This document summarizes a webinar about integrating Apache Kafka and MongoDB for data streaming. The webinar covered:
- An overview of Apache Kafka and how it can be used for data transport and integration as well as real-time stream processing.
- How MongoDB can be used as both a Kafka producer, to stream data into Kafka topics, and as a Kafka consumer, to retrieve streamed data from Kafka for storage, querying, and analytics in MongoDB.
- Various use cases for integrating Kafka and MongoDB, including handling real-time updates, storing raw and processed event data, and powering real-time applications with analytics models built from streamed data.
In the age of digital transformation and disruption, your ability to thrive depends on how you adapt to the constantly changing environment. MongoDB 3.4 is the latest release of the leading database for modern applications, a culmination of native database features and enhancements that will allow you to easily evolve your solutions to address emerging challenges and use cases.
In this webinar, we introduce you to what’s new, including:
- Multimodel Done Right. Native graph computation, faceted navigation, rich real-time analytics, and powerful connectors for BI and Apache Spark bring additional multimodel database support right into MongoDB.
- Mission-Critical Applications. Geo-distributed MongoDB zones, elastic clustering, tunable consistency, and enhanced security controls bring state-of-the-art database technology to your most mission-critical applications.
- Modernized Tooling. Enhanced DBA and DevOps tooling for schema management, fine-grained monitoring, and cloud-native integration allow engineering teams to ship applications faster, with less overhead and higher quality.
The rise of microservices - containers and orchestrationAndrew Morgan
Organisations are building their applications around microservice architectures because of the flexibility, speed of delivery, and maintainability they deliver. In this session, the concepts behind containers and orchestration will be explained and how to use them with MongoDB.
Powering Microservices with MongoDB, Docker, Kubernetes & Kafka – MongoDB Eur...Andrew Morgan
Organisations are building their applications around microservice architectures because of the flexibility, speed of delivery, and maintainability they deliver.
Want to try out MongoDB on your laptop? Execute a single command and you have a lightweight, self-contained sandbox; another command removes all trace when you're done. Need an identical copy of your application stack in multiple environments? Build your own container image and then your entire development, test, operations, and support teams can launch an identical clone environment.
Containers are revolutionizing the entire software lifecycle: from the earliest technical experiments and proofs of concept through development, test, deployment, and support. Orchestration tools manage how multiple containers are created, upgraded and made highly available. Orchestration also controls how containers are connected to build sophisticated applications from multiple, microservice containers.
This presentation introduces you to technologies such as Docker, Kubernetes & Kafka which are driving the microservices revolution. Learn about containers and orchestration – and most importantly how to exploit them for stateful services such as MongoDB.
MongoDB Europe 2016 - Deploying MongoDB on NetApp storageMongoDB
The document discusses how NetApp storage solutions can maximize investments in MongoDB. It describes MongoDB's internal storage pain points around performance, scalability, management and data movement. NetApp solutions like AFF, EF-Series and SolidFire are presented as optimizing MongoDB deployments by accelerating performance, increasing availability, eliminating data sprawl and streamlining data lifecycle management. Customer use cases demonstrate how NetApp has helped companies improve analytics, scale workloads efficiently and simplify backup/restore processes for MongoDB.
MongoDB Atlas is a database as a service for MongoDB that allows users to focus on building applications rather than managing infrastructure. It provides scalable and highly available MongoDB clusters in the cloud that automatically handle backups, patching, upgrades and maintenance. MongoDB Atlas offers various pricing and consumption models to suit different use cases. It has integrations with tools and services like MongoDB Compass and supports MongoDB versions 3.2 and 3.4.
Unlocking Operational Intelligence from the Data LakeMongoDB
The document discusses unlocking operational intelligence from data lakes using MongoDB. It begins by describing how digital transformation is driving changes in data volume, velocity, and variety. It then discusses how MongoDB can help operationalize data lakes by providing real-time access and analytics on data stored in data lakes, while also integrating batch processing capabilities. The document provides an example reference architecture of how MongoDB can be used with a data lake (Hadoop) and stream processing framework (Kafka) to power operational applications and machine learning models with both real-time and batch data and analytics.
MongoDB Europe 2016 - Choosing Between 100 Billion Travel Options – Instant S...MongoDB
Travellers are demanding more exhaustive, accurate, and relevant results when they search for flights, and they want these results instantly – even when there can be 100 Billion travel options for a single trip. Amadeus’s “Instant Search” feature was built to meet those requirements. These searches are not trivial. Several terabytes of constantly evolving data is needed to reply instantly to questions like, “I live in Frankfurt, where can I go this weekend for €200?” “What’s the cheapest and most convenient flight for a MongoDB Europe attendee?” This technical session will show you how Amadeus integrated MongoDB into its system, and how it allowed us to handle huge numbers of updates and searches in a high-volume system, to deliver the next generation of flight search products. It will cover topics such as how we discovered which extra indexes were needed, how we were able to get the balancer to meet our needs, and how we modelled our data for optimal performance.
In the age of digital transformation and disruption, your ability to thrive depends on how you adapt to the constantly changing environment. MongoDB 3.4 is the latest release of the leading database for modern applications, a culmination of native database features and enhancements that will allow you to easily evolve your solutions to address emerging challenges and use cases.
In this webinar, we introduce you to what’s new, including:
- Multimodel Done Right. Native graph computation, faceted navigation, rich real-time analytics, and powerful connectors for BI and Apache Spark bring additional multimodel database support right into MongoDB.
- Mission-Critical Applications. Geo-distributed MongoDB zones, elastic clustering, tunable consistency, and enhanced security controls bring state-of-the-art database technology to your most mission-critical applications.
- Modernized Tooling. Enhanced DBA and DevOps tooling for schema management, fine-grained monitoring, and cloud-native integration allow engineering teams to ship applications faster, with less overhead and higher quality.
The rise of microservices - containers and orchestrationAndrew Morgan
Organisations are building their applications around microservice architectures because of the flexibility, speed of delivery, and maintainability they deliver. In this session, the concepts behind containers and orchestration will be explained and how to use them with MongoDB.
Powering Microservices with MongoDB, Docker, Kubernetes & Kafka – MongoDB Eur...Andrew Morgan
Organisations are building their applications around microservice architectures because of the flexibility, speed of delivery, and maintainability they deliver.
Want to try out MongoDB on your laptop? Execute a single command and you have a lightweight, self-contained sandbox; another command removes all trace when you're done. Need an identical copy of your application stack in multiple environments? Build your own container image and then your entire development, test, operations, and support teams can launch an identical clone environment.
Containers are revolutionizing the entire software lifecycle: from the earliest technical experiments and proofs of concept through development, test, deployment, and support. Orchestration tools manage how multiple containers are created, upgraded and made highly available. Orchestration also controls how containers are connected to build sophisticated applications from multiple, microservice containers.
This presentation introduces you to technologies such as Docker, Kubernetes & Kafka which are driving the microservices revolution. Learn about containers and orchestration – and most importantly how to exploit them for stateful services such as MongoDB.
MongoDB Europe 2016 - Deploying MongoDB on NetApp storageMongoDB
The document discusses how NetApp storage solutions can maximize investments in MongoDB. It describes MongoDB's internal storage pain points around performance, scalability, management and data movement. NetApp solutions like AFF, EF-Series and SolidFire are presented as optimizing MongoDB deployments by accelerating performance, increasing availability, eliminating data sprawl and streamlining data lifecycle management. Customer use cases demonstrate how NetApp has helped companies improve analytics, scale workloads efficiently and simplify backup/restore processes for MongoDB.
MongoDB Atlas is a database as a service for MongoDB that allows users to focus on building applications rather than managing infrastructure. It provides scalable and highly available MongoDB clusters in the cloud that automatically handle backups, patching, upgrades and maintenance. MongoDB Atlas offers various pricing and consumption models to suit different use cases. It has integrations with tools and services like MongoDB Compass and supports MongoDB versions 3.2 and 3.4.
Unlocking Operational Intelligence from the Data LakeMongoDB
The document discusses unlocking operational intelligence from data lakes using MongoDB. It begins by describing how digital transformation is driving changes in data volume, velocity, and variety. It then discusses how MongoDB can help operationalize data lakes by providing real-time access and analytics on data stored in data lakes, while also integrating batch processing capabilities. The document provides an example reference architecture of how MongoDB can be used with a data lake (Hadoop) and stream processing framework (Kafka) to power operational applications and machine learning models with both real-time and batch data and analytics.
MongoDB Europe 2016 - Choosing Between 100 Billion Travel Options – Instant S...MongoDB
Travellers are demanding more exhaustive, accurate, and relevant results when they search for flights, and they want these results instantly – even when there can be 100 Billion travel options for a single trip. Amadeus’s “Instant Search” feature was built to meet those requirements. These searches are not trivial. Several terabytes of constantly evolving data is needed to reply instantly to questions like, “I live in Frankfurt, where can I go this weekend for €200?” “What’s the cheapest and most convenient flight for a MongoDB Europe attendee?” This technical session will show you how Amadeus integrated MongoDB into its system, and how it allowed us to handle huge numbers of updates and searches in a high-volume system, to deliver the next generation of flight search products. It will cover topics such as how we discovered which extra indexes were needed, how we were able to get the balancer to meet our needs, and how we modelled our data for optimal performance.
Data Streaming with Apache Kafka & MongoDBconfluent
Explore the use-cases and architecture for Apache Kafka, and how it integrates with MongoDB to build sophisticated data-driven applications that exploit new sources of data.
Presented by Claudius Li, Solutions Architect at MongoDB, at MongoDB Evenings New England 2017.
MongoDB Atlas is the premier database as a service offering. Find out how MongoDB Atlas can help your team to deploy more easily, develop faster and easily manage deployment, maintenance, upgrades and expansions. We will also demonstrate some of the key features and tools that come with MongoDB Atlas.
Introducing Change Data Capture with DebeziumChengKuan Gan
This document discusses change data capture (CDC) and how it can be used to stream change events from databases. It introduces Debezium, an open source CDC platform that captures change events from transaction logs. Debezium supports capturing changes from multiple databases and transmitting them as a stream of events. The summary discusses how CDC can be used for data replication between databases, auditing, and in microservices architectures. It also covers deployment of CDC on Kubernetes using OpenShift.
Apache Kafka - Scalable Message-Processing and more !Guido Schmutz
Presentation @ Oracle Code Berlin.
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. How can we make sure that all these events are accepted and forwarded in an efficient and reliable way? This is where Apache Kafaka comes into play, a distirbuted, highly-scalable messaging broker, build for exchanging huge amounts of messages between a source and a target. This session will start with an introduction of Apache and presents the role of Apache Kafka in a modern data / information architecture and the advantages it brings to the table.
MongoDB Breakfast Milan - Mainframe Offloading StrategiesMongoDB
The document summarizes a MongoDB event focused on modernizing mainframe applications. The event agenda includes presentations on moving from mainframes to operational data stores, demo of a mainframe offloading solution from Quantyca, and stories of mainframe modernization. Benefits of using MongoDB for mainframe modernization include 5-10x developer productivity and 80% reduction in mainframe costs.
This session will begin with a short recap of how we created systems over the past 20 years, up to the current idea of building systems, using a Microservices architecture. What is a Microservices architecture and how does it differ from a Service-Oriented Architecture? Should you use traditional REST APIs to integrate services with each eachother in a Microservcies Architecture? Or is it better to use a more loosely-coupled protocol? Answers to these and many other questions are provided. The talk will show how a distributed log (event hub) can help to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk shows the difference between a request-driven and event-driven communication and answers when to use which. It highlights how a modern stream processing systems can be used to hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...confluent
Tinder’s Quickfire Pipeline powers all things data at Tinder. It was originally built using AWS Kinesis Firehoses and has since been extended to use both Kafka and other event buses. It is the core of Tinder’s data infrastructure. This rich data flow of both client and backend data has been extended to service a variety of needs at Tinder, including Experimentation, ML, CRM, and Observability, allowing backend developers easier access to shared client side data. We perform this using many systems, including Kafka, Spark, Flink, Kubernetes, and Prometheus. Many of Tinder’s systems were natively designed in an RPC first architecture.
Things we’ll discuss decoupling your system at scale via event-driven architectures include:
– Powering ML, backend, observability, and analytical applications at scale, including an end to end walk through of our processes that allow non-programmers to write and deploy event-driven data flows.
– Show end to end the usage of dynamic event processing that creates other stream processes, via a dynamic control plane topology pattern and broadcasted state pattern
– How to manage the unavailability of cached data that would normally come from repeated API calls for data that’s being backfilled into Kafka, all online! (and why this is not necessarily a “good” idea)
– Integrating common OSS frameworks and libraries like Kafka Streams, Flink, Spark and friends to encourage the best design patterns for developers coming from traditional service oriented architectures, including pitfalls and lessons learned along the way.
– Why and how to avoid overloading microservices with excessive RPC calls from event-driven streaming systems
– Best practices in common data flow patterns, such as shared state via RocksDB + Kafka Streams as well as the complementary tools in the Apache Ecosystem.
– The simplicity and power of streaming SQL with microservices
Spark (Structured) Streaming vs. Kafka Streams - two stream processing platfo...Guido Schmutz
Spark Streaming and Kafka Streams are two popular stream processing platforms. Spark Streaming uses micro-batching and allows for code reuse between batch and streaming jobs. Kafka Streams is embedded directly into Apache Kafka and leverages Kafka as its internal messaging layer. Both platforms support stateful stream processing operations like windowing, aggregations, and joins through distributed state stores. A demo application is shown that detects dangerous driving by joining truck position data with driver data using different streaming techniques.
Building event-driven (Micro)Services with Apache Kafka EcosystemGuido Schmutz
Should you use traditional REST APIs to bind services together? Or is it better to use a richer, more loosely-coupled protocol? This talk will dive into how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk will show the difference between a request-driven and event-driven communication and show when to use which. It highlights how the modern stream processing systems can be used to hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
This document discusses Redis, MongoDB, and Amazon DynamoDB. It begins with an overview of NoSQL databases and the differences between SQL and NoSQL databases. It then covers Redis data types like strings, hashes, lists, sets, sorted sets, and streams. Examples use cases for Redis are also provided like leaderboards, geospatial queries, and message queues. The document also discusses MongoDB design patterns like embedding data, embracing duplication, and relationships. Finally, it provides a high-level overview of DynamoDB concepts like tables, items, attributes, and primary keys.
Webinar: Simplifying the Database Experience with MongoDB AtlasMongoDB
MongoDB Atlas is our database as a service for MongoDB. In this webinar you’ll learn how it provides all of the features of MongoDB, without all of the operational heavy lifting, and all through a pay-as-you-go model billed on an hourly basis.
Building a Secure, Tamper-Proof & Scalable Blockchain on Top of Apache Kafka ...confluent
Apache Kafka is an open source event streaming platform. It is often used to complement or even replace existing middleware to integrate applications and build microservice architectures. Apache Kafka is already used in various projects in almost every bigger company today. Understood, battled-tested, highly scalable, reliable, real-time.
Blockchain is a different story. This technology is a lot in the news, especially related to cryptocurrencies like Bitcoin. But what is the added value for software architectures? Is blockchain just hype and adds complexity? Or will it be used by everybody in the future, like a web browser or mobile app today? And how is it related to an integration architecture and event streaming platform?
This session explores use cases for blockchains and discusses different alternatives such as Hyperledger, Ethereum and a Kafka-native tamper-proof blockchain implementation. Different architectures are discussed to understand when blockchain really adds value and how it can be combined with the Apache Kafka ecosystem to integrate blockchain with the rest of the enterprise architecture to build a highly scalable and reliable event streaming infrastructure.
Speakers:
Kai Waehner, Technology Evangelist, Confluent
Stephen Reed, CTO, Co-Founder, AiB
Streaming Data in the Cloud with Confluent and MongoDB Atlas | Robert Walters...HostedbyConfluent
This document discusses streaming data between Confluent Cloud and MongoDB Atlas. It provides an overview of MongoDB Atlas and its fully managed database capabilities in the cloud. It then demonstrates how to stream data from a Python generator application to MongoDB Atlas using Confluent Cloud and its connectors. The presentation concludes by providing a reference architecture for connecting Confluent Platform to MongoDB.
Building Cloud-Native App Series - Part 3 of 11
Microservices Architecture Series
AWS Kinesis Data Streams
AWS Kinesis Firehose
AWS Kinesis Data Analytics
Apache Flink - Analytics
Serverless Architectures with AWS Lambda and MongoDB Atlas by Sig NarvaezData Con LA
Abstract:- It's easier than ever to power serverless architectures with managed database services like MongoDB Atlas. In this session, we will explore the rise of serverless architectures and how they've rapidly integrated into public and private cloud offerings. We will demonstrate how to build a simple REST API using AWS Lambda functions, create a highly available cluster in MongoDB Atlas, and connect both via VCP Peering. We will then simulate load and use the monitoring and scale features of MongoDB Atlas and use MongoDB Compass to browse our database.
MongoDB .local Chicago 2019: MongoDB Atlas JumpstartMongoDB
Join this talk and test session with MongoDB Support where you'll go over the configuration and deployment of an Atlas environment. Setup a service that you can take back in a production-ready state and prepare to unleash your inner genius.
A guide through the Azure Messaging services - Update ConferenceEldert Grootenboer
https://www.updateconference.net/en/2019/session/a-guide-through-the-azure-messaging-services
A guide through the Azure Messaging services - Update Conference
Ingesting streaming data into Graph DatabaseGuido Schmutz
This talk presents the experience of a customer project where we built a stream-based ingestion into a graph database. It is one thing to load the graph first and then querying it. But it is another story if the data to be added to the graph is constantly streaming in, while querying it. Data is easy to add, if each single message ends up as a new vertex in the graph. But if a message consists of hierarchical information, it most often means creating multiple new vertices as well adding edges to connect this information. What if a node already exists in the graph? Do we create it again or do we rather add edges which link to the existing node? Creating multiple nodes for the same real-life entity is not the best choice, so we have to check for existence first. We end up requiring multiple operations against the graph, which demonstrated to be a bottle neck. This talk presents the implementation of an ingestion pipeline and the design choice we made to improve performance.
Should you use traditional REST APIs to bind services together? Or is it better to use a richer, more loosely-coupled protocol? This talk will dig into how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk will show the difference between a request-driven and event-driven communication and show when to use which. It highlights how the modern stream processing systems can be used to
hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
Data Streaming with Apache Kafka & MongoDB - EMEAAndrew Morgan
A new generation of technologies is needed to consume and exploit today's real time, fast moving data sources. Apache Kafka, originally developed at LinkedIn, has emerged as one of these key new technologies.
This webinar explores the use-cases and architecture for Kafka, and how it integrates with MongoDB to build sophisticated data-driven applications that exploit new sources of data.
Webinar: Data Streaming with Apache Kafka & MongoDBMongoDB
A new generation of technologies is needed to consume and exploit today's real time, fast moving data sources. Apache Kafka, originally developed at LinkedIn, has emerged as one of these key new technologies.
Data Streaming with Apache Kafka & MongoDBconfluent
Explore the use-cases and architecture for Apache Kafka, and how it integrates with MongoDB to build sophisticated data-driven applications that exploit new sources of data.
Presented by Claudius Li, Solutions Architect at MongoDB, at MongoDB Evenings New England 2017.
MongoDB Atlas is the premier database as a service offering. Find out how MongoDB Atlas can help your team to deploy more easily, develop faster and easily manage deployment, maintenance, upgrades and expansions. We will also demonstrate some of the key features and tools that come with MongoDB Atlas.
Introducing Change Data Capture with DebeziumChengKuan Gan
This document discusses change data capture (CDC) and how it can be used to stream change events from databases. It introduces Debezium, an open source CDC platform that captures change events from transaction logs. Debezium supports capturing changes from multiple databases and transmitting them as a stream of events. The summary discusses how CDC can be used for data replication between databases, auditing, and in microservices architectures. It also covers deployment of CDC on Kubernetes using OpenShift.
Apache Kafka - Scalable Message-Processing and more !Guido Schmutz
Presentation @ Oracle Code Berlin.
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. How can we make sure that all these events are accepted and forwarded in an efficient and reliable way? This is where Apache Kafaka comes into play, a distirbuted, highly-scalable messaging broker, build for exchanging huge amounts of messages between a source and a target. This session will start with an introduction of Apache and presents the role of Apache Kafka in a modern data / information architecture and the advantages it brings to the table.
MongoDB Breakfast Milan - Mainframe Offloading StrategiesMongoDB
The document summarizes a MongoDB event focused on modernizing mainframe applications. The event agenda includes presentations on moving from mainframes to operational data stores, demo of a mainframe offloading solution from Quantyca, and stories of mainframe modernization. Benefits of using MongoDB for mainframe modernization include 5-10x developer productivity and 80% reduction in mainframe costs.
This session will begin with a short recap of how we created systems over the past 20 years, up to the current idea of building systems, using a Microservices architecture. What is a Microservices architecture and how does it differ from a Service-Oriented Architecture? Should you use traditional REST APIs to integrate services with each eachother in a Microservcies Architecture? Or is it better to use a more loosely-coupled protocol? Answers to these and many other questions are provided. The talk will show how a distributed log (event hub) can help to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk shows the difference between a request-driven and event-driven communication and answers when to use which. It highlights how a modern stream processing systems can be used to hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...confluent
Tinder’s Quickfire Pipeline powers all things data at Tinder. It was originally built using AWS Kinesis Firehoses and has since been extended to use both Kafka and other event buses. It is the core of Tinder’s data infrastructure. This rich data flow of both client and backend data has been extended to service a variety of needs at Tinder, including Experimentation, ML, CRM, and Observability, allowing backend developers easier access to shared client side data. We perform this using many systems, including Kafka, Spark, Flink, Kubernetes, and Prometheus. Many of Tinder’s systems were natively designed in an RPC first architecture.
Things we’ll discuss decoupling your system at scale via event-driven architectures include:
– Powering ML, backend, observability, and analytical applications at scale, including an end to end walk through of our processes that allow non-programmers to write and deploy event-driven data flows.
– Show end to end the usage of dynamic event processing that creates other stream processes, via a dynamic control plane topology pattern and broadcasted state pattern
– How to manage the unavailability of cached data that would normally come from repeated API calls for data that’s being backfilled into Kafka, all online! (and why this is not necessarily a “good” idea)
– Integrating common OSS frameworks and libraries like Kafka Streams, Flink, Spark and friends to encourage the best design patterns for developers coming from traditional service oriented architectures, including pitfalls and lessons learned along the way.
– Why and how to avoid overloading microservices with excessive RPC calls from event-driven streaming systems
– Best practices in common data flow patterns, such as shared state via RocksDB + Kafka Streams as well as the complementary tools in the Apache Ecosystem.
– The simplicity and power of streaming SQL with microservices
Spark (Structured) Streaming vs. Kafka Streams - two stream processing platfo...Guido Schmutz
Spark Streaming and Kafka Streams are two popular stream processing platforms. Spark Streaming uses micro-batching and allows for code reuse between batch and streaming jobs. Kafka Streams is embedded directly into Apache Kafka and leverages Kafka as its internal messaging layer. Both platforms support stateful stream processing operations like windowing, aggregations, and joins through distributed state stores. A demo application is shown that detects dangerous driving by joining truck position data with driver data using different streaming techniques.
Building event-driven (Micro)Services with Apache Kafka EcosystemGuido Schmutz
Should you use traditional REST APIs to bind services together? Or is it better to use a richer, more loosely-coupled protocol? This talk will dive into how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk will show the difference between a request-driven and event-driven communication and show when to use which. It highlights how the modern stream processing systems can be used to hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
This document discusses Redis, MongoDB, and Amazon DynamoDB. It begins with an overview of NoSQL databases and the differences between SQL and NoSQL databases. It then covers Redis data types like strings, hashes, lists, sets, sorted sets, and streams. Examples use cases for Redis are also provided like leaderboards, geospatial queries, and message queues. The document also discusses MongoDB design patterns like embedding data, embracing duplication, and relationships. Finally, it provides a high-level overview of DynamoDB concepts like tables, items, attributes, and primary keys.
Webinar: Simplifying the Database Experience with MongoDB AtlasMongoDB
MongoDB Atlas is our database as a service for MongoDB. In this webinar you’ll learn how it provides all of the features of MongoDB, without all of the operational heavy lifting, and all through a pay-as-you-go model billed on an hourly basis.
Building a Secure, Tamper-Proof & Scalable Blockchain on Top of Apache Kafka ...confluent
Apache Kafka is an open source event streaming platform. It is often used to complement or even replace existing middleware to integrate applications and build microservice architectures. Apache Kafka is already used in various projects in almost every bigger company today. Understood, battled-tested, highly scalable, reliable, real-time.
Blockchain is a different story. This technology is a lot in the news, especially related to cryptocurrencies like Bitcoin. But what is the added value for software architectures? Is blockchain just hype and adds complexity? Or will it be used by everybody in the future, like a web browser or mobile app today? And how is it related to an integration architecture and event streaming platform?
This session explores use cases for blockchains and discusses different alternatives such as Hyperledger, Ethereum and a Kafka-native tamper-proof blockchain implementation. Different architectures are discussed to understand when blockchain really adds value and how it can be combined with the Apache Kafka ecosystem to integrate blockchain with the rest of the enterprise architecture to build a highly scalable and reliable event streaming infrastructure.
Speakers:
Kai Waehner, Technology Evangelist, Confluent
Stephen Reed, CTO, Co-Founder, AiB
Streaming Data in the Cloud with Confluent and MongoDB Atlas | Robert Walters...HostedbyConfluent
This document discusses streaming data between Confluent Cloud and MongoDB Atlas. It provides an overview of MongoDB Atlas and its fully managed database capabilities in the cloud. It then demonstrates how to stream data from a Python generator application to MongoDB Atlas using Confluent Cloud and its connectors. The presentation concludes by providing a reference architecture for connecting Confluent Platform to MongoDB.
Building Cloud-Native App Series - Part 3 of 11
Microservices Architecture Series
AWS Kinesis Data Streams
AWS Kinesis Firehose
AWS Kinesis Data Analytics
Apache Flink - Analytics
Serverless Architectures with AWS Lambda and MongoDB Atlas by Sig NarvaezData Con LA
Abstract:- It's easier than ever to power serverless architectures with managed database services like MongoDB Atlas. In this session, we will explore the rise of serverless architectures and how they've rapidly integrated into public and private cloud offerings. We will demonstrate how to build a simple REST API using AWS Lambda functions, create a highly available cluster in MongoDB Atlas, and connect both via VCP Peering. We will then simulate load and use the monitoring and scale features of MongoDB Atlas and use MongoDB Compass to browse our database.
MongoDB .local Chicago 2019: MongoDB Atlas JumpstartMongoDB
Join this talk and test session with MongoDB Support where you'll go over the configuration and deployment of an Atlas environment. Setup a service that you can take back in a production-ready state and prepare to unleash your inner genius.
A guide through the Azure Messaging services - Update ConferenceEldert Grootenboer
https://www.updateconference.net/en/2019/session/a-guide-through-the-azure-messaging-services
A guide through the Azure Messaging services - Update Conference
Ingesting streaming data into Graph DatabaseGuido Schmutz
This talk presents the experience of a customer project where we built a stream-based ingestion into a graph database. It is one thing to load the graph first and then querying it. But it is another story if the data to be added to the graph is constantly streaming in, while querying it. Data is easy to add, if each single message ends up as a new vertex in the graph. But if a message consists of hierarchical information, it most often means creating multiple new vertices as well adding edges to connect this information. What if a node already exists in the graph? Do we create it again or do we rather add edges which link to the existing node? Creating multiple nodes for the same real-life entity is not the best choice, so we have to check for existence first. We end up requiring multiple operations against the graph, which demonstrated to be a bottle neck. This talk presents the implementation of an ingestion pipeline and the design choice we made to improve performance.
Should you use traditional REST APIs to bind services together? Or is it better to use a richer, more loosely-coupled protocol? This talk will dig into how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk will show the difference between a request-driven and event-driven communication and show when to use which. It highlights how the modern stream processing systems can be used to
hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
Data Streaming with Apache Kafka & MongoDB - EMEAAndrew Morgan
A new generation of technologies is needed to consume and exploit today's real time, fast moving data sources. Apache Kafka, originally developed at LinkedIn, has emerged as one of these key new technologies.
This webinar explores the use-cases and architecture for Kafka, and how it integrates with MongoDB to build sophisticated data-driven applications that exploit new sources of data.
Webinar: Data Streaming with Apache Kafka & MongoDBMongoDB
A new generation of technologies is needed to consume and exploit today's real time, fast moving data sources. Apache Kafka, originally developed at LinkedIn, has emerged as one of these key new technologies.
Unlocking Operational Intelligence from the Data LakeMongoDB
The document discusses operationalizing data lakes by integrating MongoDB with Hadoop to enable both real-time and batch processing capabilities. It describes how MongoDB can be used to power operational applications with low-latency access to analytics models generated from raw data stored in Hadoop, while Hadoop is still used for its batch processing and analytics capabilities on large datasets. By combining both technologies, companies can unlock insights from their data lakes and avoid being part of the 70% of Hadoop projects that fail to meet objectives due to skills and integration challenges.
MongoDB .local London 2019: Streaming Data on the Shoulders of GiantsMongoDB
Life doesn't happen in batch mode which is why application engineers and data architects need to closely cooperate to get the best out of streaming platforms like Apache Kafka and NoSQL data stores such as MongoDB. This session explores ways and means to integrate both worlds in a streaming fashion.
MongoDB .local London 2019: Streaming Data on the Shoulders of GiantsLisa Roth, PMP
Life doesn't happen in batch mode which is why application engineers and data architects need to closely cooperate to get the best out of streaming platforms like Apache Kafka and NoSQL data stores such as MongoDB. This session explores ways and means to integrate both worlds in a streaming fashion.
MongoDB and Hadoop: Driving Business InsightsMongoDB
MongoDB and Hadoop can work together to solve big data problems facing today's enterprises. We will take an in-depth look at how the two technologies complement and enrich each other with complex analyses and greater intelligence. We will take a deep dive into the MongoDB Connector for Hadoop and how it can be applied to enable new business insights with MapReduce, Pig, and Hive, and demo a Spark application to drive product recommendations.
Creating a Modern Data Architecture for Digital TransformationMongoDB
By managing Data in Motion, Data at Rest, and Data in Use differently, modern Information Management Solutions are enabling a whole range of architecture and design patterns that allow enterprises to fully harness the value in data flowing through their systems. In this session we explored some of the patterns (e.g. operational data lakes, CQRS, microservices and containerisation) that enable CIOs, CDOs and senior architects to tame the data challenge, and start to use data as a cross-enterprise asset.
The document discusses MongoDB and Hadoop. It provides an overview of how MongoDB and Hadoop can be used together, including use cases in commerce, insurance and fraud detection. It describes the MongoDB Connector for Hadoop, which allows reading and writing to MongoDB from Hadoop tools like MapReduce, Pig and Hive. The document concludes with a demo of a movie recommendation platform that uses both MongoDB and Spark on Hadoop to power a movie browsing web application and generate recommendations.
The document discusses MongoDB and Hadoop. It provides an overview of how MongoDB and Hadoop can be used together, including use cases in commerce, insurance and fraud detection. It describes the MongoDB Connector for Hadoop, which allows reading and writing to MongoDB from Hadoop tools like MapReduce, Pig and Hive. A demo is shown of a movie recommendation application that uses both MongoDB and Spark on Hadoop to power a web application.
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
This presentation discusses migrating data from other data stores to MongoDB Atlas. It begins by explaining why MongoDB and Atlas are good choices for data management. Several preparation steps are covered, including sizing the target Atlas cluster, increasing the source oplog, and testing connectivity. Live migration, mongomirror, and dump/restore options are presented for migrating between replicasets or sharded clusters. Post-migration steps like monitoring and backups are also discussed. Finally, migrating from other data stores like AWS DocumentDB, Azure CosmosDB, DynamoDB, and relational databases are briefly covered.
Accelerating a Path to Digital With a Cloud Data StrategyMongoDB
The document describes a conference on accelerating a path to digital transformation with a cloud data strategy. It provides an agenda for the conference including speakers on executing a cloud data strategy, customer stories from De Persgroep and Toyota Motor Europe, and a session on landing in the cloud with MongoDB Atlas. The document also provides background on the speakers and their companies.
MongoDB Days Silicon Valley: Jumpstart: The Right and Wrong Use Cases for Mon...MongoDB
Presented by Sigfrido Narvaez, Senior Solutions Architect, MongoDB
Experience level: Introductory
When it comes time to select database software for your project, there are a bewildering number of choices. How do you know if your project is a good fit for a relational database, or whether one of the many NoSQL options is a better choice? In this session you will learn when to use MongoDB and how to evaluate if MongoDB is a fit for your project. You will see how MongoDB's flexible document model is solving business problems in ways that were not previously possible, and how MongoDB's built-in features allow running at scale.
Speaker: Jay Gordon
MongoDB has grown into one of the world's more popular databases and continues to expand it's reach to developers. In this talk we will discuss MongoDB foundations that attendees can use to begin their journey in creating new apps. By the end of the talk, members attending should feel prepared for the rest of their time at MongoDB World with essential information on how MongoDB works. There is no need to have previous experience with MongoDB to attend this talk, however basic understanding of database systems is recommended.
Accelerating a Path to Digital with a Cloud Data StrategyMongoDB
1) The document discusses accelerating a path to digital transformation with a cloud data strategy. It covers topics like the seismic shifts in organizations and application architectures, and the need to rethink underlying data layers.
2) The presentation discusses building an enterprise data fabric at Royal Bank of Scotland using MongoDB to provide data storage, query, and distribution as a service. This simplified development, reduced costs, and improved velocity.
3) MongoDB was presented as the foundation for cloud data strategies, providing the freedom to run applications anywhere while leveraging the benefits of multiple clouds.
MongoDB 3.4 is a multi-model database that supports documents, relational data, key-value, and graph structures. It features new capabilities like faceted navigation for advanced analytics, views for mission critical applications, and intra-cluster compression. MongoDB also provides enterprise tools like Ops Manager for high resolution monitoring, Compass for visual data exploration, and connectors for BI and SQL tools.
As an official MongoDB-as-a-Service offering from MongoDB Inc., the maker for MongoDB, Atlas is becoming a very popular service offering for those who wish to build their applications in the cloud, regardless on AWS, Azure or GCP. One less known cloud product offered on the Atlas platform is Stitch, A group of services designed to interact with Atlas in every conceivable way, including creating endpoints, triggers, user authentication flows, serverless functions, and a UI to handle all of this. Adding these together, you have a server-less solution running on top of MongoDB cloud.
Choose Right Stream Storage: Amazon Kinesis Data Streams vs MSKSungmin Kim
This presentation compares Amazon Kinesis Data Streams to Managed Streaming for Kafka (MSK) in both architectural perspective and operational perspective. In addition, it shows common architectural patterns: (1) Data Hub: Event-Bus, (2) Log Aggregation, (3) IoT, (4) Event sourcing and CQRS.
New generations of database technologies are allowing organizations to build applications never before possible, at a speed and scale that were previously unimaginable. MongoDB is the fastest growing database on the planet, and the new 3.2 release will bring the benefits of modern database architectures to an ever broader range of applications and users.
- MongoDB is well-suited for systems of engagement that have demanding real-time requirements, diverse and mixed data sets, massive concurrency, global deployment, and no downtime tolerance.
- It performs well for workloads with mixed reads, writes, and updates and scales horizontally on demand. However, it is less suited for analytical workloads, data warehousing, business intelligence, or transaction processing workloads.
- MongoDB shines for use cases involving single views of data, mobile and geospatial applications, real-time analytics, catalogs, personalization, content management, and log aggregation. It is less optimal for workloads requiring joins, full collection scans, high-latency writes, or five nines u
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Webinar: Data Streaming with Apache Kafka & MongoDB
1. #MongoDBWebinar | @mongodb
Data Streaming with
Apache Kafka &
MongoDB
Andrew Morgan –MongoDB Product
Marketing
David Tucker–Director, Partner Engineering
andAlliances atConfluent
13th September 2016
10. #MongoDBWebinar | @mongodb
What does Kafka
do?
Producers
Consumers
Kafka Connect
Kafka Connect
Topic
Your interfaces to the world
Connected to your systems in real time
11. #MongoDBWebinar | @mongodb
What is Streaming Data
Synchronous Req/Response
0 – 100s ms
Near Real Time
> 100s ms
Offline Batch
> 1 hour
KAFKA
Stream Data Platform
Search
RDBMS
Apps Monitoring
Real-time AnalyticsNoSQL Stream Processing
HADOOP
Data Lake
Impala
DWH
Hive
Spark Map-Reduce
13. #MongoDBWebinar | @mongodb
Confluent Platform: It’s Kafka ++
Feature Benefit Apache Kafka Confluent Platform
Confluent Platform
Enterprise
Apache Kafka
High throughput,low latency, high availability, secure distributedmessage
system
Kafka Connect
Advanced framework for connecting external sources/destinations into
Kafka
Java Client Provides easy integration intoJava applications
Kafka Streams
Simple library that enables streaming applicationdevelopment within the
Kafka framework
Additional Clients Supports non-Javaclients; C, C++, Python, etc.
REST Proxy
Provides universal access to Kafka from any network connected devicevia
HTTP
Schema Registry
Central registry for the format of Kafka data – guarantees all data is always
consumable
Pre-Built Connectors
HDFS, JDBC and other connectors fully Certified
and fully supported by Confluent
Confluent Control Center Includes Connector Managementand Stream Monitoring
Support
Enterprise class support to keep your Kafkaenvironment running at top
performance
Community Community 24x7x365
Free Free Subscription
14. #MongoDBWebinar | @mongodb
Common Kafka Use Cases
Data transport and integration
• Log data
• Database changes
• Sensors and device data
• Monitoring streams
• Call data records
• Stock ticker data
Real-time stream processing
• Monitoring
• Asynchronous applications
• Fraud and security
15. #MongoDBWebinar | @mongodb
People Using Kafka Today
Financial Services
Entertainment & Media
Consumer Tech
Travel & Leisure
Enterprise Tech
Telecom Retail
29. #MongoDBWebinar | @mongodb
MessageQueue
Customer Data Mgmt Mobile App IoT App Live Dashboards
Raw Data
Processed
Events
Distributed
Processing
Frameworks
Millisecond latency. Expressive querying & flexible indexing against subsets
of data. Updates-in place. In-database aggregations & transformations
Multi-minute latency with scans across TB/PB of data. No indexes. Data
stored in 128MB blocks. Write-once-read-many & append-only storage model
Sensors
User Data
Clickstreams
Logs
Churn
Analysis
Enriched
Customer
Profiles
Risk
Modeling
Predictive
Analytics
Real-Time Access
Batch Processing, Batch Views
Design Pattern: Operationalized Data Lake
Kafka
Streams
30. #MongoDBWebinar | @mongodb
MessageQueue
Customer Data Mgmt Mobile App IoT App Live Dashboards
Raw Data
Processed
Events
Millisecond latency. Expressive querying & flexible indexing against subsets
of data. Updates-in place. In-database aggregations & transformations
Multi-minute latency with scans across TB/PB of data. No indexes. Data
stored in 128MB blocks. Write-once-read-many & append-only storage model
Sensors
User Data
Clickstreams
Logs
Churn
Analysis
Enriched
Customer
Profiles
Risk
Modeling
Predictive
Analytics
Real-Time Access
Batch Processing, Batch Views
Design Pattern: Operationalized Data Lake
Configure where to
land incoming data
Distributed
Processing
Frameworks
Kafka
Streams
31. #MongoDBWebinar | @mongodb
MessageQueue
Customer Data Mgmt Mobile App IoT App Live Dashboards
Raw Data
Processed
Events
Millisecond latency. Expressive querying & flexible indexing against subsets
of data. Updates-in place. In-database aggregations & transformations
Multi-minute latency with scans across TB/PB of data. No indexes. Data
stored in 128MB blocks. Write-once-read-many & append-only storage model
Sensors
User Data
Clickstreams
Logs
Churn
Analysis
Enriched
Customer
Profiles
Risk
Modeling
Predictive
Analytics
Real-Time Access
Batch Processing, Batch Views
Design Pattern: Operationalized Data Lake
Raw data processed to
generate analytics models
Distributed
Processing
Frameworks
Kafka
Streams
32. #MongoDBWebinar | @mongodb
MessageQueue
Customer Data Mgmt Mobile App IoT App Live Dashboards
Raw Data
Processed
Events
Millisecond latency. Expressive querying & flexible indexing against subsets
of data. Updates-in place. In-database aggregations & transformations
Multi-minute latency with scans across TB/PB of data. No indexes. Data
stored in 128MB blocks. Write-once-read-many & append-only storage model
Sensors
User Data
Clickstreams
Logs
Churn
Analysis
Enriched
Customer
Profiles
Risk
Modeling
Predictive
Analytics
Real-Time Access
Batch Processing, Batch Views
Design Pattern: Operationalized Data Lake
MongoDB exposes
analytics models to
operational apps.
Handles real time
updates
Distributed
Processing
Frameworks
Kafka
Streams
33. #MongoDBWebinar | @mongodb
MessageQueue
Customer Data Mgmt Mobile App IoT App Live Dashboards
Raw Data
Processed
Events
Millisecond latency. Expressive querying & flexible indexing against subsets
of data. Updates-in place. In-database aggregations & transformations
Multi-minute latency with scans across TB/PB of data. No indexes. Data
stored in 128MB blocks. Write-once-read-many & append-only storage model
Sensors
User Data
Clickstreams
Logs
Churn
Analysis
Enriched
Customer
Profiles
Risk
Modeling
Predictive
Analytics
Real-Time Access
Batch Processing, Batch Views
Design Pattern: Operationalized Data Lake
Compute new
models against
MongoDB &
HDFS
Distributed
Processing
Frameworks
Kafka
Streams
41. #MongoDBWebinar | @mongodb
MongoDB Atlas
Database as a service for MongoDB
MongoDBAtlas is…
• Automated: The easiestway to build,launch,and scale apps on MongoDB
• Flexible: The only database as a service with all you need for modern applications
• Secured: Multiple levels ofsecurity available to give you peace of mind
• Scalable: Deliver massive scalability with zero downtime as you grow
• Highly available: Your deployments are fault-tolerantand self-healing by default
• High performance: The performance you need for your most demanding workloads
42. #MongoDBWebinar | @mongodb
MongoDB Atlas Features
• Spin up a cluster in
seconds
• Replicated & always-
on deployments
• Fully elastic: scale
out or up in a few
clicks with zero
downtime
• Automatic patches &
simplified upgrades
for the newest
MongoDB features
• Authenticated &
encrypted
• Continuous backup
with point-in-time
recovery
• Fine-grained
monitoring &
custom alerts
Safe & SecureRun for You
• On-demand pricing
model;billed by the
hour
• Multi-cloud support
(AWS available with
others coming
soon)
• Part of a suite of
products & services
designed for all
phases of your app;
migrate easily to
different
environments
(private cloud, on-
prem, etc) when
needed
No Lock-In
Database as a service for MongoDB
43. #MongoDBWebinar | @mongodb
MongoDB Enterprise Advanced
• MongoDB Ops
Manager or
MongoDB Cloud
Manager Premium
• MongoDB Compass
• MongoDB
Connector for BI
• Encrypted Storage
Engine
• LDAP / Kerberos
Integration
• DDL & DML
Auditing
• FIPS 140-2 Support
SecurityTooling
• 24 x 7 Support
• 1 hr SLA
• Emergency
Patches
• Customer Success
Program
• On-Demand
Training
Support License
• Commercial
License
44. #MongoDBWebinar | @mongodb
Resources
• Data Streaming with Apache Kafka & MongoDB
• https://www.mongodb.com/collateral/data-streaming-with-apache-
kafka-and-mongodb
• Implementing a Kafka Consumer for MongoDB
• https://www.mongodb.com/blog/post/mongodb-and-data-streaming-
implementing-a-mongodb-kafka-consumer
• Tailing the Oplog on a sharded MongoDB Cluster
• https://www.mongodb.com/blog/post/tailing-mongodb-oplog-sharded-
clusters
45. #MongoDBWebinar | @mongodb
Old Billingsgate, London
15th November
mongodb.com/europe
Use my discount code for 20% off: andrewmorgan20
46. #MongoDBWebinar | @mongodb
Document Data Model
Relational MongoDB
{ customer_id : 1,
first_name : "Mark",
last_name : "Smith",
city : "San Francisco",
phones: [
{
number : “1-212-777-1212”,
dnc : true,
type : “home”
},
number : “1-212-777-1213”,
type : “cell”
}]
}
Customer ID First Name Last Name City
0 John Doe New York
1 Mark Smith San Francisco
2 Jay Black Newark
3 Meagan White London
4 Edward Daniels Boston
Phone Number Type DNC Customer ID
1-212-555-1212 home T 0
1-212-555-1213 home T 0
1-212-555-1214 cell F 0
1-212-777-1212 home T 1
1-212-777-1213 cell (null) 1
1-212-888-1212 home F 2
47. #MongoDBWebinar | @mongodb
Document Model Benefits
{
customer_id : 1,
first_name : "Mark",
last_name : "Smith",
city : "San Francisco",
phones: [
{
number : “1-212-777-1212”,
dnc : true,
type : “home”
},
number : “1-212-777-1213”,
type : “cell”
}]
}
Agility and flexibility
Data model supports business change
Rapidly iterate to meet new requirements
Intuitive, natural data representation
Eliminates ORM layer
Developers are more productive
Reduces the need for joins, disk seeks
Programming is more simple
Performance delivered at scale
48. #MongoDBWebinar | @mongodb
Rich Functionality
MongoDB
Expressive Queries
• Find anyone with phone # “1-212…”
• Check if the person with number “555…” is on the “do not
call” list
Geospatial
• Find the best offer for the customer at geo coordinates of 42nd
St. and 6th
Ave
Text Search • Find all tweets that mention the firm within the last 2 days
Aggregation • Count and sort number of customers by city
Native Binary
JSON support
• Add an additional phone number to Mark Smith’s without
rewriting the document
• Select just the mobile phone number in the list
• Sort on the modified date
{ customer_id : 1,
first_name : "Mark",
last_name : "Smith",
city : "San Francisco",
phones: [ {
number : “1-212-777-1212”,
dnc : true,
type : “home”
},
{
number : “1-212-777-1213”,
type : “cell”
}]
}
Left outer join
($lookup)
• Query for all San Francisco residences,lookup their
transactions, and sum the amount by person