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: 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 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.
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.
Speaker: Asya Kamsky
MongoDB Aggregation Language has been getting more powerful and expressive with every release. In this talk we'll review how to create powerful aggregation pipelines and how to leverage aggregation expressions in your queries.
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 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.
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.
Speaker: Asya Kamsky
MongoDB Aggregation Language has been getting more powerful and expressive with every release. In this talk we'll review how to create powerful aggregation pipelines and how to leverage aggregation expressions in your queries.
Aggregation Pipeline Power++: MongoDB 4.2 파이프 라인 쿼리, 업데이트 및 구체화된 뷰 소개 [MongoDB]MongoDB
MongoDB 2.2 이후 집계 파이프라인을 통한 데이터 분석을 강화하고 있습니다.
버전 4.2 에서는 더 많은 기능을 추가 했으며, 더 강력한 쿼리 및 업데이트 그리고 MView 기능까지 사용 할 수
있습니다. 집계파이프 라인을 포함한 해당 기능을 이용하여 단일뷰(SignleView), ETL, 데이터 롤업 및 MView 수행하는 방법을 설명합니다.
MongoDB .local Paris 2020: La puissance du Pipeline d'Agrégation de MongoDBMongoDB
Le pipeline d'agrégation a été en mesure d'alimenter votre analyse de données depuis la version 2.2. Dans la version 4.2, nous avons ajouté plus de puissance et vous pouvez maintenant l'utiliser pour des requêtes plus puissantes, des mises à jour et la sortie de vos données dans des collections existantes. Venez découvrir comment vous pouvez tout faire avec le pipeline, y compris les vues uniques, ETL, les cumuls de données et les vues matérialisées.
My presentation for Scala Days Amsterdam.
How to make a compile time string interpolator for a language you have? Use case and step by step code examples.
Powerful Analysis with the Aggregation PipelineMongoDB
Speaker: Asya Kamsky
Think you need to move your data "elsewhere" to do powerful analysis? Think again. The most efficient way to analyze your data is where it already lives. MongoDB Aggregation Pipeline has been getting more and more powerful and using new stages, expressions and tricks we can do extensive analysis of our data inside MongoDB Server.
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.
MongoDB Europe 2016 - Advanced MongoDB Aggregation PipelinesMongoDB
We will do a deep dive into the powerful query capabilities of MongoDB's Aggregation Framework, and show you how you can use MongoDB's built-in features to inspect the execution and tune the performance of your queries. And, last but not least, we will also give you a brief outlook into MongoDB 3.4's awesome new Aggregation Framework additions.
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.
MongoDB .local Toronto 2019: Tips and Tricks for Effective IndexingMongoDB
Query performance can either be a constant headache or the unsung hero of an application. MongoDB provides extremely powerful querying capabilities when used properly. I will share more common mistakes observed and some tips and tricks to avoiding them.
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.
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.
MongoDB Europe 2016 - Debugging MongoDB PerformanceMongoDB
Asya is back, and so is Sherlock Holmes and his techniques to gather and analyze data from your poorly performing MongoDB clusters. In this advanced talk we take a deep look at all the diagnostic data that lives inside MongoDB - how to interrogate and interpret it to help you solve those frustrating performance bottlenecks that we all face occasionally.
All Things Open 2016 -- Database Programming for NewbiesDave Stokes
This presentation covers much a new developer needs to know about working WITH a database instead of against it. Plus there is much on what goes on behind the scenes when you submit a query and hints on how to avoid the big problems that can ruin your data
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.
Aggregation Pipeline Power++: MongoDB 4.2 파이프 라인 쿼리, 업데이트 및 구체화된 뷰 소개 [MongoDB]MongoDB
MongoDB 2.2 이후 집계 파이프라인을 통한 데이터 분석을 강화하고 있습니다.
버전 4.2 에서는 더 많은 기능을 추가 했으며, 더 강력한 쿼리 및 업데이트 그리고 MView 기능까지 사용 할 수
있습니다. 집계파이프 라인을 포함한 해당 기능을 이용하여 단일뷰(SignleView), ETL, 데이터 롤업 및 MView 수행하는 방법을 설명합니다.
MongoDB .local Paris 2020: La puissance du Pipeline d'Agrégation de MongoDBMongoDB
Le pipeline d'agrégation a été en mesure d'alimenter votre analyse de données depuis la version 2.2. Dans la version 4.2, nous avons ajouté plus de puissance et vous pouvez maintenant l'utiliser pour des requêtes plus puissantes, des mises à jour et la sortie de vos données dans des collections existantes. Venez découvrir comment vous pouvez tout faire avec le pipeline, y compris les vues uniques, ETL, les cumuls de données et les vues matérialisées.
My presentation for Scala Days Amsterdam.
How to make a compile time string interpolator for a language you have? Use case and step by step code examples.
Powerful Analysis with the Aggregation PipelineMongoDB
Speaker: Asya Kamsky
Think you need to move your data "elsewhere" to do powerful analysis? Think again. The most efficient way to analyze your data is where it already lives. MongoDB Aggregation Pipeline has been getting more and more powerful and using new stages, expressions and tricks we can do extensive analysis of our data inside MongoDB Server.
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.
MongoDB Europe 2016 - Advanced MongoDB Aggregation PipelinesMongoDB
We will do a deep dive into the powerful query capabilities of MongoDB's Aggregation Framework, and show you how you can use MongoDB's built-in features to inspect the execution and tune the performance of your queries. And, last but not least, we will also give you a brief outlook into MongoDB 3.4's awesome new Aggregation Framework additions.
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.
MongoDB .local Toronto 2019: Tips and Tricks for Effective IndexingMongoDB
Query performance can either be a constant headache or the unsung hero of an application. MongoDB provides extremely powerful querying capabilities when used properly. I will share more common mistakes observed and some tips and tricks to avoiding them.
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.
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.
MongoDB Europe 2016 - Debugging MongoDB PerformanceMongoDB
Asya is back, and so is Sherlock Holmes and his techniques to gather and analyze data from your poorly performing MongoDB clusters. In this advanced talk we take a deep look at all the diagnostic data that lives inside MongoDB - how to interrogate and interpret it to help you solve those frustrating performance bottlenecks that we all face occasionally.
All Things Open 2016 -- Database Programming for NewbiesDave Stokes
This presentation covers much a new developer needs to know about working WITH a database instead of against it. Plus there is much on what goes on behind the scenes when you submit a query and hints on how to avoid the big problems that can ruin your data
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.
This describes a Functional Programming approach to computing AWS Glacier "tree hash" values, hiding the tail-call elimination in Perl5 with a keyword and also shows how to accomplish the same result in Perl6.
This was the talk actually given at YAPC::NA 2016 by Dr. Conway and myself.
Speaker: André Spiegel
Many applications require processes that load large amounts of data into MongoDB. It is easy to get these processes wrong, resulting in hours or days of loading time when it could be done in minutes. This talk identifies common mistakes and pitfalls and shows design patterns that can dramatically improve performance. The patterns introduced here can be used with any tool or programming language.
Webinar: Back to Basics: Thinking in DocumentsMongoDB
New applications, users and inputs demand new types of data, like unstructured, semi-structured and polymorphic data. Adopting MongoDB means adopting to a new, document-based data model.
While most developers have internalized the rules of thumb for designing schemas for relational databases, these rules don't apply to MongoDB. Documents can represent rich data structures, providing lots of viable alternatives to the standard, normalized, relational model. In addition, MongoDB has several unique features, such as atomic updates and indexed array keys, that greatly influence the kinds of schemas that make sense.
In this session, Buzz Moschetti explores how you can take advantage of MongoDB's document model to build modern applications.
MongoDB World 2019: Exploring your MongoDB Data with Pirates (R) and Snakes (...MongoDB
Does exploring data excite you? Do you use Python or R as your language of choice for data analysis? Does your job title include the term Data Analyst? If you answered yes to any of those questions, then the Exploring Your MongoDB Data with Pirates and Snakes is the session for you! MongoDB Developer Advocate Ken Alger will show the suggested method for using dataframe structures in R and Python with your MongoDB data. He’ll show the code for best practices in both languages to move your array based MongoDB data into the popular fast, flexible, and expressive dataframes used for data analysis in these prominent programming languages.
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.
In the Friday training I am going to introduce CouchBase Server, a NoSQL (Not only SQL, document oriented) database and outline differences to Apache CouchDB.
We're going through a quick setup of the CouchBase Server and the basics of CouchDB document design. I will show some real world examples, followed by a discussion.
Who is Patrick Heneise?
Patrick is the Founder & CEO of desentia. He achieved a MSc in Media Technology and BSc in Computer Science in Media. Ever since he has improved social interaction and media with creative and professional technology solutions. He started his first business in 2006 during his studies in the fields of eLearning and web technologies and worked for various companies and universities in eCommerce, telecommunications and research & development.
MongoDB Aggregations Indexing and ProfilingManish Kapoor
This deck consists of following operations in MongoDB: aggregation through aggregation pipeline, map reduce, operations, indexes and profiling of slow queries.
This presentation is showing how to use the Aggregation Framework, the powerful aggregation language of MongoDB. Using some real data coming from the USA Census, we will discover the most important operations.
Similar to MongoDB .local Bengaluru 2019: Aggregation Pipeline Power++: How MongoDB 4.2 Pipeline Empowers Queries, Updates, and Materialized Views (20)
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
During this talk we'll navigate through a customer's journey as they migrate an existing MongoDB deployment to MongoDB Atlas. While the migration itself can be as simple as a few clicks, the prep/post effort requires due diligence to ensure a smooth transfer. We'll cover these steps in detail and provide best practices. In addition, we’ll provide an overview of what to consider when migrating other cloud data stores, traditional databases and MongoDB imitations to MongoDB Atlas.
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB
MongoDB Kubernetes operator and MongoDB Open Service Broker are ready for production operations. Learn about how MongoDB can be used with the most popular container orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications. A demo will show you how easy it is to enable MongoDB clusters as an External Service using the Open Service Broker API for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB
Humana, like many companies, is tackling the challenge of creating real-time insights from data that is diverse and rapidly changing. This is our journey of how we used MongoDB to combined traditional batch approaches with streaming technologies to provide continues alerting capabilities from real-time data streams.
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe.
This talk covers:
Common components of an IoT solution
The challenges involved with managing time-series data in IoT applications
Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance.
How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts
At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB
Our clients have unique use cases and data patterns that mandate the choice of a particular strategy. To implement these strategies, it is mandatory that we unlearn a lot of relational concepts while designing and rapidly developing efficient applications on NoSQL. In this session, we will talk about some of our client use cases, the strategies we have adopted, and the features of MongoDB that assisted in implementing these strategies.
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB
Encryption is not a new concept to MongoDB. Encryption may occur in-transit (with TLS) and at-rest (with the encrypted storage engine). But MongoDB 4.2 introduces support for Client Side Encryption, ensuring the most sensitive data is encrypted before ever leaving the client application. Even full access to your MongoDB servers is not enough to decrypt this data. And better yet, Client Side Encryption can be enabled at the "flick of a switch".
This session covers using Client Side Encryption in your applications. This includes the necessary setup, how to encrypt data without sacrificing queryability, and what trade-offs to expect.
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB
MongoDB Kubernetes operator is ready for prime-time. Learn about how MongoDB can be used with most popular orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications.
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
When you need to model data, is your first instinct to start breaking it down into rows and columns? Mine used to be too. When you want to develop apps in a modern, agile way, NoSQL databases can be the best option. Come to this talk to learn how to take advantage of all that NoSQL databases have to offer and discover the benefits of changing your mindset from the legacy, tabular way of modeling data. We’ll compare and contrast the terms and concepts in SQL databases and MongoDB, explain the benefits of using MongoDB compared to SQL databases, and walk through data modeling basics so you feel confident as you begin using MongoDB.
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB
Query performance should be the unsung hero of an application, but without proper configuration, can become a constant headache. When used properly, MongoDB provides extremely powerful querying capabilities. In this session, we'll discuss concepts like equality, sort, range, managing query predicates versus sequential predicates, and best practices to building multikey indexes.
MongoDB .local San Francisco 2020: 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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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
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
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
17. #MDBLocal
THE FUTURE OF AGGREGATION
Better performance & optimizations
More stages & expressions
More options for output
Compass helper for aggregate
Unify different languages
18. #MDBLocal
THE FUTURE OF AGGREGATION
Better performance & optimizations
More stages & expressions
More options for output
Compass helper for aggregate
Unify different languages
19. #MDBLocal
THE FUTURE OF AGGREGATION
Better performance & optimizations
More stages & expressions
More options for output
Compass helper for aggregate
Unify different languages
51. #MDBLocal
Set Defaults
{_id: 1, a: 5, b: 12}
{_id: 2, a: 15, c: "abc"}
{_id: 3, b: 99, c: "xyz"}
If a or b are missing, set to 0, if c is missing -> "unset"
52. #MDBLocal
Set Defaults
{_id: 1, a: 5, b: 12}
{_id: 2, a: 15, c: "abc"}
{_id: 3, b: 99, c: "xyz"}
If a or b are missing, set to 0, if c is missing -> "unset"
db.coll.update({}, [
{$replaceWith:{
}}
], {multi:true})
53. #MDBLocal
Set Defaults
{_id: 1, a: 5, b: 12}
{_id: 2, a: 15, c: "abc"}
{_id: 3, b: 99, c: "xyz"}
If a or b are missing, set to 0, if c is missing -> "unset"
db.coll.update({}, [
{$replaceWith:{$mergeObjects:[
]}}
], {multi:true})
54. #MDBLocal
Set Defaults
{_id: 1, a: 5, b: 12}
{_id: 2, a: 15, c: "abc"}
{_id: 3, b: 99, c: "xyz"}
If a or b are missing, set to 0, if c is missing -> "unset"
db.coll.update({}, [
{$replaceWith:{$mergeObjects:[
{ a:0, b:0, c:"unset" },
"$$ROOT"
]}}
], {multi:true})
55. #MDBLocal
Set Defaults
{_id: 1, a: 5, b: 12}
{_id: 2, a: 15, c: "abc"}
{_id: 3, b: 99, c: "xyz"}
If a or b are missing, set to 0, if c is missing -> "unset"
db.coll.update({}, [
{$replaceWith:{$mergeObjects:[
{ a:0, b:0, c:"unset" },
"$$ROOT"
]}}
], {multi:true})
{_id: 1, a: 5, b: 12, c: "unset"}
{_id: 2, a: 15, b: 0, c: "abc"}
{_id: 3, a: 0, b: 99, c: "xyz"}
58. #MDBLocal
Recap:
Updates can be specified with aggregation pipeline
All fields from existing document can be accessed
Slightly slower, but a lot more powerful
59. #MDBLocal
THE FUTURE OF AGGREGATION
Better performance & optimizations
More stages & expressions
More options for output
Compass helper for aggregate
Unify different languages
60. #MDBLocal
THE FUTURE OF AGGREGATION
Better performance & optimizations
More stages & expressions
More options for output
Compass helper for aggregate
Unify different languages
107. #MDBLocal
aggregate 'temp' and append valid records to 'data'
db.temp.aggregate( [
{ ... } /* pipeline to massage and cleanse data in temp */,
{$merge:{
into: "data",
whenMatched: "fail"
}}
]);
108. #MDBLocal
aggregate 'temp' and append valid records to 'data'
db.temp.aggregate( [
{ ... } /* pipeline to massage and cleanse data in temp */,
{$merge:{
into: "data",
whenMatched: "fail"
}}
]);
Similar to SQL's INSERT INTO T1 SELECT * from T2