Hype, buzzword, threat; however you want to characterize it, the Internet of Things (IoT) is here.
IoT scenarios that were hypothetical only a few years ago are real today. Still thinking along the line of fleet management and temperature measurements? You’re out. Endless possibilities of IoT applications are surfacing every day, from the connected cow (huh?) to things that monitor and analyze your daily life (really?).
In this webinar, we will discuss architecture of IoT data management solutions and the challenges that arise. We will explore how MongoDB features provide solutions to those problems. Time permitting, we will demonstrate an IoT Cloud service built on top of MongoDB.
MongoDB World 2019: Near Real-Time Analytical Data Hub with MongoDBMongoDB
Attendees will learn how to build an operational data hub that can be used as a silo-buster. In this session, I will show how we developed a data hub at TD Ameritrade to provide actionable 360 views of client data using MongoDB. I will also explain why this approach suited our use case better than a Hadoop-based data lake.
MongoDB is an open-source document database, and the leading NoSQL database. Written in C++.
MongoDB has official drivers for a variety of popular programming languages and development environments. There are also a large number of unofficial or community-supported drivers for other programming languages and frameworks.
MongoDB World 2019: Near Real-Time Analytical Data Hub with MongoDBMongoDB
Attendees will learn how to build an operational data hub that can be used as a silo-buster. In this session, I will show how we developed a data hub at TD Ameritrade to provide actionable 360 views of client data using MongoDB. I will also explain why this approach suited our use case better than a Hadoop-based data lake.
MongoDB is an open-source document database, and the leading NoSQL database. Written in C++.
MongoDB has official drivers for a variety of popular programming languages and development environments. There are also a large number of unofficial or community-supported drivers for other programming languages and frameworks.
The slides defines IoT and show the differnce between M2M and IoT vision. It then describes the different layers that depicts the functional architecture of IoT, standard organizations and bodies and other IoT technology alliances, low power IoT protocols, IoT Platform components, and finally gives a short description to one of IoT low power application protocols (MQTT).
Stream processing IoT time series data with Kafka & InfluxDB | Al Sargent, In...HostedbyConfluent
Time series data is everywhere -- connected IoT devices, application monitoring & observability platforms, and more. What makes time series datastreams challenging is that they often have orders of magnitude more data than other workloads, with millions of time series datapoints being quite common. Given its ability to ingest high volumes of data, Kafka is a natural part of any data architecture handling large volumes of time series telemetry, specifically as an intermediate buffer before that data is persisted in InfluxDB for processing, analysis, and use in other applications. In this session, we will show you how you can stream time series data to your IoT application using Kafka queues and InfluxDB, drawing upon deployments done at Hulu and Wayfair that allow both to ingest 1 million metrics per second. Once this session is complete, you’ll be able to connect a Kafka queue to an InfluxDB instance as the beginning of your own time series data pipeline.
Fundamental to any distributed system are communication patterns: point-to-point, request-reply, transactional queues, and publish-subscribe. Large distributed systems often employ two or more communication patterns. Using a single middleware that supports multiple communication patterns is a very cost-effective way of developing and maintaining large distributed systems. This talk will begin with an introduction of Data Distribution Service (DDS) – an OMG standard – that supports data-centric publish-subscribe communication for real-time distributed systems. DDS separates state management and distribution from application logic and supports discoverable data models. The talk will then describe how RTI Connext Messaging goes beyond vanilla DDS and implements various communication patterns including request-reply, command-response, and guaranteed delivery. You will also learn how these patterns can be combined to create interesting variations when the underlying substrate is as powerful as DDS. We’ll also discuss APIs for creating high-performance applications using the request-reply communication pattern.
MQTT - MQ Telemetry Transport for Message QueueingPeter R. Egli
Description of message queueing (MQ) protocol for the transport of telemetry data (MQTT - MQ Telemetry Transport).
MQTT is a protocol designed to fit the needs of Internet of Things scenarios. It is lightweight and efficient, but still affords all the features required for reliable messaging between wireless sensor / actor nodes and applications. MQTT decouples producer and consumer of data (sensors, actors and applications) through message brokers with publish / subscribe message queues called topics. MQTT supports different levels of quality of service thus providing the flexibility to adapt to the different needs of applications.
Further features like will and retain messages make MQTT well suited for sensor network scenarios as well as for lightweight enterprise messaging applications.
Open source implementations like Eclipse paho provide ample code for integrating MQTT in your own applications.
DATA STORAGE.Introduction to enterprise data storage.Data storage management.File Systems.Cloud file system.Cloud data stores.Using Grid for data storage.
In general, data can be broken into two categories – data in motion vs data at rest. Learn the difference between these two types of data and the best infrastructure options to get optimal performance.
MongoDB Solution for Internet of Things and Big DataStefano Dindo
Internet of Things è uno degli scenari di mercato più importanti su cui investire entro il 2020.
L'Internet of Things permette di trasferire sul Web la vita reale delle persone grazie all'interazione con oggetti e spazi fisici scambiando un grande volume di dati.
Durante il Lab è stata fornita una descrizione di architettura necessaria a supportare progetti di Internet of Things con un focus sull'organizzazione dei dati all'interno di MongoDB, database NoSQL Leader di mercato, per raccogliere ed analizzare grandi volumi di dati in tempo reale ed in modo efficiente.
The slides defines IoT and show the differnce between M2M and IoT vision. It then describes the different layers that depicts the functional architecture of IoT, standard organizations and bodies and other IoT technology alliances, low power IoT protocols, IoT Platform components, and finally gives a short description to one of IoT low power application protocols (MQTT).
Stream processing IoT time series data with Kafka & InfluxDB | Al Sargent, In...HostedbyConfluent
Time series data is everywhere -- connected IoT devices, application monitoring & observability platforms, and more. What makes time series datastreams challenging is that they often have orders of magnitude more data than other workloads, with millions of time series datapoints being quite common. Given its ability to ingest high volumes of data, Kafka is a natural part of any data architecture handling large volumes of time series telemetry, specifically as an intermediate buffer before that data is persisted in InfluxDB for processing, analysis, and use in other applications. In this session, we will show you how you can stream time series data to your IoT application using Kafka queues and InfluxDB, drawing upon deployments done at Hulu and Wayfair that allow both to ingest 1 million metrics per second. Once this session is complete, you’ll be able to connect a Kafka queue to an InfluxDB instance as the beginning of your own time series data pipeline.
Fundamental to any distributed system are communication patterns: point-to-point, request-reply, transactional queues, and publish-subscribe. Large distributed systems often employ two or more communication patterns. Using a single middleware that supports multiple communication patterns is a very cost-effective way of developing and maintaining large distributed systems. This talk will begin with an introduction of Data Distribution Service (DDS) – an OMG standard – that supports data-centric publish-subscribe communication for real-time distributed systems. DDS separates state management and distribution from application logic and supports discoverable data models. The talk will then describe how RTI Connext Messaging goes beyond vanilla DDS and implements various communication patterns including request-reply, command-response, and guaranteed delivery. You will also learn how these patterns can be combined to create interesting variations when the underlying substrate is as powerful as DDS. We’ll also discuss APIs for creating high-performance applications using the request-reply communication pattern.
MQTT - MQ Telemetry Transport for Message QueueingPeter R. Egli
Description of message queueing (MQ) protocol for the transport of telemetry data (MQTT - MQ Telemetry Transport).
MQTT is a protocol designed to fit the needs of Internet of Things scenarios. It is lightweight and efficient, but still affords all the features required for reliable messaging between wireless sensor / actor nodes and applications. MQTT decouples producer and consumer of data (sensors, actors and applications) through message brokers with publish / subscribe message queues called topics. MQTT supports different levels of quality of service thus providing the flexibility to adapt to the different needs of applications.
Further features like will and retain messages make MQTT well suited for sensor network scenarios as well as for lightweight enterprise messaging applications.
Open source implementations like Eclipse paho provide ample code for integrating MQTT in your own applications.
DATA STORAGE.Introduction to enterprise data storage.Data storage management.File Systems.Cloud file system.Cloud data stores.Using Grid for data storage.
In general, data can be broken into two categories – data in motion vs data at rest. Learn the difference between these two types of data and the best infrastructure options to get optimal performance.
MongoDB Solution for Internet of Things and Big DataStefano Dindo
Internet of Things è uno degli scenari di mercato più importanti su cui investire entro il 2020.
L'Internet of Things permette di trasferire sul Web la vita reale delle persone grazie all'interazione con oggetti e spazi fisici scambiando un grande volume di dati.
Durante il Lab è stata fornita una descrizione di architettura necessaria a supportare progetti di Internet of Things con un focus sull'organizzazione dei dati all'interno di MongoDB, database NoSQL Leader di mercato, per raccogliere ed analizzare grandi volumi di dati in tempo reale ed in modo efficiente.
If you have somehow missed the hype, the Internet of Things (IoT) is a fast-growing constellation of internet-connected sensors attached to a wide variety of 'things'. Sensors can take a multitude of possible measurements, Internet connections can be wired or wireless, while 'things' can literally be any object to which you can attach or embed a sensor. If you carry a smartphone, for example, you become a multi-sensor IoT 'thing', and many of your day-to-day activities can be tracked, analysed and acted upon.
Many of the conversations taking place around the IoT are incomplete without a mention of big data. Big data is characterised by “4 V’s”: volume, variety, velocity and veracity. That is, big data comes in large amounts (volume), is a mixture of structured and unstructured information (variety), arrives at (often real-time) speed (velocity) and can be different levels of uncertainty (veracity).
As organizations step into IoT, they must understand the symbiotic relationship between IoT and big data. Just like with any big-data play, merely collecting the data isn't enough. The data must be processed and analyzed to derive meaningful insights, and those insights must drive actionable steps that can improve the business.
What that means is that, without Big Data, the IoT can offer an enterprise little more than noise. But wait…! On the other hand, without IoT, the Big Data is little more than any other software lying idle. Actually you need two to Tango. That’s when you get the perfect marriage!
Imagine that self-driving cars now exist and are becoming widespread around the world. To facilitate the transition, it's necessary to set up central service to monitor traffic conditions nationwide, deploy sensors throughout the interstate system that monitor traffic conditions including car speeds, pavement and weather conditions, as well as accidents, construction, and other sources of traffic tie ups.
MongoDB has been selected as the database for this application. In this webinar, we will walk through designing the application’s schema that will both support the high update and read volumes as well as the data aggregation and analytics queries.
Hype, buzzword, threat; however you want to characterize it, the Internet of Things (IoT) is here.
IoT scenarios that were hypothetical only a few years ago are real today. Still thinking along the line of fleet management and temperature measurements? You’re out. Endless possibilities of IoT applications are surfacing every day, from the connected cow (huh?) to things that monitor and analyze your daily life (really?).
In this webinar, we will discuss architecture of IoT data management solutions and the challenges that arise. We will explore how MongoDB features provide solutions to those problems. Time permitting, we will demonstrate an IoT Cloud service built on top of MongoDB.
MongoDB IoT City Tour STUTTGART: Industry 4.0 and the Internet of Things: Inm...MongoDB
Presented by, Timo Klingenmeier, General Manager, inmation GmbH & Co. KG
Industry 4.0 and the Internet of Things translate to new opportunities and new challenges for manufacturing corporations. The informed workforce requires access to vast amounts of data. Handling floods of data from the production floor across the organisation and in exchange with selected business partners is not a simple task, but when achieved it turns into a major competitive advantage. MongoDB’s strategic partner inmation has developed a middleware solution which combines intelligent industrial real-time connectivity with unlimited scalability and one uniform storage layer for all kinds of data structures – MongoDB.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Dependent on the size and quantity of such events, this can quickly be in the range of Big Data. How can we efficiently collect and transmit these events? How can we make sure that we can always report over historical events? How can these new events be integrated into traditional infrastructure and application landscape?
Starting with a product and technology neutral reference architecture, we will then present different solutions using Open Source frameworks and the Oracle Stack both for on premises as well as the cloud.
MongoDB for Time Series Data Part 2: Analyzing Time Series Data Using the Agg...MongoDB
The United States will be deploying 16,000 traffic speed monitoring sensors - 1 on every mile of US interstate in urban centers. These sensors update the speed, weather, and pavement conditions once per minute. MongoDB will collect and aggregate live sensor data feeds from roadways around the country, support real-time queries from cars on traffic conditions on their route as well as be the platform for real-time dashboards displaying traffic conditions and more complex analytical queries used to identify traffic trends. In this session, we’ll implement a few different data aggregation techniques to query and dashboard the metrics gathered from the US interstate.
This webinar will cover best practices around dev/ops and general operations for those already familiar with basics of MongoDB. Topics will include team roles around data model design, monitoring, hardware configurations, replication and horizontal scaling.
Webinar: Best Practices for Getting Started with MongoDBMongoDB
MongoDB adoption continues to grow at a record pace due to the significant enhancements in developer productivity and scalability that the database provides. Occasionally, however, organizations new to the technology make mistakes that limit their ability to leverage the significant advantages MongoDB provides. This webinar will discuss some of the common mistakes made by users when they first start working with MongoDB, how to identify when you've made those mistakes, and how to resolve them.
Analyzing petabytes of smartmeter data using Cloud Bigtable, Cloud Dataflow, ...Edwin Poot
The Energy Industry is in transition due to the exponential growth of data being generated by the ever increasing number of connected devices which comprise the Smart Grid. Learn how Energyworx uses GCP to collect and ingest this IoT data with ease and is helping her customers uncover hidden value from this data, allowing them to create new business models and concepts.
Mark Goldstein, President of International Research Center gave the opening keynote address “Internet of Things – Transformative Megatrends for Sustainability” to the IEEE Conference on Technologies for Sustainability (IEEE SusTech, http://sites.ieee.org/sustech/) on October 10, 2016 in Phoenix, AZ. He explored the next Internet wave, the Internet of Things (IoT), expected to connect tens of billions of new sensors and devices in the coming years driving sustainability while transforming home, business, government, industrial, medical, transportation, and other complex ecosystems. This deck examines how IoT will be implemented and monetized creating new business models from pervasive sensor deployments and data gathering, accompanied by new privacy and security risks. Explore IoT’s roadblocks and operational challenges, emerging standards and protocols, gateway and wireless integration, and big data strategies and opportunities.
The fascinating world of Internet of Things is so huge that it cannot be fully described in one session. But you can start your adventure. Presentation of IoT Hub, reference architecture, fast review of a few ready solutions and interaction with MXChip IoT DevKit.
Enerji Sektöründe Endüstriyel IoT Uygulamaları - Şahin Çağlayan (Reengen)ideaport
Reengen Enerji IoT Platformu kurucu ortağı ve AR-GE sorumlusu Sahin Çaglayan, nesnelerin interneti ve büyük veri analizi yeteneklerini bir araya getirerek ticari binalarda ve enerji şebekesinde bulut tabanlı optimizasyon süreçlerini anlattı.
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23 Mart 2016
meet@ideaport | IoTxTR#21 'Enerji Sektöründe Endüstriyel IoT Uygulamaları' Semineri
Building a reliable and scalable IoT platform with MongoDB and HiveMQDominik Obermaier
Today’s Internet of Things (IoT) is enabling companies to blend together the physical and digital worlds, creating new business models and generating insights that increase productivity at once unimaginable levels. However, managing the ever growing volume of heterogeneous IoT data from disparate devices, systems and applications both on premise and in the cloud can be a challenging endeavour without a scalable and reliable IoT platform.
In this webinar, we will explore why and how companies are leveraging HiveMQ and MongoDB to build exactly that: a scalable and reliable IoT platform. Based upon a sample fleet management scenario, we will explain how telematics data can be routed via MQTT and efficiently stored to provide analytics and insights into the data.
Key Learnings
- Common challenges and pitfalls of IoT projects
- Required components for effectively handling data with an IoT platform
- HiveMQ for MQTT to enable bi-directional device communication over unstable networks
- MongoDB as the flexible and scalable modern data platform combining data from different sources and powering your applications
- Why MongoDB and HiveMQ is such a great combination
My perspective on the evolution of big data from the perspective of a distributed systems researcher & engineer -- the background of how it get started, the scale-out paradigm, industry use cases, open source development paradigm, and interesting future challenges.
Mike McBride will provide a look at the Industrial IoT (IIoT) landscape and the OT/IT convergence. He will cover several use cases including healthcare, entertainment and smart buildings. He will cover the challenges IIoT networking faces with emerging technologies and how edge computing will provide increased performance, security and reliability. Mike will discuss the various Edge Computing standards & opensource forums along with proposed architectures. And Mike will present new solutions being proposed (ICN, slicing, Blockchain) to support the bandwidth, latency and security requirements within Industrial verticals.
About the speaker: As Sr. Director of Innovation & Strategy, within Huawei's IP Network BU, Mike leads Industrial IoT, Edge Computing and IP/SDN architecture, standardization, and strategy across product lines and industry forums. He leads architecture and standardization activities within the IIc and BBF and has served as an IETF Working Group chair for 15 years. Mike has led emerging technology projects within opensource communities and played a key role in the formation of OPEN-O (Now ONAP). He is an Ericsson alum where he developed and directed SDN/NFV network architectures. And for many years with Cisco, Mike supported customers, worked in development teams and managed mobility, wireless and video projects across BUs. Mike began his career supporting customers at Apple Computer. He resides in Orange County, CA
The starting point for this project was a MapReduce application that processed log files produced by the support portal. This application was running on Hadoop with Ruby Wukong. At the time of the project start it was underperforming and did not show good scalability. This made the case for redesigning it using Spark with Scala and Java.
Initial review of the Ruby code revealed that it was using disk IO excessively, in order to communicate between MapReduce jobs. Each job was implemented as a separate script passing large data volumes through. Spark is more efficient in managing intermediate data passed between MapReduce jobs – not only it keeps it in memory whenever possible, it often eliminates the need for intermediate data at all. However, that alone not brought us much improvement since there were additional bottlenecks at data aggregation stages.
The application involved a global data ordering step, followed by several localized aggregation steps. This first global sort required significant data shuffle that was inefficient. Spark allowed us to partition the data and convert a single global sort into many local sorts, each running on a single node and not exchanging any data with other nodes. As a result, several data processing steps started to fit into node memory, which brought about a tenfold performance improvement.
How Cisco Migrated from MapReduce Jobs to Spark Jobs - StampedeCon 2015StampedeCon
At the StampedeCon 2015 Big Data Conference: The starting point for this project was a MapReduce application that processed log files produced by the support portal. This application was running on Hadoop with Ruby Wukong. At the time of the project start it was underperforming and did not show good scalability. This made the case for redesigning it using Spark with Scala and Java.
Initial review of the Ruby code revealed that it was using disk IO excessively, in order to communicate between MapReduce jobs. Each job was implemented as a separate script passing large data volumes through. Spark is more efficient in managing intermediate data passed between MapReduce jobs – not only it keeps it in memory whenever possible, it often eliminates the need for intermediate data at all. However, that alone not brought us much improvement since there were additional bottlenecks at data aggregation stages.
The application involved a global data ordering step, followed by several localized aggregation steps. This first global sort required significant data shuffle that was inefficient. Spark allowed us to partition the data and convert a single global sort into many local sorts, each running on a single node and not exchanging any data with other nodes. As a result, several data processing steps started to fit into node memory, which brought about a tenfold performance improvement.
AWS Summit Berlin 2013 - Big Data AnalyticsAWS Germany
Learn more about the tools, techniques and technologies for working productively with data at any scale. This session will introduce the family of data analytics tools on AWS which you can use to collect, compute and collaborate around data, from gigabytes to petabytes. We'll discuss Amazon Elastic MapReduce, Hadoop, structured and unstructured data, and the EC2 instance types which enable high performance analytics.
Xprize Think Tank Phoenix IoT Presentation 4/18/16Mark Goldstein
Mark Goldstein, President of International Research Center explored the next Internet wave, the Internet of Things (IoT), expected to connect tens of billions of new sensors and devices in the coming years with the Xprize Think Tank Phoenix Chapter (http://www.meetup.com/xprize-think-tank-phoenix/) on 4/18/16. Waves of change will roll through home, business, government, industrial, medical, transportation, and other complex ecosystems. This deck examines how IoT will be implemented and monetized creating new business models from pervasive sensor deployments and data gathering, accompanied by new privacy and security risks. Explore IoT’s roadblocks and operational challenges, emerging standards and protocols, gateway and wireless integration, and big data strategies and opportunities.
Note that this presentation is fresher though briefer than the one to the IEEE Computer Society Phoenix from 12/15 to be found at http://www.slideshare.net/markgirc/ieee-cs-phoenix-internet-of-things-innovations-megatrends-12215. This one stays at a somewhat higher level and includes newer material, but the other dives deeper into available devices and standards. Check them both out.
As a Presidio Fellow in Sustainability and Sports, at the Presidio Graduate School, San Francisco, CA, [http://www.presidio.edu/academics/presidiopro/certificates/sports- sustainability] I presented a class on energy efficiency and solar in sports stadiums and arenas. It covers related issues of advanced BIM (Building Information Modeling or Building Intelligence Management), Internet of Everything (IoT), continuous commissioning over building lifecycle, LED lighting systems, and more.
Session Sponsored by Intel: Smart Cities, Infrastructure and Health powered b...Amazon Web Services
Intel and AWS have helped to deliver advances in technology and infrastructure that are delivering economic value to IoT Solutions across many industries and segments. This session will discuss the benefits and impediments to adoption of IoT solutions and include case studies from Smart Buildings, Parking, Transportation and Health with security as a foundational pillar to all these IoT solutions.
Speaker: Andrew Hurren, Senior Regional Solution Architect, ANZ, Intel Security and Peter Kerney, Enterprise Solutions Architect, Intel
During the session we'll talk about IoT Solution based on Azure & AWS which is under active development phase at the moment. We will review product architecture and compare implementations on both of the cloud platforms as well as briefly take a look to the possible evolvements of the architecture to cover future needs. Also I'll share the main problems we've faced in during development process as well as cover solutions to them.
Phoenix Data Conference - Big Data Analytics for IoT 11/4/17Mark Goldstein
“Big Data for IoT: Analytics from Descriptive to Predictive to Prescriptive” was presented to the Phoenix Data Conference on 11/4/17 at Grand Canyon University.
As the Internet of Things (IoT) floods data lakes and fills data oceans with sensor and real-world data, analytic tools and real-time responsiveness will require improved platforms and applications to deal with the data flow and move from descriptive to predictive to prescriptive analysis and outcomes.
Similar to MongoDB and the Internet of Things (20)
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
During this talk we'll navigate through a customer's journey as they migrate an existing MongoDB deployment to MongoDB Atlas. While the migration itself can be as simple as a few clicks, the prep/post effort requires due diligence to ensure a smooth transfer. We'll cover these steps in detail and provide best practices. In addition, we’ll provide an overview of what to consider when migrating other cloud data stores, traditional databases and MongoDB imitations to MongoDB Atlas.
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB
MongoDB Kubernetes operator and MongoDB Open Service Broker are ready for production operations. Learn about how MongoDB can be used with the most popular container orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications. A demo will show you how easy it is to enable MongoDB clusters as an External Service using the Open Service Broker API for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB
Humana, like many companies, is tackling the challenge of creating real-time insights from data that is diverse and rapidly changing. This is our journey of how we used MongoDB to combined traditional batch approaches with streaming technologies to provide continues alerting capabilities from real-time data streams.
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe.
This talk covers:
Common components of an IoT solution
The challenges involved with managing time-series data in IoT applications
Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance.
How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts
At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB
Our clients have unique use cases and data patterns that mandate the choice of a particular strategy. To implement these strategies, it is mandatory that we unlearn a lot of relational concepts while designing and rapidly developing efficient applications on NoSQL. In this session, we will talk about some of our client use cases, the strategies we have adopted, and the features of MongoDB that assisted in implementing these strategies.
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB
Encryption is not a new concept to MongoDB. Encryption may occur in-transit (with TLS) and at-rest (with the encrypted storage engine). But MongoDB 4.2 introduces support for Client Side Encryption, ensuring the most sensitive data is encrypted before ever leaving the client application. Even full access to your MongoDB servers is not enough to decrypt this data. And better yet, Client Side Encryption can be enabled at the "flick of a switch".
This session covers using Client Side Encryption in your applications. This includes the necessary setup, how to encrypt data without sacrificing queryability, and what trade-offs to expect.
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB
MongoDB Kubernetes operator is ready for prime-time. Learn about how MongoDB can be used with most popular orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications.
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
When you need to model data, is your first instinct to start breaking it down into rows and columns? Mine used to be too. When you want to develop apps in a modern, agile way, NoSQL databases can be the best option. Come to this talk to learn how to take advantage of all that NoSQL databases have to offer and discover the benefits of changing your mindset from the legacy, tabular way of modeling data. We’ll compare and contrast the terms and concepts in SQL databases and MongoDB, explain the benefits of using MongoDB compared to SQL databases, and walk through data modeling basics so you feel confident as you begin using MongoDB.
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB
Query performance should be the unsung hero of an application, but without proper configuration, can become a constant headache. When used properly, MongoDB provides extremely powerful querying capabilities. In this session, we'll discuss concepts like equality, sort, range, managing query predicates versus sequential predicates, and best practices to building multikey indexes.
MongoDB .local San Francisco 2020: 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.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
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.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
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/
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
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/
11. CONNECTED COW
by VITAL HERD
E-pill ingested into stomach
Transmits heart rate, temp,
chemical composition
Notifies farmer when
abnormality is detected
Health management
94 Million Cows in US, Billions
of savings
21. SAMPLE DESIGN 1
EVENT_ID PLANE_ID TIMESTAMP LAT LONG ENGINE
TEMP
FUEL
LEVEL
… SPEED
100001 3902 1437297148810 38.2031 -124.4904
100002 3902 1437297149213 750
Modeling all metrics as columns in one relational table
Huge table, lots of wasted space caused by empty
values
Frequent schema change and data migrations when
adding new metrics
22. SAMPLE DESIGN 2
EVENT_ID METRIC_NAME METRIC_VALUE
100001 LAT 38.2031
100001 LONG -124.4904
100002 SPEED 750
Store variable metrics in an EAV table
EVENT_ID PLANE_ID TIMESTAMP
100001 3902 1437297148810
METRIC_VALUE needs be
defined as TEXT field
Index implication for
METRIC_VALUE field
Multiple self joins necessary
23. Enormous
Data
Volume
A single flight, per minute interval:
3 * 60 * 100 = 18K data points/flight
100,000 flights per day:
1.8 Billion, 1.8TB per day
21,000 QPS
28. AGILITY
Start coding now, without month long ER design.
Changing schema as you go without penalty.
Flexible schema models variable structure with ease
29. location: (-84.2391, 34.1039)
speed: 750
engine:
fuel_level: 100 ,
temperature: 88.48
DATA MODEL
1
3
2
1 Variable data structure
Sparse Indexes
Dynamic Schema
2
3
31. OPTIMIZE
With document model
A time series is
a sequence of data points,
typically consisting of successive
measurements
made over a time interval. Examples
of time series are ocean tides, counts
of sunspots, and the daily closing value
of the Dow Jones Industrial Average.
--wikipedia
35. CHOOSING A SHARD KEY FOR SENSORS
Cardinality - LARGE
Write distribution - EVEN
Query isolation – ISOLATED
36. CHOOSING A SHARD KEY
Cardinality
Write distribution
Query isolation
Reliability
Index locality
Cardinality
Write
Distribution
Query
Isolation
Reliability
Index
Locality
_id Doc level One shard Scatter/gather All users affected Good
hash(_id) Hash level All Shards Scatter/gather All users affected Poor
asset_id Many docs All Shards Targeted
Some assets
affected
Good
asset_id, ts Doc level All Shards Targeted
Some assets
affected
Good
43. 43
WE CAN HELP
MongoDB Enterprise Advanced
The best way to run MongoDB in your data center
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The easiest way to run MongoDB in your datacenter
Production Support
In production and under control
Development Support
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Get your teams up to speed.
Editor's Notes
Okay so what is IoT, or Internet of Things?
It is ranked as #2 or #3 buzzwords in the tech industry, depending who you prefer. According to profoundry, there’re 93 million Google search results and 36 thousand Twitter mentions within a 30 day period. According to Cloud Computing, IoT is second biggest buzzword and all of you know which one is the top:
Being a buzzword, it means there’re really bazillions of interpretations, which makes it hard to have a universally agreed definition.
The good thing about being a buzzword is everyone gets a chance to give it a spin. And my personal definition of IoT is Internet 4.0
Why 4.0?
The prototype of the Internet occurred in the 1960s but only in the 1980s did it start to mature. At first it was mostly used by the universities and research institutions. And we call it 1.0
In 1990s, the commercialization of internet took off as personal computers were connected to the network. This period can be considered as version 2.0.
Less than 10 years ago the invention of iPhone brought us into the version 3.0. As of today 3 billion people are connected, thanks to the proliferation of mobile devices, accounting for as much as 80% of connected devices.
From server, to PC, to mobile, finally we are expanding the reach to so-called things, devices that traditionally weren’t meant to be smart. Internet 4.0 is really an extension of today’s internet to devices that are equipped with sensors and network modules to allow the devices to sense its surroundings and to communicate the rest of the world.
We’re at the beta version of Internet 4.0. If you didn’t build a website during the 90s and didn’t create a iPhone app in 2008, now is your opportunity to catch the internet wave. Once the beta version becomes official, the competition will be a lot more severe.
Okay so what exactly do we need to know about IoT, from a technological perspective? Here I’m borrowing a few slides from postscapes.com, a website dedicated to tracking everything about Internet of Things. It worked with Harbor research to produce an very informative infographis. The folks there have done a phenomenal job visualizing the key aspects of the IoT, if you are somewhat new to IoT world, I’d highly recommend you to take a look at the full version.
In Postscapes version, there are three, or rather four critical components that drives the smart systems in our world: Sensors/Connectivity and People and Processes.
Sensors come at first when we talk about IoT, or Internet of Things. Sensor are the eyes and ears for the internet. In the early days, or 1.0, keyboard was the main interface between outside world and the internet. In 2.0 personal computer we added cameras and microphones. With mobile phones, GPS, accelerometer and the gyroscope are almost universal standard configuration. In IoT era, sensors now have an even wider range of options. Temperature, pressure, ambient light, velocity, displacement, humidity, torque strain, anything you want to know there is a sensor for it.
In addition to sensors, what’s equally important is the actuators that allow IoT applications to respond to the changing condition. For instance, a connected HVAC controller might automatically slightly raise the temperature to save energy when the cloud detects a spike in electricity use.
Sensors, especially those industry sensors are nothing new and they have been in existence for decades. What is different today is the connectivity to the network(internet) that brings it to the next level. With a variety of network protocols, such as WiFi, Bluetooth, Zigbee, Z-wave, GPRS, these sensors are now able to feed the knowledge, what they see, what they hear and what they sense, to the connected networks or to the cloud. Among these, Zigbee and Z-wave are specifically designed for the smart things. Compare to WiFi, Zigbee and Z-wave has much lower effective range but typically requires much lower footprint in terms of power consumption and hardware resources. Zigbee can often be found in chips which only have less than 100KB RAM.
We have the sensors and actuators as our input and output, we have the connectivity to bring the data in, real time, and on the same communication channel we can send commands out to those devices to respond to the changes. This kind of bi-directional infrastructure opened a new arena for us to design and build applications The reason I think Internet 4.0 is a better name is essentially these things are connected to us, the people and our everyday life, the internet we have grown to live without .
IoT applications are found in all industries today. Geofencing is a very popular for nursing homes, cattle management etc. Fleet management becomes real time with the GPS tracking and reporting. Agriculture is improving the produce by monitoring and surroundings and provide immediate feedback to growers. Crowd management becomes more effective with smart bracelet or mobile apps. Smart city and smart home are also the hot areas of IoT applications.
The city of Santander in northern Spain has 20,000 sensors connecting buildings, infrastructure, transport, networks and utilities。 These sensors monitor the levels of pollution, noise, traffic and parking and allow city management to make informed, timely decisions to best use the city resources.
Here we can look at a few exemplary use cases.
First is the connected cow app provided by Vital Herd. They produce an e-pill to be ingested by cows. The pill then sinks to the bottom of its stomach for the animal’s lifetime, transmitting information out about its vital signs: heart and respiratory rate, digestion information, core temperature and one day soon, the chemical composition of its stomach. The data are sent to the cloud where the data is normalized, create vital sign benchmarks for each animal and then deliver that information back to the producer in an easy-to-understand, actionable dashboard format.
This data offers new and early insight into productivity-limiting illnesses, suboptimal nutrition programs and even environmental factors such as heat stress that can reduce production.
In US alone there are 94 million cows, the potential savings the company has estimated to be billions.
John Deere uses sensors added to their latest equipment to help farmers manage their fleet and to decrease downtime of their tractors as well as save on fuel. The information is combined with historical and real-time data regarding weather prediction, soil conditions, crop features and many other data sets. The information is presented in the MyJohnDeere.com platform as well as on the iPad and iPhone app Mobile Farm Manager in order to helper farmers figure out which crops to plant where and when, when and where to plough, where the best return will be made with the crops and even which path to follow when ploughing.
All this will increase the productivity and efficiency of the crops that will in the end lead to higher production and revenue.
John Deere uses MongoDB to store the sensor data and provide real time analytics for the dashboards.
You may not realize, but the number sensors or connected devices are already at an astounding number. According to Cisco’s research, in 2015 there’re close to 20 billions of connections from the things. Put into perspective that’s about 3 sensors per person on the earth. And this number will only increase and by 2020, it will reach 50 billions.
Another interesting chart, is that Asia will be dominating other continents in terms of the IoT application deployments. This is partly due to the fact that 60% of the population is in Asia.
According to IDC, the market size for global IoT will reach 7.1 trillion. This is compared at 1.9T a couple of years ago. The IoT ecosystem includes intelligent and embedded systems shipments, connectivity services, infrastructure, purpose-built IoT platforms, applications, security, analytics, and professional services”
Here’s a diagram that depicts the relationship between these technology stacks. On the left you have sensors and actuators. The sensors typically works 24 x 7, sensing the surrounding information and gather the data, establish a connection typically to a local gateway, and sends the data over. The local gateway then may choose immediately relay or aggregate a bunch of data points then send over the internet to the centralized server, typically in the cloud or in enterprise data centers.
The use of gateway is optional. Sometimes the device may directly interact with a smart phone and the data interaction ends right there. And sometimes the sensor may be equipped with GPRS module thus can directly communicate with remote servers.
http://www.forbes.com/sites/huawei/2015/05/12/the-challenges-of-iot-to-build-a-better-connected-future/
Here let’s look at a few challenges that you might face if you’re going to work on an IoT application.
How many of you still remember MH370, the passenger plane vanished over Indian Ocean last year? After the incident lots of people are wondering, in today’s technology, why we couldn’t even track such a big monster?
Let’s use this example to look at what is involved.
The tracking data could be from multiple sources onboard the plane today. Such as ADS-C, HFDL, EUROCONTROL, ACARS(Airline Comm Addressing & Reporting Sys). A robust tracking may need to combine all these sources together to patch up the whole picture when the unfortunate event happens. For instance, it is reported the ACARS system was deliberately switched off by the pilot. In this case, multiple sources of data may help alleviate the difficulty of locating the plane afterwards.
Even when data comes from one source, depending on the nature of the data, they can have a big variance. For instance, we could be tracking the location data which comes in the form of a pair of coordinates, and engine parameters, which could come with a complex data structure such as a nested document representing the engine performances.
How do you model these data in database?
The not necessary very structured data poses a big challenge for traditional relational database, which works best when you have a clearly defined, well structured data models across all of the data points. For instance, if you were to naively design a huge table, each metric is a column. You would end up lots of wasted spaces for the null values for those rows that don’t have the data. The 2nd downside is that you would need to change the schema structure if you need to add more metrics to the data.
A better alternative is to use an EAV table, stands for Entity, Attribute, Value. The EAV pattern uses two table design. One table holds the data that typically that do not vary among different rows. For those varying data fields, such as LAT/LONG, SPEED, ENGINE, Fuel, that may occur in some rows but not others, use a separate metadata table.
This would appear to be a better design since it doesn’t have the empty cell problem. However, if you think carefully, the METRIC_VALUE column must be defined as a TEXT field, or a string long enough to hold the biggest possible metric value. You still end up some significant disk space waste and IO inefficiencies. Things get worse if you ever want to index on that column: its type variations will severely degrade your index performance.
Furthermore, this EAV design also has another drawback, in that it makes the query more difficult, Especially if you want to lookup a row/document based on multiple fields. For instance: find the planes within 100 miles of New York City with fuel level below 10%. You would need to do multiple joins on the AV table in order to be able to filter on multiple attribute fields.
So this is an example of the variable data structure problem I was trying to illustrate.
The other data challenge is the data volume issue. Again using the flight tracking. Assuming we’re sending data for various stats per minute level, some data may be more frequent some may be less frequent. For the sake of example here, we use a one-minute interval.
MongoDB was built to address the way the world has changed while preserving the core database capabilities required to build functional apps
MongoDB is the only database that harnesses the innovations of NoSQL and maintains the foundation of relational databases
What do we mean by Agility?
Variable structure example
You can create Indexes for selected fields, such as location. What happens with those documents without this field? Sparse
Dynamic schema
Many of sensor data cases fall into this time series pattern. Such as wind sensors, tide monitor, location tracking etc. It turns out the rich document model has another benefit for this type of data. Let’s use the engine fuel as an example
Don’t take my word for it – many of IoT apps out there are using MongoDB. Here are a few examples:
Kaa
IoTgo
Sitewhere
iKeg
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