Eagle6 Enterprise Situational Awareness

Oct 14, 2014Download as PPTX, PDF1 like741 views

Eagle6 is a product that use system artifacts to create a replica model that represents a near real-time view of system architecture. Eagle6 was built to collect system data (log files, application source code, etc.) and to link system behaviors in such a way that the user is able to quickly identify risks associated with unknown or unwanted behavioral events that may result in unknown impacts to seemingly unrelated down-stream systems. This session is designed to present the capabilities of the Eagle6 modeling product and how we are using MongoDB to support near-real-time analysis of large disparate datasets.

Adam Bell, Director, Product Management
◦ 15+ years enterprise software solutions experience
◦ 10+ years enterprise architecture
◦ Healthcare
◦ Technology
◦ MongoDB user since 2012
Confidential & Proprietary

Service-Disabled Veteran
Owned Small Business
Minority-Owned Business
Primary NAICS Codes:
◦ 541511
◦ 541512
Confidential & Proprietary
Established: 2002*
Subject Matter Expertise:
Enterprise Software
Development
Business Process
Reengineering (BPR)
Proprietary Software:
Eagle6 Modeling &
Simulation
*Over 30% of Rivera Group Employees Are Veterans

Eagle6 is a modeling tool that automatically
collects system data (application code,
database schemas, log files, etc.) and
provides an ability to continuously monitor
for unwanted system states (bugs) that may
result in system degradation and/or system
outages.
Confidential & Proprietary

Large sets of Multi-Dimensional Data
Heavy Read and Write
Heavy Audit Requirements
Fast Near Real-Time Analytics
Analytics are User Driven
Confidential & Proprietary

About Our Documents
◦ 1000(s) of leafs on documents
◦ Document Sizes (Bson):
2300 bytes (2.3Kb) - 729699 bytes (729Kb)
◦ Need to quickly add new data structures
Confidential & Proprietary

Confidential & Proprietary
"network" : {
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{
"host" : {
"network" : null,
"ips" : [
"6PNfL9bV7BJO"
],
"names" : [ ],
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"txt" : [ ],
"srv" : [ ],
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"mx" : [ ],
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"srv" : [ ],
"ns" : [ ]
}
}
],
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Do not drop transactions
Capturing large volumes of real-time data
(web access logs, database transactions, etc)
Confidential & Proprietary

Indexing has been a challenge
MongoDB only supports 64 indexes
To many indexes defeats the purpose of
indexes
Scenarios exist where we will not know what
fields the user needs until they request them
Confidential & Proprietary

Key Value Approach
TODO: Need an example of KV indexes
Confidential & Proprietary

Shard Key is an object hash
Goal is equal distribution of data across
shards
◦ Example
{
hash:
'00003820efcff8b669b055606813bcd360ace3f43fbf9c129845b3028992eacabca
ef8cd13796dc7a96b7a5f38b0efaceaadecfd537c72eaec8a8f9c10a00a1e’,
offset: -1}
Confidential & Proprietary

Needed a flexible way to Aggregate
Needed a way to represent Aggregations to
end users with out writing functions
Needed a way to cache frequently run
analytics
Confidential & Proprietary

Provides a Rich set of operations for
aggregating data
We have been using the Aggregation
Framework since MongoDB 2.2
Aggregation Pipelines have enabled us to do
smart caching
JSON versions of the pipeline allow end users
flexibility without writing Map/Reduce code
Confidential & Proprietary

$Confidential & Proprietary { "$match": { ”servers": { "$exists": true } } }, { "$group": { "_id": ”$serversr", "clients": { "$addToSet": ”$client.names } } }, { "$project": { "title": { "$join": "$_id.names" }, "_id": { "id": { "$join": "$_id.ips" } }, "clients": 1, "server": "$_id" } }, { "$group": { "_id": "$_id", "server": { "$addToSet": "$server" }, "clients": { "$addToSet": "$clients" }, "title": { "$addToSet": "$title" } } }, { "$unwind": "$clients" }, { "$unwind": "$clients" }, { "$project": { "clients": { "$join": "$clients" }, "server": "$server", "title": "$title" } }, { "$group": { "_id": "$_id", "server": { "$addToSet": "$server" }, "clients": { "$addToSet": "$clients" }, "title": { "$addToSet": "$title" } } }, { "$unwind": "$title" }, { "$unwind": "$title" }$

Ported Aggregation framework to Javascript
◦ Custom built extensions
Accumulators
$stdDev
Expressions
$regex
$slice
Document Sources
$projectPrevious
$split
Confidential & Proprietary

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MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB

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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

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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 .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB

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MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB

The document discusses guidelines for ordering fields in compound indexes to optimize query performance. It recommends the E-S-R approach: placing equality fields first, followed by sort fields, and range fields last. This allows indexes to leverage equality matches, provide non-blocking sorts, and minimize scanning. Examples show how indexes ordered by these guidelines can support queries more efficiently by narrowing the search bounds.

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MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB

The document describes a methodology for data modeling with MongoDB. It begins by recognizing the differences between document and tabular databases, then outlines a three step methodology: 1) describe the workload by listing queries, 2) identify and model relationships between entities, and 3) apply relevant patterns when modeling for MongoDB. The document uses examples around modeling a coffee shop franchise to illustrate modeling approaches and techniques.

MongoDB .local San Francisco 2020: 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.

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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.

MongoDB .local Paris 2020: Tout savoir sur le moteur de recherche Full Text S...MongoDB

Venez en apprendre davantage sur notre nouvel opérateur de recherche en texte intégral pour MongoDB Atlas. Il s'agit d'une amélioration significative des fonctionnalités de recherches de MongoDB et c'est également la solution de recherche en texte intégral la plus simple et la plus puissante pour les bases de données MongoDB Atlas. Cette présentation est importante pour quiconque a mis en place ou en visage de mettre en place une fonctionnalité de recherche dans son application MongoDB. Vous assisterez à une démo de $searchBeta, apprendrez comment cela fonctionne, découvrirez des fonctionnalités spécifiques vous permettant d'obtenir des résultats de recherche pertinents et apprendrez comment vous pouvez commencer à utiliser la recherche en texte intégral dans votre application dès aujourd'hui.

MongoDB .local Paris 2020: Adéo @MongoDB : MongoDB Atlas & Leroy Merlin : et ...MongoDB

Adeo et en particulier Leroy Merlin utilisent massivement MongoDB pour propulser de nombreuses applications et en particulier son site web leroymerlin.fr. Emmanuel Dieval Ingénieur Software chez ADEO, présentera le nouveau système au coeur de la publication de l'offre Leroy Merlin: OPUS. OPUS s'appuie particulièrement sur MongoDB pour la construction des pages de famille de produits tout en supportant un important flux de données journalier. Après un rappel sur les pipelines d'agrégation et une présentation de MongoDB Atlas par Maxime Beugnet, Developer Advocate chez MongoDB, Emmanuel parlera de l'utilisation des pipelines d'agrégation pour la construction des pages de famille de produits, mais aussi de Google Cloud Platform et des avantages à utiliser MongoDB Atlas.

MongoDB .local Paris 2020: Les bonnes pratiques pour travailler avec les donn...MongoDB

Les données de séries chronologiques sont de plus en plus au cœur des applications modernes: pensez à l'IoT, aux transactions sur actions, aux flux de clics, aux médias sociaux, etc. Avec le passage des systèmes batch aux systèmes temps réel, la capture et l'analyse efficaces des données de séries chronologiques peuvent permettre aux entreprises de mieux détecter et réagir aux événements en avance sur leurs concurrents ou d'améliorer l'efficacité opérationnelle pour réduire les coûts et les risques. Travailler avec des données de séries chronologiques est souvent différent des données d’application classiques et vous devez observer les meilleures pratiques. Cette conférence couvre: Composants communs d'une solution IoT Les défis liés à la gestion de données chronologiques dans les applications IoT Différentes conceptions de schéma et leur incidence sur l'utilisation de la mémoire et du disque sont deux facteurs déterminants dans les performances des applications. Comment interroger, analyser et présenter les données de séries chronologiques IoT à l'aide de MongoDB Compass et MongoDB Charts À la fin de la session, vous aurez une meilleure compréhension des meilleures pratiques clés en matière de gestion des données de séries chronologiques de l'IoT avec MongoDB.