MongoDB and Hadoop can work together to solve big data problems facing today's enterprises. We will take an in-depth look at how the two technologies complement and enrich each other with complex analyses and greater intelligence. We will take a deep dive into the MongoDB Connector for Hadoop and how it can be applied to enable new business insights with MapReduce, Pig, and Hive, and demo a Spark application to drive product recommendations.
The MongoDB Spark Connector integrates MongoDB and Apache Spark, providing users with the ability to process data in MongoDB with the massive parallelism of Spark. The connector gives users access to Spark's streaming capabilities, machine learning libraries, and interactive processing through the Spark shell, Dataframes and Datasets. We'll take a tour of the connector with a focus on practical use of the connector, and run a demo using both Spark and MongoDB for data processing.
Modern architectures are moving away from a "one size fits all" approach. We are well aware that we need to use the best tools for the job. Given the large selection of options available today, chances are that you will end up managing data in MongoDB for your operational workload and with Spark for your high speed data processing needs.
Description: When we model documents or data structures there are some key aspects that need to be examined not only for functional and architectural purposes but also to take into consideration the distribution of data nodes, streaming capabilities, aggregation and queryability options and how we can integrate the different data processing software, like Spark, that can benefit from subtle but substantial model changes. A clear example is when embedding or referencing documents and their implications on high speed processing.
Over the course of this talk we will detail the benefits of a good document model for the operational workload. As well as what type of transformations we should incorporate in our document model to adjust for the high speed processing capabilities of Spark.
We will look into the different options that we have to connect these two different systems, how to model according to different workloads, what kind of operators we need to be aware of for top performance and what kind of design and architectures we should put in place to make sure that all of these systems work well together.
Over the course of the talk we will showcase different libraries that enable the integration between spark and MongoDB, such as MongoDB Hadoop Connector, Stratio Connector and MongoDB Spark Native Connector.
By the end of the talk I expect the attendees to have an understanding of:
How they connect their MongoDB clusters with Spark
Which use cases show a net benefit for connecting these two systems
What kind of architecture design should be considered for making the most of Spark + MongoDB
How documents can be modeled for better performance and operational process, while processing these data sets stored in MongoDB.
The talk is suitable for:
Developers that want to understand how to leverage Spark
Architects that want to integrate their existing MongoDB cluster and have real time high speed processing needs
Data scientists that know about Spark, are playing with Spark and want to integrate with MongoDB for their persistency layer
The integration between Spring Framework and MongoDB tends to be somewhat unknown. This presentation shows the different projects that compose Spring ecosystem, Springdata, Springboot, SpringIO etc and how to merge between the pure JAVA projects to massive enterprise systems that require the interaction of these systems together.
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.
The MongoDB Spark Connector integrates MongoDB and Apache Spark, providing users with the ability to process data in MongoDB with the massive parallelism of Spark. The connector gives users access to Spark's streaming capabilities, machine learning libraries, and interactive processing through the Spark shell, Dataframes and Datasets. We'll take a tour of the connector with a focus on practical use of the connector, and run a demo using both Spark and MongoDB for data processing.
Modern architectures are moving away from a "one size fits all" approach. We are well aware that we need to use the best tools for the job. Given the large selection of options available today, chances are that you will end up managing data in MongoDB for your operational workload and with Spark for your high speed data processing needs.
Description: When we model documents or data structures there are some key aspects that need to be examined not only for functional and architectural purposes but also to take into consideration the distribution of data nodes, streaming capabilities, aggregation and queryability options and how we can integrate the different data processing software, like Spark, that can benefit from subtle but substantial model changes. A clear example is when embedding or referencing documents and their implications on high speed processing.
Over the course of this talk we will detail the benefits of a good document model for the operational workload. As well as what type of transformations we should incorporate in our document model to adjust for the high speed processing capabilities of Spark.
We will look into the different options that we have to connect these two different systems, how to model according to different workloads, what kind of operators we need to be aware of for top performance and what kind of design and architectures we should put in place to make sure that all of these systems work well together.
Over the course of the talk we will showcase different libraries that enable the integration between spark and MongoDB, such as MongoDB Hadoop Connector, Stratio Connector and MongoDB Spark Native Connector.
By the end of the talk I expect the attendees to have an understanding of:
How they connect their MongoDB clusters with Spark
Which use cases show a net benefit for connecting these two systems
What kind of architecture design should be considered for making the most of Spark + MongoDB
How documents can be modeled for better performance and operational process, while processing these data sets stored in MongoDB.
The talk is suitable for:
Developers that want to understand how to leverage Spark
Architects that want to integrate their existing MongoDB cluster and have real time high speed processing needs
Data scientists that know about Spark, are playing with Spark and want to integrate with MongoDB for their persistency layer
The integration between Spring Framework and MongoDB tends to be somewhat unknown. This presentation shows the different projects that compose Spring ecosystem, Springdata, Springboot, SpringIO etc and how to merge between the pure JAVA projects to massive enterprise systems that require the interaction of these systems together.
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 and Hadoop work powerfully together as complementary technologies. Learn how the Hadoop connector allows you to use the power of MapReduce to process data sourced from your MongoDB cluster.
How Thermo Fisher Is Reducing Mass Spectrometry Experiment Times from Days to...MongoDB
Mass spectrometry is the gold standard for determining chemical compositions, with spectrometers often measuring the mass of a compound down to a single electron. This level of granularity produces an enormous amount of hierarchical data that doesn't fit well into rows and columns. In this talk, learn how Thermo Fisher is using MongoDB Atlas on AWS to allow their users to get near real-time insights from mass spectrometry experiments—a process that used to take days. We also share how the underlying database service used by Thermo Fisher was built on AWS.
MongoDB .local Munich 2019: Managing a Heterogeneous Stack with MongoDB & SQLMongoDB
Data administrators face the challenge of integrating disparate data technologies into a cohesive and performant data platform. This is especially true when using diverse query languages and protocols. This session will focus on how to integrate SQL-aware applications into a MongoDB data platform.
Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Du...NoSQLmatters
Data analysis is an exploratory process that requires a variety of tools and a flexible data store. Data analysis projects are easy to start but quickly become difficult to manage and error prone when depending on file-based data storage. Relational databases are poorly equipped to accommodate the dynamic demands complex analysis. This talk describes best practices for using MongoDB for analytics projects. Examples will be drawn from a large scale text mining project (approximately 25 million documents) that applies machine learning (neural networks and support vector machines) and statistical analysis. Tools discussed include R, Spark, Python scientific stack, and custom pre-processing scripts but the focus is on using these with the document database.
Webinar: Live Data Visualisation with Tableau and MongoDBMongoDB
MongoDB 3.2 introduces a new way for familiar Business Intelligence (BI) tools to access your real-time operational data – opening it up to data analysts and data scientist, enabling new insights to be discovered faster than ever before. Tableau accesses the JSON document data stored in MongoDB via this new BI connector. We will cover how the BI connector works by creating a relational view definition of a JSON data set that is then used to present a tabular SQL/ODBC interface to Tableau. Then we will set-up a live connection from Tableau Desktop to the MongoDB Connector for BI. Once we have Tableau Desktop and MongoDB connected, we will demonstrate the visual power of Tableau to explore the agile data storage of MongoDB. This webinar will cover:
What is the MongoDB BI Connector?
Setting up a connection from Tableau to the MongoDB BI Connector.
How to perform data discovery Tableau connected to MongoDB live data.
Publishing a Tableau Dashboard for sharing insights.
MongoDB Schema Design: Practical Applications and ImplicationsMongoDB
Presented by Austin Zellner, Solutions Architect, MongoDB
Schema design is as much art as it is science, but it is central to understanding how to get the most out of MongoDB. Attendees will walk away with an understanding of how to approach schema design, what influences it, and the science behind the art. After this session, attendees will be ready to design new schemas, as well as re-evaluate existing schemas with a new mental model.
Are you in the process of evaluating or migrating to MongoDB? We will cover key aspects of migrating to MongoDB from a RDBMS, including Schema design, Indexing strategies, Data migration approaches as your implementation reaches various SDLC stages, Achieving operational agility through MongoDB Management Services (MMS).
Intro to MongoDB
Get a jumpstart on MongoDB, use cases, and next steps for building your first app with Buzz Moschetti, MongoDB Enterprise Architect.
@BuzzMoschetti
Webinar: Schema Patterns and Your Storage EngineMongoDB
How do MongoDB’s different storage options change the way you model your data?
Each storage engine, WiredTiger, the In-Memory Storage engine, MMAP V1 and other community supported drivers, persists data differently, writes data to disk in different formats and handles memory resources in different ways.
This webinar will go through how to design applications around different storage engines based on your use case and data access patterns. We will be looking into concrete examples of schema design practices that were previously applied on MMAPv1 and whether those practices still apply, to other storage engines like WiredTiger.
Topics for review: Schema design patterns and strategies, real-world examples, sizing and resource allocation of infrastructure.
MongoDB has taken a clear lead in adoption among the new generation of databases, including the enormous variety of NoSQL offerings. A key reason for this lead has been a unique combination of agility and scalability. Agility provides business units with a quick start and flexibility to maintain development velocity, despite changing data and requirements. Scalability maintains that flexibility while providing fast, interactive performance as data volume and usage increase. We'll address the key organizational, operational, and engineering considerations to ensure that agility and scalability stay aligned at increasing scale, from small development instances to web-scale applications. We will also survey some key examples of highly-scaled customer applications of MongoDB.
MongoDB and Hadoop work powerfully together as complementary technologies. Learn how the Hadoop connector allows you to use the power of MapReduce to process data sourced from your MongoDB cluster.
How Thermo Fisher Is Reducing Mass Spectrometry Experiment Times from Days to...MongoDB
Mass spectrometry is the gold standard for determining chemical compositions, with spectrometers often measuring the mass of a compound down to a single electron. This level of granularity produces an enormous amount of hierarchical data that doesn't fit well into rows and columns. In this talk, learn how Thermo Fisher is using MongoDB Atlas on AWS to allow their users to get near real-time insights from mass spectrometry experiments—a process that used to take days. We also share how the underlying database service used by Thermo Fisher was built on AWS.
MongoDB .local Munich 2019: Managing a Heterogeneous Stack with MongoDB & SQLMongoDB
Data administrators face the challenge of integrating disparate data technologies into a cohesive and performant data platform. This is especially true when using diverse query languages and protocols. This session will focus on how to integrate SQL-aware applications into a MongoDB data platform.
Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Du...NoSQLmatters
Data analysis is an exploratory process that requires a variety of tools and a flexible data store. Data analysis projects are easy to start but quickly become difficult to manage and error prone when depending on file-based data storage. Relational databases are poorly equipped to accommodate the dynamic demands complex analysis. This talk describes best practices for using MongoDB for analytics projects. Examples will be drawn from a large scale text mining project (approximately 25 million documents) that applies machine learning (neural networks and support vector machines) and statistical analysis. Tools discussed include R, Spark, Python scientific stack, and custom pre-processing scripts but the focus is on using these with the document database.
Webinar: Live Data Visualisation with Tableau and MongoDBMongoDB
MongoDB 3.2 introduces a new way for familiar Business Intelligence (BI) tools to access your real-time operational data – opening it up to data analysts and data scientist, enabling new insights to be discovered faster than ever before. Tableau accesses the JSON document data stored in MongoDB via this new BI connector. We will cover how the BI connector works by creating a relational view definition of a JSON data set that is then used to present a tabular SQL/ODBC interface to Tableau. Then we will set-up a live connection from Tableau Desktop to the MongoDB Connector for BI. Once we have Tableau Desktop and MongoDB connected, we will demonstrate the visual power of Tableau to explore the agile data storage of MongoDB. This webinar will cover:
What is the MongoDB BI Connector?
Setting up a connection from Tableau to the MongoDB BI Connector.
How to perform data discovery Tableau connected to MongoDB live data.
Publishing a Tableau Dashboard for sharing insights.
MongoDB Schema Design: Practical Applications and ImplicationsMongoDB
Presented by Austin Zellner, Solutions Architect, MongoDB
Schema design is as much art as it is science, but it is central to understanding how to get the most out of MongoDB. Attendees will walk away with an understanding of how to approach schema design, what influences it, and the science behind the art. After this session, attendees will be ready to design new schemas, as well as re-evaluate existing schemas with a new mental model.
Are you in the process of evaluating or migrating to MongoDB? We will cover key aspects of migrating to MongoDB from a RDBMS, including Schema design, Indexing strategies, Data migration approaches as your implementation reaches various SDLC stages, Achieving operational agility through MongoDB Management Services (MMS).
Intro to MongoDB
Get a jumpstart on MongoDB, use cases, and next steps for building your first app with Buzz Moschetti, MongoDB Enterprise Architect.
@BuzzMoschetti
Webinar: Schema Patterns and Your Storage EngineMongoDB
How do MongoDB’s different storage options change the way you model your data?
Each storage engine, WiredTiger, the In-Memory Storage engine, MMAP V1 and other community supported drivers, persists data differently, writes data to disk in different formats and handles memory resources in different ways.
This webinar will go through how to design applications around different storage engines based on your use case and data access patterns. We will be looking into concrete examples of schema design practices that were previously applied on MMAPv1 and whether those practices still apply, to other storage engines like WiredTiger.
Topics for review: Schema design patterns and strategies, real-world examples, sizing and resource allocation of infrastructure.
MongoDB has taken a clear lead in adoption among the new generation of databases, including the enormous variety of NoSQL offerings. A key reason for this lead has been a unique combination of agility and scalability. Agility provides business units with a quick start and flexibility to maintain development velocity, despite changing data and requirements. Scalability maintains that flexibility while providing fast, interactive performance as data volume and usage increase. We'll address the key organizational, operational, and engineering considerations to ensure that agility and scalability stay aligned at increasing scale, from small development instances to web-scale applications. We will also survey some key examples of highly-scaled customer applications of MongoDB.
Bobby Evans and Tom Graves, the engineering leads for Spark and Storm development at Yahoo will talk about how these technologies are used on Yahoo's grids and reasons why to use one or the other.
Bobby Evans is the low latency data processing architect at Yahoo. He is a PMC member on many Apache projects including Storm, Hadoop, Spark, and Tez. His team is responsible for delivering Storm as a service to all of Yahoo and maintaining Spark on Yarn for Yahoo (Although Tom really does most of that work).
Tom Graves a Senior Software Engineer on the Platform team at Yahoo. He is an Apache PMC member on Hadoop, Spark, and Tez. His team is responsible for delivering and maintaining Spark on Yarn for Yahoo.
Apache Storm 0.9 basic training - VerisignMichael Noll
Apache Storm 0.9 basic training (130 slides) covering:
1. Introducing Storm: history, Storm adoption in the industry, why Storm
2. Storm core concepts: topology, data model, spouts and bolts, groupings, parallelism
3. Operating Storm: architecture, hardware specs, deploying, monitoring
4. Developing Storm apps: Hello World, creating a bolt, creating a topology, running a topology, integrating Storm and Kafka, testing, data serialization in Storm, example apps, performance and scalability tuning
5. Playing with Storm using Wirbelsturm
Audience: developers, operations, architects
Created by Michael G. Noll, Data Architect, Verisign, https://www.verisigninc.com/
Verisign is a global leader in domain names and internet security.
Tools mentioned:
- Wirbelsturm (https://github.com/miguno/wirbelsturm)
- kafka-storm-starter (https://github.com/miguno/kafka-storm-starter)
Blog post at:
http://www.michael-noll.com/blog/2014/09/15/apache-storm-training-deck-and-tutorial/
Many thanks to the Twitter Engineering team (the creators of Storm) and the Apache Storm open source community!
MongoDB and Hadoop: Driving Business InsightsMongoDB
MongoDB and Hadoop can work together to solve big data problems facing today's enterprises. We will take an in-depth look at how the two technologies complement and enrich each other with complex analyses and greater intelligence. We will take a deep dive into the MongoDB Connector for Hadoop and how it can be applied to enable new business insights with MapReduce, Pig, and Hive, and demo a Spark application to drive product recommendations.
Mongo db and hadoop driving business insights - finalMongoDB
MongoDB and Hadoop can work together to solve big data problems facing today's enterprises. We will take an in-depth look at how the two technologies complement and enrich each other with complex analyses and greater intelligence. We will take a deep dive into the MongoDB Connector for Hadoop and how it can be applied to enable new business insights with MapReduce, Pig, and Hive, and demo a Spark application to drive product recommendations.
Webinar: MongoDB and Hadoop - Working Together to provide Business InsightsMongoDB
Join us for a webinar on how MongoDB and Hadoop can work together to solve Big Data problems in today's enterprises. We will take an in depth look at how the two technologies make real business intelligence accessible to end users. After a brief introduction to both technologies, this webinar will dive deep into the MongoDB+Hadoop Connector and how it is applied to enable new business insights.
In this webinar you will learn:
What information problems are a good fit for MongoDB and Hadoop
How to integrate the two technologies using the MongoDB+Hadoop Connector
Programming paradigms for tackling common problems
MongoDB Days Germany: Data Processing with MongoDBMongoDB
Presented by Marc Schwering, Senior Solutions Architect, MongoDB
Modern architectures are moving away from "one size fits all" solutions. The best tools need to be put to the job and given the large amounts of options today, chances are that you’ll end up using MongoDB for your operational workload, as well as Data Processing Systems like Apache Flink or Spark for your high speed data processing needs. When documents or data structures are modeled, there are some key aspects that need to be attended. This takes into consideration the distribution of data nodes, streaming capabilities, performance, aggregation, and queryability options, and how we can integrate the different data processing software that can benefit from subtle but substantial model changes. This session will cover the way how you enhance your architecture using data processing technologies such as Apache Flink and Spark. It will take the audience through the evolution of an app from simple to complex with its architectural requirements . We´ll look into similarities and differences of available technologies and you will walk away with an understanding how to use MongoDB to fulfill more advanced tasks such as personalization through clustering algorithms.
In the age of digital transformation and disruption, your ability to thrive depends on how you adapt to the constantly changing environment. MongoDB 3.4 is the latest release of the leading database for modern applications, a culmination of native database features and enhancements that will allow you to easily evolve your solutions to address emerging challenges and use cases.
In this webinar, we introduce you to what’s new, including:
- Multimodel Done Right. Native graph computation, faceted navigation, rich real-time analytics, and powerful connectors for BI and Apache Spark bring additional multimodel database support right into MongoDB.
- Mission-Critical Applications. Geo-distributed MongoDB zones, elastic clustering, tunable consistency, and enhanced security controls bring state-of-the-art database technology to your most mission-critical applications.
- Modernized Tooling. Enhanced DBA and DevOps tooling for schema management, fine-grained monitoring, and cloud-native integration allow engineering teams to ship applications faster, with less overhead and higher quality.
How sitecore depends on mongo db for scalability and performance, and what it...Antonios Giannopoulos
Percona Live 2017 - How sitecore depends on mongo db for scalability and performance, and what it can teach you by Antonios Giannopoulos and Grant Killian
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.
Dev Jumpstart: Build Your First App with MongoDBMongoDB
New to MongoDB? This talk will introduce the philosophy and features of MongoDB. We’ll discuss the benefits of the document-based data model that MongoDB offers by walking through how one can build a simple app to store books. We’ll cover inserting, updating, and querying the database of books. This session will jumpstart your knowledge of MongoDB development, providing you with context for the rest of the day's content.
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB
MongoDB Kubernetes operator and MongoDB Open Service Broker are ready for production operations. Learn about how MongoDB can be used with the most popular container orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications. A demo will show you how easy it is to enable MongoDB clusters as an External Service using the Open Service Broker API for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB
Humana, like many companies, is tackling the challenge of creating real-time insights from data that is diverse and rapidly changing. This is our journey of how we used MongoDB to combined traditional batch approaches with streaming technologies to provide continues alerting capabilities from real-time data streams.
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe.
This talk covers:
Common components of an IoT solution
The challenges involved with managing time-series data in IoT applications
Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance.
How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts
At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB
Our clients have unique use cases and data patterns that mandate the choice of a particular strategy. To implement these strategies, it is mandatory that we unlearn a lot of relational concepts while designing and rapidly developing efficient applications on NoSQL. In this session, we will talk about some of our client use cases, the strategies we have adopted, and the features of MongoDB that assisted in implementing these strategies.
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB
Encryption is not a new concept to MongoDB. Encryption may occur in-transit (with TLS) and at-rest (with the encrypted storage engine). But MongoDB 4.2 introduces support for Client Side Encryption, ensuring the most sensitive data is encrypted before ever leaving the client application. Even full access to your MongoDB servers is not enough to decrypt this data. And better yet, Client Side Encryption can be enabled at the "flick of a switch".
This session covers using Client Side Encryption in your applications. This includes the necessary setup, how to encrypt data without sacrificing queryability, and what trade-offs to expect.
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB
MongoDB Kubernetes operator is ready for prime-time. Learn about how MongoDB can be used with most popular orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications.
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
When you need to model data, is your first instinct to start breaking it down into rows and columns? Mine used to be too. When you want to develop apps in a modern, agile way, NoSQL databases can be the best option. Come to this talk to learn how to take advantage of all that NoSQL databases have to offer and discover the benefits of changing your mindset from the legacy, tabular way of modeling data. We’ll compare and contrast the terms and concepts in SQL databases and MongoDB, explain the benefits of using MongoDB compared to SQL databases, and walk through data modeling basics so you feel confident as you begin using MongoDB.
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB
Query performance should be the unsung hero of an application, but without proper configuration, can become a constant headache. When used properly, MongoDB provides extremely powerful querying capabilities. In this session, we'll discuss concepts like equality, sort, range, managing query predicates versus sequential predicates, and best practices to building multikey indexes.
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB
Aggregation pipeline has been able to power your analysis of data since version 2.2. In 4.2 we added more power and now you can use it for more powerful queries, updates, and outputting your data to existing collections. Come hear how you can do everything with the pipeline, including single-view, ETL, data roll-ups and materialized views.
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB
MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business.
This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented. In addition, we'll discuss future plans and opportunities and offer ample Q&A time with the engineers on the project.
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB
Virtual assistants are becoming the new norm when it comes to daily life, with Amazon’s Alexa being the leader in the space. As a developer, not only do you need to make web and mobile compliant applications, but you need to be able to support virtual assistants like Alexa. However, the process isn’t quite the same between the platforms.
How do you handle requests? Where do you store your data and work with it to create meaningful responses with little delay? How much of your code needs to change between platforms?
In this session we’ll see how to design and develop applications known as Skills for Amazon Alexa powered devices using the Go programming language and MongoDB.
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB
aux Core Data, appréciée par des centaines de milliers de développeurs. Apprenez ce qui rend Realm spécial et comment il peut être utilisé pour créer de meilleures applications plus rapidement.
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB
Il n’a jamais été aussi facile de commander en ligne et de se faire livrer en moins de 48h très souvent gratuitement. Cette simplicité d’usage cache un marché complexe de plus de 8000 milliards de $.
La data est bien connu du monde de la Supply Chain (itinéraires, informations sur les marchandises, douanes,…), mais la valeur de ces données opérationnelles reste peu exploitée. En alliant expertise métier et Data Science, Upply redéfinit les fondamentaux de la Supply Chain en proposant à chacun des acteurs de surmonter la volatilité et l’inefficacité du marché.
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.
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.
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
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/
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.
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.
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.
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
4. Hadoop
The Apache Hadoop software library is a framework
that allows for the distributed processing of large data
sets across clusters of computers using simple
programming models.
• Terabyte and Petabtye datasets
• Data warehousing
• Advanced analytics
5. Enterprise IT Stack
Operational Analytical
EDW
Management & Monitoring
Security & Auditing
Applications
CRM, ERP, Collaboration, Mobile, BI
Data Management
RDBMS
RDBMS
Infrastructure
OS & Virtualization, Compute, Storage, Network
7. Operational: MongoDB
First-level
Analytics
Product/Asset
Catalogs
Security &
Fraud
Internet of
Things
Mobile Apps
Customer
Data Mgmt
Single View Social
Churn Analysis Recommender
Warehouse &
ETL
Risk Modeling
Trade
Surveillance
Predictive
Analytics
Ad Targeting
Sentiment
Analysis
8. Analytical: Hadoop
First-level
Analytics
Product/Asset
Catalogs
Security &
Fraud
Internet of
Things
Mobile Apps
Customer
Data Mgmt
Single View Social
Churn Analysis Recommender
Warehouse &
ETL
Risk Modeling
Trade
Surveillance
Predictive
Analytics
Ad Targeting
Sentiment
Analysis
9. Operational vs. Analytical: Lifecycle
First-level
Analytics
Product/Asset
Catalogs
Security &
Fraud
Internet of
Things
Mobile Apps
Customer
Data Mgmt
Single View Social
Churn Analysis Recommender
Warehouse &
ETL
Risk Modeling
Trade
Surveillance
Predictive
Analytics
Ad Targeting
Sentiment
Analysis
16. HDFS and YARN
• Hadoop Distributed File System
– Distributed file-system that stores data on commodity
machines in a Hadoop cluster
• YARN
– Resource management platform responsible for
managing and scheduling compute resources in a
Hadoop cluster
17. MapReduce
• Paralell, distributed
computation across a
Hadoop cluster
• Process and/or generate
large datasets
• Simplistic model for
individual tasks
Map(k1, v1)
list(k2,v2)
Reduce(k2, list(v2))
list(v3)
18. Pig
• High-level platform for creating
MapReduce
• Pig Latin abstracts Java into
easier-to-use notation
• Executed as a series of
MapReduce applications
• Supports user-defined
functions (UDFs)
19. Hive
• Data warehouse infrastructure built on top of
Hadoop
• Provides data summarization, query, and analysis
• HiveQL is a subset of SQL
• Support for user-defined functions (UDFs)
20. Spark
Spark is a fast and powerful engine for
processing Hadoop data. It is designed to
perform both general data processing (similar
to MapReduce) and new workloads like
streaming, interactive queries, and machine
learning.
• Powerful built-in transformations and actions
– map, reduceByKey, union, distinct, sample, intersection, and
more
– foreach, count, collect, take, and many more
23. Features and Functionality
• MongoDB and BSON
– Input and Output formats
• Computes splits to read data
• Support for
– Filtering data with MongoDB queries
– Authentication
– Reading directly from shard Primaries
– ReadPreferences and Replica Set tags
– Appending to existing collections
26. Reducer Example
public class Reduce extends Reducer<Text, IntWritable, NullWritable, BSONWritable> {
public void reduce(Text key, Iterable<IntWritable> values, Context context) {
int sum = 0;
for(IntWritable value : values) {
sum += value.get();
}
DBObject object = new BasicDBObjectBuilder().start()
.add("genre", key.toString())
.add("count", sum)
.get();
BSONWritable doc = new BSONWritable(object);
context.write(NullWritable.get(), doc);
{ _id: ObjectId(…), genre: “Action”, count: 1370 }
{ _id: ObjectId(…), genre: “Adventure”, count: 957 }
{ _id: ObjectId(…), genre: “Animation”, count: 258 }
}
}
27. Pig – Mappings
Read:
– BSONLoader and MongoLoader
data = LOAD ‘mongodb://mydb:27017/db.collection’
using com.mongodb.hadoop.pig.MongoLoader
– Map schema, _id, datatypes
Insert:
– BSONStorage and MongoInsertStorage
STORE records INTO ‘hdfs:///output.bson’
using com.mongodb.hadoop.pig.BSONStorage
– Map output id, schema
Update:
– MongoUpdateStorage
– Specify query, update operations, schema, update options
28. Pig Specifics
• Fixed or dynamic schema with Loader
• Types auto-mapped
– Embedded documents → Map
– Arrays → Tuple
• Supply alias for “_id”
– not a legal Pig variable name
29. Hive – Tables
CREATE TABLE mongo_users (id int, name string, age int)
STORED BY "com.mongodb.hadoop.hive.MongoStorageHandler"
WITH SERDEPROPERTIES("mongo.columns.mapping”="_id,name,age”)
TBLPROPERTIES("mongo.uri" = "mongodb://host:27017/test.users”)
• Access collections as Hive tables
• Use with MongoStorageHandler or BSONStorageHandler
30. Hive Particulars
• Queries are not (currently) pushed down to MongoDB
• WHERE predicates are evaluated after reading data
from MongoDB
• Types auto-mapped
– Embedded documents (mixed types) → STRUCT
– Embedded documents (single type) → MAP
– Arrays → ARRAY
– ObjectId → STRUCT
• Use EXTERNAL when creating tables otherwise
dropping Hive table drops underlying collection
31. Spark Usage
• Use with MapReduce
input/output formats
• Create Configuration objects
with input/output formats and
data URI
• Load/save data using
SparkContext Hadoop file or
RDD APIs
33. Data Movement
Dynamic queries to MongoDB vs. BSON snapshots in
HDFS
Dynamic queries with
most recent data
Puts load on operational
database
Snapshots move load to
Hadoop
Snapshots add
predictable load to
MongoDB
36. MovieWeb Components
• MovieLens dataset
– 10M ratings, 10K movies, 70K users
• Python web app to browse movies,
recommendations
– Flask, PyMongo
• Spark app computes recommendations
– MLLib collaborative filter
• Predicted ratings are exposed in web app
– New predictions collection
37. MovieWeb Web Application
• Browse
– Top movies by ratings count
– Top genres by movie count
• Log in to
– See My Ratings
– Rate movies
• What’s missing?
– Movies You May Like
– Recommendations
38. Spark Recommender
• Apache Hadoop 2.3.0
– HDFS
• Spark 1.0
– Execute locally
– Assign executor
resources
• Data
– From HDFS
– To MongoDB
39. Snapshot
database as
BSON
Store BSON in
HDFS
Read BSON into
Spark app
Train model from
existing ratings
Create user-movie
pairings
Predict ratings for
all pairings
Write predictions
to MongoDB
collection
Web application
exposes
recommendations
Repeat the
process
MovieWeb Workflow