://github.com/geek007/mongdb-examples
3. Software prerequisite
a. Java version 1.6+
b. Your favorite IDE, Preferred http://www.jetbrains.com/idea/download/
c. MongoDB server version – 2.6.3 (http://www.mongodb.org/downloads - 64 bit version)
d. Participants can install MongoDB client – http://robomongo.org/
About Speaker:
Akbar Gadhiya is working with Ishi Systems as Programmer Analyst. Previously he worked with PMC, Baroda and HCL Technologies.
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).
Jumpstart your day with an introduction to MongoDB by building a simple web app with React, Atlas, and Stitch. First, we will cover the foundations of schema design, security, queries and indexing. Then we'll partially refactor an application using the MERN stack to a serverless application using MongoDB Stitch.
Rapid and Scalable Development with MongoDB, PyMongo, and MingRick Copeland
This talk, given at PyGotham 2011, will teach you techniques using the popular NoSQL database MongoDB and the Python library Ming to write maintainable, high-performance, and scalable applications. We will cover everything you need to become an effective Ming/MongoDB developer from basic PyMongo queries to high-level object-document mapping setups in Ming.
This upload requires better support for ODP formatForest Mars
I uplopaded this version in Open Office .ODP format, which is presumably the reason slideshare messed up the formatting. Slideshare, can we get some better support for open formats, stat?
If you'd like to view these slides, I've re-uploaded this talk in .ppt format.
MongoDB.local Sydney: An Introduction to Document Databases with MongoDBMongoDB
This presentation will describe MongoDB's document database and what advantages it has over traditional databases. The presentation will explore MongoDB's server, query language, ecosystem and various tools. Brett will demonstrate using various MongoDB tools to assist in developing a Python application that utilises MongoDB as the database.
Architecture | Busy Java Developers Guide to NoSQL | Ted NewardJAX London
2011-11-02 | 03:45 PM - 04:35 PM |
The NoSQL movement has stormed onto the development scene, and it’s left a few developers scratching their heads, trying to figure out when to use a NoSQL database instead of a regular database, much less which NoSQL database to use. In this session, we’ll examine the NoSQL ecosystem, look at the major players, how the compare and contrast, and what sort of architectural implications they have for software systems in general.
Rapid, Scalable Web Development with MongoDB, Ming, and PythonRick Copeland
In 2009, SourceForge embarked on a quest to modernize our websites, converting a site written for a hodge-podge of relational databases in PHP to a MongoDB and Python-powered site, with a small development team and a tight deadline. We have now completely rewritten both the consumer and producer parts of the site with better usability, more functionality and better performance. This talk focuses on how we're using MongoDB, the pymongo driver, and Ming, an ORM-like library implemented at SourceForge, to continually improve and expand our offerings, with a special focus on how3 anyone can quickly become productive with Ming and pymongo without having to apologize for poor performance.
Use Performance Insights To Enhance MongoDB Performance - (Manosh Malai - Myd...Mydbops
Performance improvement, debugging, and monitoring are essential parts of the DBE(Database Engineering Team) role. The presentation present intriguing strategies, techniques, and tools that can be used to address or circumvent the majority of performance-related problems in this MongoDB Performance.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
4. Not a RDBMS Mongo is not a relational database like MySQL No transactions No referential integrity No joins No schema, so no columns or rows NoSQL
5. Not a Key-Value Store Mongo is not simply a key-value store like Redis Stores structured data Rich query interface Indexes Map/Reduce Automatic sharding, GridFS, geospatial indexing, etc.
6. Document-oriented Database Records are JSON documents (actually BSON) Stored in collections No predefined schema Docs in the same collection don’t even need to have the same fields Atomic in-place operators for contention-free updates $set, $inc, $push, $pop, etc.
7. Mongo Document user = { name: "Frank Furter", occupation: "A scientist", location: "Transylvania" }
9. It’s Stupid Fast! Anywhere from 2 to 10 times faster than MySQL Depends on which contrived benchmark you’re looking at Here’s one I just made up:
10. It’s Stupid Fast! About 50 times faster than CouchDB According to http://www.idiotsabound.com/did-i-mention-mongodb-is-fast-way-to-go-mongo 2 important points: It’s pretty quick Benchmarks are worthless unless you do them on your actual workload
11. It’s Web Scale! Sharding built-in, automatic, and *Just Works™ *Just Works™ guarantee applies only if you have a cluster of shard replica sets with config servers and routing servers and you define your own shard key(s) with appropriate uniformity and granularity Asynchronous replication for failover and redundancy
12. It’s Pretty Painless Schemaless No more configuring database columns with types No more defining and managing migrations Just stick your data in there, it’s fine NoSQL ORMs exist mostly because writing SQL sucks Mongo’s query language is basically JSON The Mongo driver for your favorite language is really nice and officially supported Handy JavaScript shell for the CLI
13. It’s Pretty Painless MySQL /* First go create the database, the table, the schema, etc. */ mysql_connect("localhost", "username", "password") or die(mysql_error()); mysql_select_db("test") or die(mysql_error()); $sql = "INSERT INTO users (name, age) VALUES ('Janet', 23)"; mysql_query($sql); $result = mysql_query("SELECT * FROM users WHERE age = 23"); $row = mysql_fetch_assoc($result); echo "Oh, " . $row['name'] . "!"; // prints "Oh, Janet!" MongoDB $mongo = new Mongo(); // defaults to localhost with no auth $users = $mongo->test_db->users; // database and collection created implicitly $users->insert( array('name' => 'Brad', 'age' => 25) ); $user = $users->findOne( array('age' => 25) ); echo "Oh, " . $user->name . "!"; // prints "Oh, Brad!"
14. All the Cool Kids Are Doing It http://www.mongodb.org/display/DOCS/Production+Deployments
16. Documents and Collections Documents are the records Like objects in OOP, or rows in RDBMS Collections are groups of documents Usually represent a top-level class in your app Heterogeneous set Unlike RDBMS tables, no predefined schema No foreign keys, so how do we reference other objects? Don't! Just embed the sub-item in the parent doc Or, use a key for references and deal with the fact that you don't get integrity or joins
17. Embedded Objects Documents can embed other documents Used to efficiently represent a relation For example: { name: 'Brad Majors', address: { street: 'Oak Terrace', city: 'Denton' } }
22. JS Shell Comes with MongoDB Launch it with 'mongo' on the command-line Try a simplified version at http://www.mongodb.org/ Great fit since Mongo docs are basically JSON
25. I thought you said no schema? There is no predefined schema Your application creates an ad-hoc schema with the objects it creates The schema is implicit in the queries your application runs
26. Schema Design Use collections to represent the top-level classes of your application But don't just make a collection for every object type These aren't like tables in an RDBMS Less normalization, more embedding
27. Obligatory Blog Post Example A blog post has an author, some text, and many comments The comments are unique per post, but one author has many posts How would you design this in SQL? Let's look at how we might design it in Mongo
28. Bad Schema Design: References Collections for posts, authors, and comments References by manually created ID post = { id: 150, author: 100, text: 'This is a pretty awesome post.', comments: [100, 105, 112] } author = { id: 100, name: 'Michael Arrington' posts: [150] } comment = { id: 105, text: 'Whatever this sux.' }
29. Better Schema Design: Embedding Collection for posts Embed comments, author name post = { author: 'Michael Arrington', text: 'This is a pretty awesome post.', comments: [ 'Whatever this post sux.', 'I agree, lame!' ] }
30. Benefits Embedded objects brought back in the same query as parent object Only 1 trip to the DB server required Objects in the same collection are generally stored contiguously on disk Spatial locality = faster If the document model matches your domain well, it can be much easier to comprehend than nasty joins
31. Indexes Mongo supports indexes to greatly improve query performance No need to create in advance Create idempotent indexes in your app with "ensure_index"
32. Schema Design Limitations No referential integrity High degree of denormalization means updating something in many places instead of one Lack of predefined schema is a double-edged sword Hopefully you have a model in your app Objects within a collection can be completely inconsistent in their fields
34. Final Thoughts MongoDB is fast no matter how you slice it It achieves high performance by literally playing fast and loose with your data That's not necessarily a bad thing, just a tradeoff Very rapid development, open source Document model is simple but powerful Advanced features like map/reduce, geospatial indexing etc. are very compelling Surprisingly great drivers for most languages