In this webinar, we will introduce MongoDB’s new aggregation system that simplifies tasks like counting, averaging, and finding minima or maxima while grouping by keys in a collection. The new aggregation features are not a replacement for MapReduce but will make it possible to do a number of things much more easily without having to resort to the big hammer that is MapReduce. After introducing the syntax and usage patterns, we will give some demonstrations of aggregation using the new system.
We're working on a new aggregation framework for MongoDB that will introduce a new aggregation system that will make it a lot easier to do simple tasks like counting, averaging, and finding minima or maxima while grouping by keys in a collection. The new aggregation features are not a replacement for map-reduce, but will make it possible to do a number of things much more easily, without having to resort to the big hammer that is map-reduce. After introducing the syntax and usage patterns for the new aggregation system, we will give some demonstrations of aggregation using the new system.
Introduction to the New Aggregation FrameworkMongoDB
The aggregation framework provides a more powerful and flexible way to query and aggregate data compared to MapReduce in MongoDB. It is implemented using pipelines of aggregation operators written in C++ rather than JavaScript. Some key benefits include being declarative, optimized for aggregation workflows, and playing nicely with sharding. The document provides examples of common aggregation operators like $match, $project, $group, $sort, and $limit that allow filtering, transforming, grouping, sorting and limiting documents as they pass through the pipeline.
This document discusses JavaScript MV* frameworks. It covers common features of these frameworks including the client-server model, event handling, view templates, and URL routing. It also provides examples of models, collections, implementing client-server sync, views and events, view templates, and UI element binding.
The document provides a comparison of MongoDB query and aggregation capabilities versus Couchbase N1QL capabilities. It begins with an introduction and overview of the different approaches and use cases for each. It then delves into detailed comparisons of specific query and aggregation operations such as CRUD, nested queries, array queries, text search, and joins. Overall, it finds that while both provide robust querying, Couchbase N1QL expressions tend to be more declarative due to its basis in SQL, whereas MongoDB requires more familiarity with its syntax.
The document is a HTML page for a YouTube video. It contains metadata such as the video title ("Dota 2 The International 2014 Virtus.Pro vs MVP [GAME 1]"), description, and tags describing the video content. It also includes scripts that control elements on the YouTube watch page like menus, buttons and player.
This document discusses using a Raspberry Pi to log temperature and humidity readings and display the data in a graph on a WordPress site. It describes creating a custom post type to store fever log entries, registering REST API routes to log readings and retrieve the history, and using a Python script and crontab to automatically log readings. JavaScript and CSS are used to display a graph of the fever readings on the WordPress site.
Operational Intelligence with MongoDB WebinarMongoDB
This document discusses using MongoDB for operational intelligence and real-time analytics of log and event data. It describes how MongoDB can ingest large volumes of data from multiple sources at high write volumes. Queries can then be performed rapidly to analyze the data and drill down into specific events. The aggregation framework is used to generate rollups and reports from the data on-demand or on a scheduled basis.
This document discusses using MapReduce, the Aggregation Framework, and the Hadoop Connector to perform data analysis and reporting on data stored in MongoDB. It provides examples of using various aggregation pipeline stages like $match, $project, $group to filter, reshape, and group documents. It also covers limitations of the aggregation framework and how the Hadoop Connector can help integrate MongoDB with Hadoop for distributed processing of large datasets across multiple nodes.
We're working on a new aggregation framework for MongoDB that will introduce a new aggregation system that will make it a lot easier to do simple tasks like counting, averaging, and finding minima or maxima while grouping by keys in a collection. The new aggregation features are not a replacement for map-reduce, but will make it possible to do a number of things much more easily, without having to resort to the big hammer that is map-reduce. After introducing the syntax and usage patterns for the new aggregation system, we will give some demonstrations of aggregation using the new system.
Introduction to the New Aggregation FrameworkMongoDB
The aggregation framework provides a more powerful and flexible way to query and aggregate data compared to MapReduce in MongoDB. It is implemented using pipelines of aggregation operators written in C++ rather than JavaScript. Some key benefits include being declarative, optimized for aggregation workflows, and playing nicely with sharding. The document provides examples of common aggregation operators like $match, $project, $group, $sort, and $limit that allow filtering, transforming, grouping, sorting and limiting documents as they pass through the pipeline.
This document discusses JavaScript MV* frameworks. It covers common features of these frameworks including the client-server model, event handling, view templates, and URL routing. It also provides examples of models, collections, implementing client-server sync, views and events, view templates, and UI element binding.
The document provides a comparison of MongoDB query and aggregation capabilities versus Couchbase N1QL capabilities. It begins with an introduction and overview of the different approaches and use cases for each. It then delves into detailed comparisons of specific query and aggregation operations such as CRUD, nested queries, array queries, text search, and joins. Overall, it finds that while both provide robust querying, Couchbase N1QL expressions tend to be more declarative due to its basis in SQL, whereas MongoDB requires more familiarity with its syntax.
The document is a HTML page for a YouTube video. It contains metadata such as the video title ("Dota 2 The International 2014 Virtus.Pro vs MVP [GAME 1]"), description, and tags describing the video content. It also includes scripts that control elements on the YouTube watch page like menus, buttons and player.
This document discusses using a Raspberry Pi to log temperature and humidity readings and display the data in a graph on a WordPress site. It describes creating a custom post type to store fever log entries, registering REST API routes to log readings and retrieve the history, and using a Python script and crontab to automatically log readings. JavaScript and CSS are used to display a graph of the fever readings on the WordPress site.
Operational Intelligence with MongoDB WebinarMongoDB
This document discusses using MongoDB for operational intelligence and real-time analytics of log and event data. It describes how MongoDB can ingest large volumes of data from multiple sources at high write volumes. Queries can then be performed rapidly to analyze the data and drill down into specific events. The aggregation framework is used to generate rollups and reports from the data on-demand or on a scheduled basis.
This document discusses using MapReduce, the Aggregation Framework, and the Hadoop Connector to perform data analysis and reporting on data stored in MongoDB. It provides examples of using various aggregation pipeline stages like $match, $project, $group to filter, reshape, and group documents. It also covers limitations of the aggregation framework and how the Hadoop Connector can help integrate MongoDB with Hadoop for distributed processing of large datasets across multiple nodes.
The Ring programming language version 1.7 book - Part 73 of 196Mahmoud Samir Fayed
This document describes the code for a basic notepad application created using the Ring programming language and Qt GUI library. It defines functions for opening, saving, and creating new files. It also implements search/replace, font selection, and color settings. The main window contains dockable panels for files, source code, and a web browser. The application loads previous settings and allows opening, editing, and saving text files.
The Ring programming language version 1.8 book - Part 75 of 202Mahmoud Samir Fayed
This document describes the code for a basic notepad application created using the Ring programming language. It defines functions for opening, saving, and editing text files. The application features a menu bar, toolbars, dockable panels for a file tree and text editor, and basic text editing functionality like font selection, find/replace, and print.
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.
The new MongoDB aggregation framework provides a more powerful and performant way to perform data aggregation compared to the existing MapReduce functionality. The aggregation framework uses a pipeline of aggregation operations like $match, $project, $group and $unwind. It allows expressing data aggregation logic through a declarative pipeline in a more intuitive way without needing to write JavaScript code. This provides better performance than MapReduce as it is implemented in C++ rather than JavaScript.
Python Ireland Nov 2010 Talk: Unit TestingPython Ireland
Unit testing for those seeking instant gratification - Maciej Bliziński
Abstract: Unit testing has long term benefits. However, depending on how you use it, it can have short term benefits too. This is an introductory talk, aimed at both beginner and experienced Python programmers who would like to get started testing their code.
MongoDB Analytics: Learn Aggregation by Example - Exploratory Analytics and V...MongoDB
This document discusses analyzing flight data using MongoDB aggregation pipelines and visualization with JSON Studio. It provides examples of aggregation queries to group and calculate statistics on carriers, airports, routes and delays. These include finding the largest carriers, airports with most cancellations, carrier delay statistics, and hub airports. Visualizing the results in JSON Studio is recommended to explore the data further.
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.
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.
The Ring programming language version 1.10 book - Part 81 of 212Mahmoud Samir Fayed
This document describes a cards game application developed using RingQt. The application deals 5 cards to each of two players. Players take turns clicking cards to reveal them. If a card matches another visible card or is a "5", the player earns points and may eat additional matching cards. The game ends when all cards are revealed, and the player with the most points wins. The application displays the cards, scores, and gameplay logic through a graphical user interface built with RingQt widgets.
This document provides an overview of MongoDB aggregation which allows processing data records and returning computed results. It describes some common aggregation pipeline stages like $match, $lookup, $project, and $unwind. $match filters documents, $lookup performs a left outer join, $project selects which fields to pass to the next stage, and $unwind deconstructs an array field. The document also lists other pipeline stages and aggregation pipeline operators for arithmetic, boolean, and comparison expressions.
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?
Attend 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 using it to analyze U.S. census data.
This document discusses various MongoDB aggregation operations including count, distinct, match, limit, sort, project, group, and map reduce. It provides examples of how to use each operation in an aggregation pipeline to count, filter, sort, select fields, compute new fields, group documents, and perform more complex aggregations.
MongoDB .local Toronto 2019: Aggregation Pipeline Power++: How MongoDB 4.2 Pi...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 Chicago 2019: Aggregation Pipeline Power++: How MongoDB 4.2 Pi...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 World 2019: Aggregation Pipeline Power++: How MongoDB 4.2 Pipeline Em...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.
The document describes MongoDB's GridFS specification for storing and retrieving files that exceed the BSON document size limit of 16MB. It explains that GridFS splits files into chunks, which are stored as individual documents, and maintains metadata about the file such as length, MD5, and filename in a separate collection. It provides examples of using the mongofiles command line tool to list, search, put, and get files from GridFS.
This document discusses using MongoDB to analyze user data from a game platform. It includes examples of queries on collections like user_charge, daily_charge, user_trace, and daily_trace to retrieve user activity and purchase data for a given date range. It also shows an example of retrieving user registration information and calculating metrics for user retention and engagement. The document demonstrates how to store and retrieve analytics data from different collections in MongoDB.
Shift Remote FRONTEND: Reactivity in Vue.JS 3 - Marko Boskovic (Barrage)Shift Conference
In previous versions of Vue we needed abstracted patterns like Higher Order Components (HOC), mixins or props to implement reactivity. The new functional API gives us the ability to encapsulate and reuse logic across multiple components without those abstracted patterns.
MongoDB Analytics: Learn Aggregation by Example - Exploratory Analytics and V...MongoDB
This document discusses analyzing flight data using MongoDB aggregation. It provides examples of aggregation pipelines to group, match, project, sort, unwind and other stages. It explores questions about major carriers, airport cancellations, delays by distance and carrier. It also discusses visualizing route data and hub airports. Finally, it proposes a quiz on analyzing NYC flight data by importing data and performing queries on origins, cancellations, delays and weather impacts by month between the three major NYC airports.
Morning with MongoDB Paris 2012 - Accueil et IntroductionsMongoDB
The document outlines the agenda for a MongoDB conference in Paris. It introduces the presenters from 10gen and partner companies. The agenda includes introductions to NoSQL and MongoDB, technical overviews, customer and partner presentations, and a Q&A session. It also discusses how MongoDB provides an operational 'Big Data' solution and can help address the challenges of increasing data complexity, volume, and types that traditional RDBMS struggle with.
This webinar will cover best practices around dev/ops and general operations for those already familiar with basics of MongoDB. Topics will include team roles around data model design, monitoring, hardware configurations, replication and horizontal scaling.
The Ring programming language version 1.7 book - Part 73 of 196Mahmoud Samir Fayed
This document describes the code for a basic notepad application created using the Ring programming language and Qt GUI library. It defines functions for opening, saving, and creating new files. It also implements search/replace, font selection, and color settings. The main window contains dockable panels for files, source code, and a web browser. The application loads previous settings and allows opening, editing, and saving text files.
The Ring programming language version 1.8 book - Part 75 of 202Mahmoud Samir Fayed
This document describes the code for a basic notepad application created using the Ring programming language. It defines functions for opening, saving, and editing text files. The application features a menu bar, toolbars, dockable panels for a file tree and text editor, and basic text editing functionality like font selection, find/replace, and print.
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.
The new MongoDB aggregation framework provides a more powerful and performant way to perform data aggregation compared to the existing MapReduce functionality. The aggregation framework uses a pipeline of aggregation operations like $match, $project, $group and $unwind. It allows expressing data aggregation logic through a declarative pipeline in a more intuitive way without needing to write JavaScript code. This provides better performance than MapReduce as it is implemented in C++ rather than JavaScript.
Python Ireland Nov 2010 Talk: Unit TestingPython Ireland
Unit testing for those seeking instant gratification - Maciej Bliziński
Abstract: Unit testing has long term benefits. However, depending on how you use it, it can have short term benefits too. This is an introductory talk, aimed at both beginner and experienced Python programmers who would like to get started testing their code.
MongoDB Analytics: Learn Aggregation by Example - Exploratory Analytics and V...MongoDB
This document discusses analyzing flight data using MongoDB aggregation pipelines and visualization with JSON Studio. It provides examples of aggregation queries to group and calculate statistics on carriers, airports, routes and delays. These include finding the largest carriers, airports with most cancellations, carrier delay statistics, and hub airports. Visualizing the results in JSON Studio is recommended to explore the data further.
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.
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.
The Ring programming language version 1.10 book - Part 81 of 212Mahmoud Samir Fayed
This document describes a cards game application developed using RingQt. The application deals 5 cards to each of two players. Players take turns clicking cards to reveal them. If a card matches another visible card or is a "5", the player earns points and may eat additional matching cards. The game ends when all cards are revealed, and the player with the most points wins. The application displays the cards, scores, and gameplay logic through a graphical user interface built with RingQt widgets.
This document provides an overview of MongoDB aggregation which allows processing data records and returning computed results. It describes some common aggregation pipeline stages like $match, $lookup, $project, and $unwind. $match filters documents, $lookup performs a left outer join, $project selects which fields to pass to the next stage, and $unwind deconstructs an array field. The document also lists other pipeline stages and aggregation pipeline operators for arithmetic, boolean, and comparison expressions.
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?
Attend 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 using it to analyze U.S. census data.
This document discusses various MongoDB aggregation operations including count, distinct, match, limit, sort, project, group, and map reduce. It provides examples of how to use each operation in an aggregation pipeline to count, filter, sort, select fields, compute new fields, group documents, and perform more complex aggregations.
MongoDB .local Toronto 2019: Aggregation Pipeline Power++: How MongoDB 4.2 Pi...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 Chicago 2019: Aggregation Pipeline Power++: How MongoDB 4.2 Pi...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 World 2019: Aggregation Pipeline Power++: How MongoDB 4.2 Pipeline Em...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.
The document describes MongoDB's GridFS specification for storing and retrieving files that exceed the BSON document size limit of 16MB. It explains that GridFS splits files into chunks, which are stored as individual documents, and maintains metadata about the file such as length, MD5, and filename in a separate collection. It provides examples of using the mongofiles command line tool to list, search, put, and get files from GridFS.
This document discusses using MongoDB to analyze user data from a game platform. It includes examples of queries on collections like user_charge, daily_charge, user_trace, and daily_trace to retrieve user activity and purchase data for a given date range. It also shows an example of retrieving user registration information and calculating metrics for user retention and engagement. The document demonstrates how to store and retrieve analytics data from different collections in MongoDB.
Shift Remote FRONTEND: Reactivity in Vue.JS 3 - Marko Boskovic (Barrage)Shift Conference
In previous versions of Vue we needed abstracted patterns like Higher Order Components (HOC), mixins or props to implement reactivity. The new functional API gives us the ability to encapsulate and reuse logic across multiple components without those abstracted patterns.
MongoDB Analytics: Learn Aggregation by Example - Exploratory Analytics and V...MongoDB
This document discusses analyzing flight data using MongoDB aggregation. It provides examples of aggregation pipelines to group, match, project, sort, unwind and other stages. It explores questions about major carriers, airport cancellations, delays by distance and carrier. It also discusses visualizing route data and hub airports. Finally, it proposes a quiz on analyzing NYC flight data by importing data and performing queries on origins, cancellations, delays and weather impacts by month between the three major NYC airports.
Morning with MongoDB Paris 2012 - Accueil et IntroductionsMongoDB
The document outlines the agenda for a MongoDB conference in Paris. It introduces the presenters from 10gen and partner companies. The agenda includes introductions to NoSQL and MongoDB, technical overviews, customer and partner presentations, and a Q&A session. It also discusses how MongoDB provides an operational 'Big Data' solution and can help address the challenges of increasing data complexity, volume, and types that traditional RDBMS struggle with.
This webinar will cover best practices around dev/ops and general operations for those already familiar with basics of MongoDB. Topics will include team roles around data model design, monitoring, hardware configurations, replication and horizontal scaling.
Bringing Spatial Love to Your Java ApplicationMongoDB
This document discusses bringing spatial capabilities to Java applications using MongoDB. It provides an overview of spatial data and indexing in MongoDB, demonstrating how to [1] store location coordinates in an array or nested objects, and [2] create a 2d index. The document then [2] demonstrates loading sample spatial data, running spatial queries, and accessing the data through web services. It concludes by encouraging developers to use these techniques to build location-based applications on MongoDB and OpenShift.
The document discusses various schema design considerations for MongoDB including modeling one-to-one, one-to-many, and many-to-many relationships. It provides examples of embedding documents versus referencing them with foreign keys. Other topics covered include evolving schemas, indexing, common data patterns such as trees, and handling queues.
MongoDC 2012: Taming Social Media with MongoDBMongoDB
This document summarizes using MongoDB to collect and analyze tweets from social media. It describes setting up MongoDB collections to store tweets collected from Twitter's API, querying the tweets by location, user, and other fields, and building an interface with maps and visualization tools to interact with the tweets. Key steps included collecting tweets from Australia, augmenting the data, indexing for efficient querying, finding the most active tweeter, and lessons learned around data structure and API limitations.
The document provides an overview of an evening event about MongoDB hosted by 10gen in Detroit. The agenda includes an overview of 10gen and MongoDB, MongoDB and big data processing, and MongoDB and Node.js. The document then discusses 10gen, MongoDB's features and advantages over relational databases, use cases, customers, and a few case studies of companies using MongoDB.
The document discusses MongoDB and Spring Data integration. It provides examples of creating, querying, and commenting on documents using Spring Data and MongoDB. It also covers topics like replication, sharding, and eventual consistency in MongoDB.
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
This presentation discusses migrating data from other data stores to MongoDB Atlas. It begins by explaining why MongoDB and Atlas are good choices for data management. Several preparation steps are covered, including sizing the target Atlas cluster, increasing the source oplog, and testing connectivity. Live migration, mongomirror, and dump/restore options are presented for migrating between replicasets or sharded clusters. Post-migration steps like monitoring and backups are also discussed. Finally, migrating from other data stores like AWS DocumentDB, Azure CosmosDB, DynamoDB, and relational databases are briefly covered.
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
The document discusses guidelines for ordering fields in compound indexes to optimize query performance. It recommends the E-S-R approach: placing equality fields first, followed by sort fields, and range fields last. This allows indexes to leverage equality matches, provide non-blocking sorts, and minimize scanning. Examples show how indexes ordered by these guidelines can support queries more efficiently by narrowing the search bounds.
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
The document describes a methodology for data modeling with MongoDB. It begins by recognizing the differences between document and tabular databases, then outlines a three step methodology: 1) describe the workload by listing queries, 2) identify and model relationships between entities, and 3) apply relevant patterns when modeling for MongoDB. The document uses examples around modeling a coffee shop franchise to illustrate modeling approaches and techniques.
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB
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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.
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Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
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Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
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- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
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Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
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Creating a compelling user experience for any software, without the limitations of APIs.
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3. STATE OF AGGREGATION
We're storing our data in MongoDB.
We need to do ad-hoc reporting,
grouping, common aggregations, etc.
What are we using for this?
4. DATA WAREHOUSING
SQL for reporting and analytics
Infrastructure complications
Additional maintenance
Data duplication
ETL processes
Real time?
6. MAPREDUCE IN MONGODB
Implemented with JavaScript
Single-threaded
Difficult to debug
Concurrency
Appearance of parallelism
Write locks (without i l n or j M d )
nie soe
8. AGGREGATION FRAMEWORK
Declared with BSON, executes in C++
Flexible, functional, and simple
Operation pipeline
Computational expressions
Plays nice with sharding
10. PIPELINE
Process a stream of documents
Original input is a collection
Final output is a result document
Series of operators
Filter or transform data
Input/output chain
p x|ge ogd|ha n1
sa rpmno ed‐
11. PIPELINE OPERATORS
$ac
mth
$rjc
poet
$ru
gop
$nid
uwn
$ot
sr
$ii
lmt
$kp
si
12. OUR EXAMPLE DATA
Library Books
{_d 7,
i:35
tte TeGetGtb"
il:"h ra asy,
IB:"715109"
SN 9887513,
aalbe re
vial:tu,
pgs 1,
ae:28
catr:9
hpes ,
sbet:[
ujcs
"ogIln"
Ln sad,
"e ok,
NwYr"
"90"
12s
]
,
lnug:"nls"
agae Egih,
pbihr
ulse:{
ct:"odn,
iy Lno"
nm:"admHue
ae Rno os"
}
}
20. $GROUP
Group documents by an ID
Field reference, object, constant
Other output fields are computed
$a, $i, $v, $u
mx mn ag sm
adoe, ps
$ d T S t$ u h
$is, $at
frt ls
Processes all data in memory
24. $UNWIND
Operate on an array field
Yield new documents for each array element
Array replaced by element value
Missing/empty fields → no output
Non-array fields → error
Pipe to $ r u to aggregate array values
gop
25. $UNWIND
Yielding multiple documents from one
{_d 7,
i:35 ► {$nid $ujcs
uwn:"sbet"}
tte TeGetGtb"
il:"h ra asy,
sbet:[
ujcs
"ogIln"
Ln sad, ▼
"e ok,
NwYr"
"90"
12s {_d 7,
i:35
]
tte TeGetGtb"
il:"h ra asy,
} sbet:"ogIln"
ujcs Ln sad
}
{_d 7,
i:35
tte TeGetGtb"
il:"h ra asy,
sbet:"e ok
ujcs NwYr"
}
{_d 7,
i:35
tte TeGetGtb"
il:"h ra asy,
sbet:"90"
ujcs 12s
}
26. $SORT, $LIMIT, $SKIP
Sort documents by one or more fields
Same order syntax as cursors
Waits for earlier pipeline operator to return
In-memory unless early and indexed
Limit and skip follow cursor behavior
27. $SORT
Sort all documents in the pipeline
{tte TeGetGtb"}
il:"h ra asy ► {$ot il:1}
sr:{tte }
{tte BaeNwWrd
il:"rv e ol"}
▼
{tte TeGae fWah
il:"h rpso rt"}
{tte Aia am
il:"nmlFr"}
{tte Aia am
il:"nmlFr"}
{tte BaeNwWrd
il:"rv e ol"}
{tte Lr fteFis
il:"odo h le"}
{tte Fhehi 5"}
il:"arnet41
{tte FtesadSn"}
il:"ahr n os
{tte FtesadSn"}
il:"ahr n os
{tte IvsbeMn
il:"niil a"}
{tte IvsbeMn
il:"niil a"}
{tte Fhehi 5"}
il:"arnet41
{tte Lr fteFis
il:"odo h le"}
{tte TeGae fWah
il:"h rpso rt"}
{tte TeGetGtb"}
il:"h ra asy
28. $LIMIT
Limit documents through the pipeline
{tte Aia am
il:"nmlFr"} ► {$ii:5}
lmt
{tte BaeNwWrd
il:"rv e ol"}
▼
{tte Fhehi 5"}
il:"arnet41
{tte Aia am
il:"nmlFr"}
{tte FtesadSn"}
il:"ahr n os
{tte BaeNwWrd
il:"rv e ol"}
{tte IvsbeMn
il:"niil a"}
{tte Fhehi 5"}
il:"arnet41
{tte Lr fteFis
il:"odo h le"}
{tte FtesadSn"}
il:"ahr n os
{tte TeGae fWah
il:"h rpso rt"}
{tte IvsbeMn
il:"niil a"}
{tte TeGetGtb"}
il:"h ra asy
29. $SKIP
Skip over documents in the pipeline
{tte Aia am
il:"nmlFr"} ► {$kp
si:2}
{tte BaeNwWrd
il:"rv e ol"}
▼
{tte Fhehi 5"}
il:"arnet41
{tte Fhehi 5"}
il:"arnet41
{tte FtesadSn"}
il:"ahr n os
{tte FtesadSn"}
il:"ahr n os
{tte IvsbeMn
il:"niil a"}
{tte IvsbeMn
il:"niil a"}
30. EXPRESSIONS
State of Aggregation
Pipeline
Expressions
Usage and Limitations
Sharding
Looking Ahead
32. EXPRESSIONS
Logic Comparison
$ n , $ r$ o …
ad o, nt $ m , $ q$ t
cp e, g…
Arithmetic String
$d, $iie
ad dvd… sraem, sbt…
$ t c s c p$ u s r
Date Conditional
ya, dyfot…
$ e r$ a O M n h cn, iNl…
$ o d$ f u l
39. SHARDING
Split the pipeline at first $ r u or $ o t
gop sr
Shards execute pipeline up to that point
mongos merges results and continues
Early $ a c may excuse shards
mth
CPU and memory implications for mongos
42. LOOKING AHEAD
State of Aggregation
Pipeline
Expressions
Usage and Limitations
Sharding
Looking Ahead
43. FRAMEWORK USE CASES
Basic aggregation queries
Ad-hoc reporting
Real-time analytics
Visualizing time series data
44. EXTENDING THE FRAMEWORK
Adding new pipeline operators, expressions
w t i expression for $ a c
$ihn mth
$ e N a pipeline operator
goer
$ u operator for output control
ot
45. FUTURE ENHANCEMENTS
Improved handling of n l values
ul
Optimizing $ a c position
mth
Pipeline explain facility
Support BSON binary, code, etc.
Memory usage improvements
Grouping input sorted by _ d
i
Sorting with limited output (top k)
46. ENABLING DEVELOPERS
Doing more within MongoDB, faster
Refactoring MapReduce and groupings
Replace pages of JavaScript
Longer aggregation pipelines
Quick aggregations from the shell