SlideShare una empresa de Scribd logo
1 de 17
MongoDC 2014 
Confidential & Proprietary
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" : { 
"host" : [ 
{ 
"host" : { 
"network" : null, 
"ips" : [ 
"6PNfL9bV7BJO" 
], 
"names" : [ ], 
"mx" : [ ], 
"txt" : [ ], 
"srv" : [ ], 
"ns" : [ ] 
} 
}, 
{ 
"host" : { 
"network" : null, 
"ips" : [ 
"yqb6q7er3DvWf" 
], 
"names" : [ 
"tQypbmzVrEZHtWG1n" 
], 
"mx" : [ ], 
"txt" : [ ], 
"srv" : [ ], 
"ns" : [ ] 
} 
} 
], 
}
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
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
Confidential & Proprietary

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

MongoDB on Azure
MongoDB on AzureMongoDB on Azure
MongoDB on Azure
 
MongoDB .local Chicago 2019: Practical Data Modeling for MongoDB: Tutorial
MongoDB .local Chicago 2019: Practical Data Modeling for MongoDB: TutorialMongoDB .local Chicago 2019: Practical Data Modeling for MongoDB: Tutorial
MongoDB .local Chicago 2019: Practical Data Modeling for MongoDB: Tutorial
 
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
 
Mongo db workshop # 02
Mongo db workshop # 02Mongo db workshop # 02
Mongo db workshop # 02
 
Schema Design Best Practices with Buzz Moschetti
Schema Design Best Practices with Buzz MoschettiSchema Design Best Practices with Buzz Moschetti
Schema Design Best Practices with Buzz Moschetti
 
Elastic Search
Elastic SearchElastic Search
Elastic Search
 
Mongodb Training Tutorial in Bangalore
Mongodb Training Tutorial in BangaloreMongodb Training Tutorial in Bangalore
Mongodb Training Tutorial in Bangalore
 
Webinar: Managing Real Time Risk Analytics with MongoDB
Webinar: Managing Real Time Risk Analytics with MongoDB Webinar: Managing Real Time Risk Analytics with MongoDB
Webinar: Managing Real Time Risk Analytics with MongoDB
 
Doing Joins in MongoDB: Best Practices for Using $lookup
Doing Joins in MongoDB: Best Practices for Using $lookupDoing Joins in MongoDB: Best Practices for Using $lookup
Doing Joins in MongoDB: Best Practices for Using $lookup
 
Introduction to elasticsearch
Introduction to elasticsearchIntroduction to elasticsearch
Introduction to elasticsearch
 
MongoDB .local Munich 2019: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local Munich 2019: MongoDB Atlas Data Lake Technical Deep DiveMongoDB .local Munich 2019: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local Munich 2019: MongoDB Atlas Data Lake Technical Deep Dive
 
MongoDB .local Munich 2019: Managing a Heterogeneous Stack with MongoDB & SQL
MongoDB .local Munich 2019: Managing a Heterogeneous Stack with MongoDB & SQLMongoDB .local Munich 2019: Managing a Heterogeneous Stack with MongoDB & SQL
MongoDB .local Munich 2019: Managing a Heterogeneous Stack with MongoDB & SQL
 
How Thermo Fisher is Reducing Data Analysis Times from Days to Minutes with M...
How Thermo Fisher is Reducing Data Analysis Times from Days to Minutes with M...How Thermo Fisher is Reducing Data Analysis Times from Days to Minutes with M...
How Thermo Fisher is Reducing Data Analysis Times from Days to Minutes with M...
 
MongoDB and Spark
MongoDB and SparkMongoDB and Spark
MongoDB and Spark
 
Webinar: Introducing the MongoDB Connector for BI 2.0 with Tableau
Webinar: Introducing the MongoDB Connector for BI 2.0 with TableauWebinar: Introducing the MongoDB Connector for BI 2.0 with Tableau
Webinar: Introducing the MongoDB Connector for BI 2.0 with Tableau
 
Webinar: Live Data Visualisation with Tableau and MongoDB
Webinar: Live Data Visualisation with Tableau and MongoDBWebinar: Live Data Visualisation with Tableau and MongoDB
Webinar: Live Data Visualisation with Tableau and MongoDB
 
Tutorial: Building Your First App with MongoDB Stitch
Tutorial: Building Your First App with MongoDB StitchTutorial: Building Your First App with MongoDB Stitch
Tutorial: Building Your First App with MongoDB Stitch
 
Bloom Filters for Web Caching - Lightning Talk
Bloom Filters for Web Caching - Lightning TalkBloom Filters for Web Caching - Lightning Talk
Bloom Filters for Web Caching - Lightning Talk
 
Mongodb tutorial at Easylearning Guru
Mongodb tutorial  at Easylearning GuruMongodb tutorial  at Easylearning Guru
Mongodb tutorial at Easylearning Guru
 
Why NoSQL Makes Sense
Why NoSQL Makes SenseWhy NoSQL Makes Sense
Why NoSQL Makes Sense
 

Destacado

Quantifying Business Advantage: The Value of Database Selection
Quantifying Business Advantage: The Value of Database SelectionQuantifying Business Advantage: The Value of Database Selection
Quantifying Business Advantage: The Value of Database Selection
MongoDB
 
Introduction to Sharding
Introduction to ShardingIntroduction to Sharding
Introduction to Sharding
MongoDB
 

Destacado (7)

Quantifying Business Advantage: The Value of Database Selection
Quantifying Business Advantage: The Value of Database SelectionQuantifying Business Advantage: The Value of Database Selection
Quantifying Business Advantage: The Value of Database Selection
 
Webinar: Data Modeling Examples in the Real World
Webinar: Data Modeling Examples in the Real WorldWebinar: Data Modeling Examples in the Real World
Webinar: Data Modeling Examples in the Real World
 
Webinar: How to Drive Business Value in Financial Services with MongoDB
Webinar: How to Drive Business Value in Financial Services with MongoDBWebinar: How to Drive Business Value in Financial Services with MongoDB
Webinar: How to Drive Business Value in Financial Services with MongoDB
 
Introduction to Sharding
Introduction to ShardingIntroduction to Sharding
Introduction to Sharding
 
How Government Agencies are Using MongoDB to Build Data as a Service Solutions
How Government Agencies are Using MongoDB to Build Data as a Service SolutionsHow Government Agencies are Using MongoDB to Build Data as a Service Solutions
How Government Agencies are Using MongoDB to Build Data as a Service Solutions
 
Real World MongoDB: Use Cases from Financial Services by Daniel Roberts
Real World MongoDB: Use Cases from Financial Services by Daniel RobertsReal World MongoDB: Use Cases from Financial Services by Daniel Roberts
Real World MongoDB: Use Cases from Financial Services by Daniel Roberts
 
How Financial Services Organizations Use MongoDB
How Financial Services Organizations Use MongoDBHow Financial Services Organizations Use MongoDB
How Financial Services Organizations Use MongoDB
 

Similar a Eagle6 Enterprise Situational Awareness

Webinar: How MongoDB is making Government Better, Faster, Smarter
Webinar: How MongoDB is making Government Better, Faster, SmarterWebinar: How MongoDB is making Government Better, Faster, Smarter
Webinar: How MongoDB is making Government Better, Faster, Smarter
MongoDB
 
Evolving your Data Access with MongoDB Stitch
Evolving your Data Access with MongoDB StitchEvolving your Data Access with MongoDB Stitch
Evolving your Data Access with MongoDB Stitch
MongoDB
 

Similar a Eagle6 Enterprise Situational Awareness (20)

MongodB Internals
MongodB InternalsMongodB Internals
MongodB Internals
 
MongoDB 3.4 webinar
MongoDB 3.4 webinarMongoDB 3.4 webinar
MongoDB 3.4 webinar
 
MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
 
Webinar: The Anatomy of the Cloudant Data Layer
Webinar: The Anatomy of the Cloudant Data LayerWebinar: The Anatomy of the Cloudant Data Layer
Webinar: The Anatomy of the Cloudant Data Layer
 
MongoDB Meetup
MongoDB MeetupMongoDB Meetup
MongoDB Meetup
 
Simplifying & accelerating application development with MongoDB's intelligent...
Simplifying & accelerating application development with MongoDB's intelligent...Simplifying & accelerating application development with MongoDB's intelligent...
Simplifying & accelerating application development with MongoDB's intelligent...
 
MongoDB .local Bengaluru 2019: New Encryption Capabilities in MongoDB 4.2: A ...
MongoDB .local Bengaluru 2019: New Encryption Capabilities in MongoDB 4.2: A ...MongoDB .local Bengaluru 2019: New Encryption Capabilities in MongoDB 4.2: A ...
MongoDB .local Bengaluru 2019: New Encryption Capabilities in MongoDB 4.2: A ...
 
Confluent & MongoDB APAC Lunch & Learn
Confluent & MongoDB APAC Lunch & LearnConfluent & MongoDB APAC Lunch & Learn
Confluent & MongoDB APAC Lunch & Learn
 
Webinar: How MongoDB is making Government Better, Faster, Smarter
Webinar: How MongoDB is making Government Better, Faster, SmarterWebinar: How MongoDB is making Government Better, Faster, Smarter
Webinar: How MongoDB is making Government Better, Faster, Smarter
 
Application development with Oracle NoSQL Database 3.0
Application development with Oracle NoSQL Database 3.0Application development with Oracle NoSQL Database 3.0
Application development with Oracle NoSQL Database 3.0
 
MongoDB Evenings Dallas: What's the Scoop on MongoDB & Hadoop
MongoDB Evenings Dallas: What's the Scoop on MongoDB & HadoopMongoDB Evenings Dallas: What's the Scoop on MongoDB & Hadoop
MongoDB Evenings Dallas: What's the Scoop on MongoDB & Hadoop
 
Evolving your Data Access with MongoDB Stitch
Evolving your Data Access with MongoDB StitchEvolving your Data Access with MongoDB Stitch
Evolving your Data Access with MongoDB Stitch
 
Building your first app with MongoDB
Building your first app with MongoDBBuilding your first app with MongoDB
Building your first app with MongoDB
 
Evolving your Data Access with MongoDB Stitch - Drew Di Palma
Evolving your Data Access with MongoDB Stitch - Drew Di PalmaEvolving your Data Access with MongoDB Stitch - Drew Di Palma
Evolving your Data Access with MongoDB Stitch - Drew Di Palma
 
Data Con LA 2022 - What's new with MongoDB 6.0 and Atlas
Data Con LA 2022 - What's new with MongoDB 6.0 and AtlasData Con LA 2022 - What's new with MongoDB 6.0 and Atlas
Data Con LA 2022 - What's new with MongoDB 6.0 and Atlas
 
MongoDB Stich Overview
MongoDB Stich OverviewMongoDB Stich Overview
MongoDB Stich Overview
 
MongoDB Evenings Houston: What's the Scoop on MongoDB and Hadoop? by Jake Ang...
MongoDB Evenings Houston: What's the Scoop on MongoDB and Hadoop? by Jake Ang...MongoDB Evenings Houston: What's the Scoop on MongoDB and Hadoop? by Jake Ang...
MongoDB Evenings Houston: What's the Scoop on MongoDB and Hadoop? by Jake Ang...
 
Microsoft Azure Big Data Analytics
Microsoft Azure Big Data AnalyticsMicrosoft Azure Big Data Analytics
Microsoft Azure Big Data Analytics
 
Webinar: How Banks Use MongoDB as a Tick Database
Webinar: How Banks Use MongoDB as a Tick DatabaseWebinar: How Banks Use MongoDB as a Tick Database
Webinar: How Banks Use MongoDB as a Tick Database
 
MongoDB Stitch Introduction
MongoDB Stitch IntroductionMongoDB Stitch Introduction
MongoDB Stitch Introduction
 

Más de MongoDB

Más de MongoDB (20)

MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
 
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
 
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
 
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
 
MongoDB SoCal 2020: MongoDB Atlas Jump Start
 MongoDB SoCal 2020: MongoDB Atlas Jump Start MongoDB SoCal 2020: MongoDB Atlas Jump Start
MongoDB SoCal 2020: MongoDB Atlas Jump Start
 
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
 
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
 
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
 
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
 
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
 
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
 
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
 
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
 
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
 
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
 
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
 
MongoDB .local Paris 2020: Les bonnes pratiques pour sécuriser MongoDB
MongoDB .local Paris 2020: Les bonnes pratiques pour sécuriser MongoDBMongoDB .local Paris 2020: Les bonnes pratiques pour sécuriser MongoDB
MongoDB .local Paris 2020: Les bonnes pratiques pour sécuriser MongoDB
 
MongoDB .local Paris 2020: Tout savoir sur le moteur de recherche Full Text S...
MongoDB .local Paris 2020: Tout savoir sur le moteur de recherche Full Text S...MongoDB .local Paris 2020: Tout savoir sur le moteur de recherche Full Text S...
MongoDB .local Paris 2020: Tout savoir sur le moteur de recherche Full Text S...
 
MongoDB .local Paris 2020: Adéo @MongoDB : MongoDB Atlas & Leroy Merlin : et ...
MongoDB .local Paris 2020: Adéo @MongoDB : MongoDB Atlas & Leroy Merlin : et ...MongoDB .local Paris 2020: Adéo @MongoDB : MongoDB Atlas & Leroy Merlin : et ...
MongoDB .local Paris 2020: Adéo @MongoDB : MongoDB Atlas & Leroy Merlin : et ...
 
MongoDB .local Paris 2020: Les bonnes pratiques pour travailler avec les donn...
MongoDB .local Paris 2020: Les bonnes pratiques pour travailler avec les donn...MongoDB .local Paris 2020: Les bonnes pratiques pour travailler avec les donn...
MongoDB .local Paris 2020: Les bonnes pratiques pour travailler avec les donn...
 

Último

EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
Earley Information Science
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
giselly40
 

Último (20)

Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Tech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdfTech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdf
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 

Eagle6 Enterprise Situational Awareness

  • 2. Adam Bell, Director, Product Management ◦ 15+ years enterprise software solutions experience ◦ 10+ years enterprise architecture ◦ Healthcare ◦ Technology ◦ MongoDB user since 2012 Confidential & Proprietary
  • 3. 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
  • 4. 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
  • 5. Large sets of Multi-Dimensional Data Heavy Read and Write Heavy Audit Requirements Fast Near Real-Time Analytics Analytics are User Driven Confidential & Proprietary
  • 6. 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
  • 7. Confidential & Proprietary "network" : { "host" : [ { "host" : { "network" : null, "ips" : [ "6PNfL9bV7BJO" ], "names" : [ ], "mx" : [ ], "txt" : [ ], "srv" : [ ], "ns" : [ ] } }, { "host" : { "network" : null, "ips" : [ "yqb6q7er3DvWf" ], "names" : [ "tQypbmzVrEZHtWG1n" ], "mx" : [ ], "txt" : [ ], "srv" : [ ], "ns" : [ ] } } ], }
  • 8. Do not drop transactions Capturing large volumes of real-time data (web access logs, database transactions, etc) Confidential & Proprietary
  • 9. 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
  • 10. Key Value Approach TODO: Need an example of KV indexes Confidential & Proprietary
  • 12. Shard Key is an object hash Goal is equal distribution of data across shards ◦ Example { hash: '00003820efcff8b669b055606813bcd360ace3f43fbf9c129845b3028992eacabca ef8cd13796dc7a96b7a5f38b0efaceaadecfd537c72eaec8a8f9c10a00a1e’, offset: -1} Confidential & Proprietary
  • 13. 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
  • 14. 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
  • 15. 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" }
  • 16. Ported Aggregation framework to Javascript ◦ Custom built extensions Accumulators $stdDev Expressions $regex $slice Document Sources $projectPrevious $split Confidential & Proprietary