Applications get great efficiency from MongoDB by combining data that is accessed together into a single document. There are however situations where it is more efficient to have references between documents rather than embedding everything into a single document. This led to joins being our most requested feature. MongoDB 3.2 addresses this through the introduction of the $lookup stage in the aggregation pipeline to implement left-outer joins.
This webinar looks at $lookup as well as the other significant aggregation enhancements coming with MongoDB 3.2—why they're needed, what they deliver, and how to use them.
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.
MongoDB Europe 2016 - Graph Operations with MongoDBMongoDB
The popularity of dedicated graph technologies has risen greatly in recent years, at least partly fuelled by the explosion in social media and similar systems, where a friend network or recommendation engine is often a critical component when delivering a successful application. MongoDB 3.4 introduces a new Aggregation Framework graph operator, $graphLookup, to enable some of these types of use cases to be built easily on top of MongoDB. We will see how semantic relationships can be modelled inside MongoDB today, how the new $graphLookup operator can help simplify this in 3.4, and how $graphLookup can be used to leverage these relationships and build a commercially focused news article recommendation system.
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.
Webinar: Exploring the Aggregation FrameworkMongoDB
Developers love MongoDB because its flexible document model enhances their productivity. But did you know that MongoDB supports rich queries and lets you accomplish some of the same things you currently do with SQL statements? And that MongoDB's powerful aggregation framework makes it possible to perform real-time analytics for dashboards and reports?
Watch this webinar for an introduction to the MongoDB aggregation framework and a walk through of what you can do with it. We'll also demo an analysis of U.S. census data.
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.
MongoDB Europe 2016 - Graph Operations with MongoDBMongoDB
The popularity of dedicated graph technologies has risen greatly in recent years, at least partly fuelled by the explosion in social media and similar systems, where a friend network or recommendation engine is often a critical component when delivering a successful application. MongoDB 3.4 introduces a new Aggregation Framework graph operator, $graphLookup, to enable some of these types of use cases to be built easily on top of MongoDB. We will see how semantic relationships can be modelled inside MongoDB today, how the new $graphLookup operator can help simplify this in 3.4, and how $graphLookup can be used to leverage these relationships and build a commercially focused news article recommendation system.
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.
Webinar: Exploring the Aggregation FrameworkMongoDB
Developers love MongoDB because its flexible document model enhances their productivity. But did you know that MongoDB supports rich queries and lets you accomplish some of the same things you currently do with SQL statements? And that MongoDB's powerful aggregation framework makes it possible to perform real-time analytics for dashboards and reports?
Watch this webinar for an introduction to the MongoDB aggregation framework and a walk through of what you can do with it. We'll also demo an analysis of U.S. census data.
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.
For developers new to MongoDB and Node.js, however, some the common design patterns are very different than those of a RDBMS and traditional synchronous languages. Developers learning these technologies together may find it a bit bewildering. In reality, however, these tools fit perfectly together and enable I high degree of developer productivity and application performance.
This webinar will walk developers through common MongoDB development patterns in Node.js, such as efficiently loading data into MongoDB using MongoDB's bulk API, iterating through query results, and managing simultaneous asynchronous MongoDB queries to provide the best possible application performance. Working Node.js and MongoDB examples will be used throughout the presentation.
MongoDB .local Chicago 2019: Practical Data Modeling for MongoDB: TutorialMongoDB
For 30 years, developers have been taught that relational data modeling was THE way to model, but as more companies adopt MongoDB as their data platform, the approaches that work well in relational design actually work against you in a document model design. In this talk, we will discuss how to conceptually approach modeling data with MongoDB, focusing on practical foundational techniques, paired with tips and tricks, and wrapping with discussing design patterns to solve common real world problems.
This presentation will demonstrate how you can use the aggregation pipeline with MongoDB similar to how you would use GROUP BY in SQL and the new stage operators coming 3.4. MongoDB’s Aggregation Framework has many operators that give you the ability to get more value out of your data, discover usage patterns within your data, or use the Aggregation Framework to power your application. Considerations regarding version, indexing, operators, and saving the output will be reviewed.
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.
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.
MongoDB for Time Series Data Part 2: Analyzing Time Series Data Using the Agg...MongoDB
The United States will be deploying 16,000 traffic speed monitoring sensors - 1 on every mile of US interstate in urban centers. These sensors update the speed, weather, and pavement conditions once per minute. MongoDB will collect and aggregate live sensor data feeds from roadways around the country, support real-time queries from cars on traffic conditions on their route as well as be the platform for real-time dashboards displaying traffic conditions and more complex analytical queries used to identify traffic trends. In this session, we’ll implement a few different data aggregation techniques to query and dashboard the metrics gathered from the US interstate.
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.
To understand how to make your application fast, it's important to understand what makes the database fast. We will take a detailed look at how to think about performance, and how different choices in schema design affect your cluster performances depending on storage engines used and physical resources available.
MongoDB .local Munich 2019: Best Practices for Working with IoT and Time-seri...MongoDB
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.
MongoDB offers two native data processing tools: MapReduce and the Aggregation Framework. MongoDB’s built-in aggregation framework is a powerful tool for performing analytics and statistical analysis in real-time and generating pre-aggregated reports for dashboarding. In this session, we will demonstrate how to use the aggregation framework for different types of data processing including ad-hoc queries, pre-aggregated reports, and more. At the end of this talk, you should walk aways with a greater understanding of the built-in data processing options in MongoDB and how to use the aggregation framework in your next project.
Data analytics can offer insights into your business and help take it to the next level. In this talk you'll learn about MongoDB tools for building visualizations, dashboards and interacting with your data. We'll start with exploratory data analysis using MongoDB Compass.
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.
For developers new to MongoDB and Node.js, however, some the common design patterns are very different than those of a RDBMS and traditional synchronous languages. Developers learning these technologies together may find it a bit bewildering. In reality, however, these tools fit perfectly together and enable I high degree of developer productivity and application performance.
This webinar will walk developers through common MongoDB development patterns in Node.js, such as efficiently loading data into MongoDB using MongoDB's bulk API, iterating through query results, and managing simultaneous asynchronous MongoDB queries to provide the best possible application performance. Working Node.js and MongoDB examples will be used throughout the presentation.
MongoDB .local Chicago 2019: Practical Data Modeling for MongoDB: TutorialMongoDB
For 30 years, developers have been taught that relational data modeling was THE way to model, but as more companies adopt MongoDB as their data platform, the approaches that work well in relational design actually work against you in a document model design. In this talk, we will discuss how to conceptually approach modeling data with MongoDB, focusing on practical foundational techniques, paired with tips and tricks, and wrapping with discussing design patterns to solve common real world problems.
This presentation will demonstrate how you can use the aggregation pipeline with MongoDB similar to how you would use GROUP BY in SQL and the new stage operators coming 3.4. MongoDB’s Aggregation Framework has many operators that give you the ability to get more value out of your data, discover usage patterns within your data, or use the Aggregation Framework to power your application. Considerations regarding version, indexing, operators, and saving the output will be reviewed.
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.
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.
MongoDB for Time Series Data Part 2: Analyzing Time Series Data Using the Agg...MongoDB
The United States will be deploying 16,000 traffic speed monitoring sensors - 1 on every mile of US interstate in urban centers. These sensors update the speed, weather, and pavement conditions once per minute. MongoDB will collect and aggregate live sensor data feeds from roadways around the country, support real-time queries from cars on traffic conditions on their route as well as be the platform for real-time dashboards displaying traffic conditions and more complex analytical queries used to identify traffic trends. In this session, we’ll implement a few different data aggregation techniques to query and dashboard the metrics gathered from the US interstate.
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.
To understand how to make your application fast, it's important to understand what makes the database fast. We will take a detailed look at how to think about performance, and how different choices in schema design affect your cluster performances depending on storage engines used and physical resources available.
MongoDB .local Munich 2019: Best Practices for Working with IoT and Time-seri...MongoDB
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.
MongoDB offers two native data processing tools: MapReduce and the Aggregation Framework. MongoDB’s built-in aggregation framework is a powerful tool for performing analytics and statistical analysis in real-time and generating pre-aggregated reports for dashboarding. In this session, we will demonstrate how to use the aggregation framework for different types of data processing including ad-hoc queries, pre-aggregated reports, and more. At the end of this talk, you should walk aways with a greater understanding of the built-in data processing options in MongoDB and how to use the aggregation framework in your next project.
Data analytics can offer insights into your business and help take it to the next level. In this talk you'll learn about MongoDB tools for building visualizations, dashboards and interacting with your data. We'll start with exploratory data analysis using MongoDB Compass.
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.
On Tuesday 18th March, the MongoDB team held on online Cloud Workshop in place of the in-person event which was planned.
Attendees learnt how to build modern, event driven applications powered by MongoDB Atlas in Google Cloud Platform (GCP) and were shown relevant operational and security best practices, to get started immediately with their own digital transformations.
Webinar: Data Processing and Aggregation OptionsMongoDB
MongoDB scales easily to store mass volumes of data. However, when it comes to making sense of it all what options do you have? In this talk, we'll take a look at 3 different ways of aggregating your data with MongoDB, and determine the reasons why you might choose one way over another. No matter what your big data needs are, you will find out how MongoDB the big data store is evolving to help make sense of your data.
Solutions for bi-directional integration between Oracle RDBMS & Apache KafkaGuido Schmutz
Apache Kafka is a popular distributed streaming data platform. A Kafka cluster stores streams of records (messages) in categories called topics. It is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. Data sources flowing into Kafka are often native data streams such as social media streams, telemetry data, financial transactions and many others. But these data stream only contain part of the information. A lot of data necessary in stream processing is stored in traditional systems backed by relational databases. To implement new and modern, real-time solutions, an up-to-date view of that information is needed. So how do we make sure that information can flow between the RDBMS and Kafka, so that changes are available in Kafka as soon as possible in near-real-time? This session will present different approaches for integrating relational databases with Kafka, such as Kafka Connect, Oracle GoldenGate and bridging Kafka with Oracle Advanced Queuing (AQ).
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.
MongoDB.local DC 2018: Tutorial - Data Analytics with MongoDBMongoDB
Data analytics can offer insights into your business and help take it to the next level. In this talk you'll learn about MongoDB tools for building visualizations, dashboards and interacting with your data. We'll start with exploratory data analysis using MongoDB Compass. Then, in a matter of minutes, we'll take you from 0 to 1 - connecting to your Atlas cluster via BI Connector and running analytical queries against it in Microsoft Excel. We'll also showcase the new MongoDB Charts product and you'll see how quick, easy and intuitive analytics can be on the MongoDB platform without flattening the data or spending time and effort on complicated and fragile ETL.
Solutions for bi-directional Integration between Oracle RDMBS & Apache KafkaGuido Schmutz
A Kafka cluster stores streams of records (messages) in categories called topics. It is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. Today’s enterprises have their core systems often implemented on top of relational databases, such as the Oracle RDBMS. Implementing a new solution supporting the digital strategy using Kafka and the ecosystem can not always be done completely separate from the traditional legacy solutions. Often streaming data has to be enriched with state data which is held in an RDBMS of a legacy application. It’s important to cache this data in the stream processing solution, so that It can be efficiently joined to the data stream. But how do we make sure that the cache is kept up-to-date, if the source data changes? We can either poll for changes from Kafka using Kafka Connect or let the RDBMS push the data changes to Kafka. But what about writing data back to the legacy application, i.e. an anomaly is detected inside the stream processing solution which should trigger an action inside the legacy application. Using Kafka Connect we can write to a database table or view, which could trigger the action. But this not always the best option. If you have an Oracle RDBMS, there are many other ways to integrate the database with Kafka, such as Advanced Queueing (message broker in the database), CDC through Golden Gate or Debezium, Oracle REST Database Service (ORDS) and more. In this session, we present various blueprints for integrating an Oracle RDBMS with Apache Kafka in both directions and discuss how these blueprints can be implemented using the products mentioned before.
Solutions for bi-directional integration between Oracle RDBMS and Apache Kafk...confluent
A Kafka cluster stores streams of records (messages) in categories called topics. It is the architectural backbone for integrating streaming data with a Data Lake, Microservices and Stream Processing. Today's enterprises have their core systems often implemented on top of relational databases, such as the Oracle RDBMS. Implementing a new solution supporting the digital strategy using Kafka and the ecosystem can not always be done completely separate from the traditional legacy solutions. Often streaming data has to be enriched with state data which is held in an RDBMS of a legacy application. It's important to cache this data in the stream processing solution, so that It can be efficiently joined to the data stream. But how do we make sure that the cache is kept up-to-date, if the source data changes? We can either poll for changes from Kafka using Kafka Connect or let the RDBMS push the data changes to Kafka. But what about writing data back to the legacy application, i.e. an anomaly is detected inside the stream processing solution which should trigger an action inside the legacy application. Using Kafka Connect we can write to a database table or view, which could trigger the action. But this not always the best option. If you have an Oracle RDBMS, there are many other ways to integrate the database with Kafka, such as Advanced Queueing (message broker in the database), CDC through Golden Gate or Debezium, Oracle REST Database Service (ORDS) and more. In this session, we present various blueprints for integrating an Oracle RDBMS with Apache Kafka in both directions and discuss how these blueprints can be implemented using the products mentioned before.
Back to Basics Webinar 4: Advanced Indexing, Text and Geospatial IndexesMongoDB
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.
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.
CouchApps are web applications built using CouchDB, JavaScript, and HTML5. CouchDB is a document-oriented database that stores JSON documents, has a RESTful HTTP API, and is queried using map/reduce views. This talk will answer your basic questions about CouchDB, but will focus on building CouchApps and related tools.
Learn why financial services companies have adopted MongoDB at an unprecedented pace. This webinar will focus on how banks use MongoDB to aggregate, analyze and manage risk within groups and across the firm. It will illustrate how MongoDB’s dynamic schema and scalability enable it to act as the data hub, integrating all source data from across the firm regardless of its volume and variable structure. We will explore how banks use MongoDB's rich query and aggregation capabilities to assess risk exposure across asset classes and counterparties in real-time and overnight capacities.
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: 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.
MongoDB .local Paris 2020: Les bonnes pratiques pour sécuriser MongoDBMongoDB
Chaque entreprise devient une entreprise de logiciels, fournissant des solutions client pour accéder à une variété de services et d'informations. Les entreprises commencent maintenant à valoriser leurs données et à obtenir de meilleures informations pour l'entreprise. Un défi crucial consiste à s'assurer que ces données sont toujours disponibles et sécurisées pour être conformes aux objectifs commerciaux de l'entreprise et aux contraintes réglementaires des pays. MongoDB fournit la couche de sécurité dont vous avez besoin, venez découvrir comment sécuriser vos données avec MongoDB.
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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.
2. DISCLAIMER: MongoDB's product
plans are for informational purposes
only. MongoDB's plans may change
and you should not rely on them for
delivery of a specific feature at a
specific time.
3. Agenda
Document vs. Relational Model
Analytics on MongoDB data
60,000 feet – what is the aggregation pipeline
Aggregation pipeline operators
$lookup (Left Outer Equi Joins) in MongoDB
3.2
Other aggregation enhancements
Worked examples
5. Existing Alternatives to Joins
{ "_id": 10000,
"items": [
{
"productName": "laptop",
"unitPrice": 1000,
"weight": 1.2,
"remainingStock": 23
},
{
"productName": "mouse",
"unitPrice": 20,
"weight": 0.2,
"remainingStock": 276
}
],
…
}
• Option 1: Include all data for an order in
the same document
– Fast reads
• One find delivers all the required data
– Captures full description at the time of the
event
– Consumes extra space
• Details of each product stored in many order
documents
– Complex to maintain
• A change to any product attribute must be
propagated to all affected orders
orders
6. Existing Alternatives to Joins
{
"_id": 10000,
"items": [
12345,
54321
],
...
}
• Option 2: Order document
references product documents
– Slower reads
• Multiple trips to the database
– Space efficient
• Product details stored once
– Lose point-in-time snapshot of full
record
– Extra application logic
• Must iterate over product IDs in
the order document and find the
product documents
• RDBMS would automate through
a JOIN
orders
{
"_id": 12345,
"productName": "laptop",
"unitPrice": 1000,
"weight": 1.2,
"remainingStock": 23
}
{
"_id": 54321,
"productName": "mouse",
"unitPrice": 20,
"weight": 0.2,
"remainingStock": 276
}
products
7. The Winner?
• In general, Option 1 wins
– Performance and containment of everything in same place beats space
efficiency of normalization
– There are exceptions
• e.g. Comments in a blog post -> unbounded size
• However, analytics benefit from combining data from multiple collections
– Keep listening...
8. Analytics on MongoDB Data – Options in 3.0
• Extract data from MongoDB and
perform complex analytics with
Hadoop
– Batch rather than real-time
– Extra nodes to manage
• Direct access to MongoDB from
SPARK
• MongoDB’s MapReduce feature
– Complexity
• MongoDB aggregation pipeline
– Real-time
– Live, operation data set
– Narrower feature set
Hadoop
Connecto
r
BSON
Files
MapReduce & HDFS
17. Aggregation Pipeline Stages
• $match
Filter documents
• $geoNear
Geospherical query
• $project
Reshape documents
• $lookup
New – Left-outer joins
• $unwind
Expand documents
• $group
Summarize documents
• $sample
New – Randomly selects a subset
of documents
• $sort
Order documents
• $skip
Jump over a number of documents
• $limit
Limit number of documents
• $redact
Restrict documents
• $out
Sends results to a new collection
18. $lookup
• Left-outer join
– Includes all documents from the
left collection
– For each document in the left
collection, find the matching
documents from the right
collection and embed them
Left Collection Right Collection
30. Exercise for the Viewer
By year, statistics on house
prices for all streets within
3 km of a particular school
(you know the school’s
location)
Answer:
http://clusterdb.com/answer.h
tml
{
"_id": 2015,
"highestPrice": 1350000,
"lowestPrice": 125000,
"averagePrice": 410593,
"priceStdDev": 182358
},
{
"_id": 2014,
"highestPrice": 3950000,
"lowestPrice": 66950,
"averagePrice": 392074,
"priceStdDev": 216119
},
32. Aggregation With a Sharded Database
• Workload split between shards
– Client works through mongos as with
any query
– Shards execute pipeline up to a point
– A single shard merges cursors and
continues processing
– Use explain to analyze pipeline split
– Early $match on shard key may
exclude shards
– Potential CPU and memory
implications for primary shard host
– $lookup & $out performed within
Primary shard for the database
?
34. Restrictions
• $lookup only support equality for the match
• $lookup can only be used in the aggregation pipeline (e.g. not for find)
• The pipeline is linear; no forks. Can remove data at each stage and can only add new
raw data through $lookup
• Right collection for $lookup cannot be sharded
• Indexes are only used at the beginning of the pipeline (and right tables in subsequent
$lookups), before any data transformations
• $out can only be used in the final stage of the pipeline
• $geoNear can only be the first stage in the pipeline
• The BI Connector for MongoDB is part of MongoDB Enterprise Advanced
– Not in community
35. Next Steps
• Documentation
– https://docs.mongodb.org/manual/release-notes/3.2/#aggregation-framework-
enhancements
• Not yet ready for production but download and try!
– https://www.mongodb.org/downloads#development
• Detailed blog
– https://www.mongodb.com/blog/post/joins-and-other-aggregation-enhancements-
coming-in-mongodb-3-2-part-1-of-3-introduction
• Feedback
– MongoDB 3.2 Bug Hunt
• https://www.mongodb.com/blog/post/announcing-the-mongodb-3-2-bug-hunt
– https://jira.mongodb.org/
DISCLAIMER: MongoDB's product plans are for informational purposes only. MongoDB's plans
may change and you should not rely on them for delivery of a specific feature at a specific time.
36. MongoDB Days 2015
October 6, 2015
October 20, 2015
November 5, 2015
December 2, 2015
France
Germany
UK
Silicon Valley