Streaming Data in the Cloud with Confluent and MongoDB Atlas | Robert Walters, MongoDB

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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.

Tecnologia

Streaming Data with
Confluent Cloud and
MongoDB Atlas
Robert Walters | MongoDB

Presenter
Robert Walters
Product Manager
Connectors and Things
MongoDB
https://www.linkedin.com/in/robwaltersprofile/

Agenda
MongoDB Atlas
MongoDB in the Confluent Cloud
Demo
Confluent Cloud
Connectors
MongoDB
Atlas
AWS: us-east
Virginia
MongoDB
Atlas
AWS: eu-west
Ireland

MongoDB Adoption Continues to Grow
DB-Engines Rankings
Fastest Growing Database
over the past decade
Worldwide Activations
Most Wanted Database: 4 Years Straight
2020 Stack Overflow Developer Survey
155,000,000+
MongoDB Downloads
1,500,000+
Online Education Course
Registrations
1,750,000+
MongoDB Atlas Clusters
1,000+
Technology and Services
Partners
24,800+
Customers Across All
Industries
155M+

Self-hosted MongoDB Turn-key modern database
You install, patch,
maintain, scale, etc..
MongoDB as a service plus
a whole lot more…
MongoDB & MongoDB Atlas

At the core is the database for modern applications
● Transactional guarantees at a
global scale
● Intuitive and flexible data model
● Unique data distribution
capabilities
● MongoDB Query Language (MQL)
is built for nearly any workload
Distributed
Intuitive & Flexible Data Model
Transactional

We fully manage it for you in the cloud
● Fully managed database lifecycle with
MongoDB Atlas.
● Multi-cloud, available in ~ 80 regions
across AWS, GCP, Azure
● Sophisticated security controls and
next-gen end user privacy
● Autopilot features such as auto-scale,
performance advisor, and more
● Built-in data access, movement,
manipulation services for rapid
application development

Interactive data visualization for MongoDB data
● MongoDB Charts is the fastest
and easiest way to create
visualizations of MongoDB data
● Share, embed, and collaborate
on live data
● Support for the richness of
document model, including
nested and hierarchical data

Integrated full-text search capabilities
● Atlas Search allows you to
implement full-text search on
top of your data in cloud with no
need to replicate your data
elsewhere and no additional
systems to learn or manage
● Atlas search queries use the
MongoDB Query Language

Tier, query and analyze your data using MQL
● Auto-archive aged data into Atlas Data
Lake
● Blend, query, and analyze the
structured / unstructured data in your
cloud object storage using the
MongoDB Query Language
● Support for federated queries means
you can submit a single query and
analyze operational data in MongoDB
Atlas alongside your data in S3

Streaming data
Confluent Cloud <-> MongoDB Atlas
Confluent Cloud
Connectors
MongoDB
Atlas Source
"Quantity" : { $gte : 5 }
MongoDB
Atlas Sink
MongoDB Atlas
AWS: us-east
Virginia
MongoDB Atlas
AWS: eu-west
Ireland
Python data generator application

Not ready for “- as a service” ?
https://www.confluent.io/resources/confluent-platform-reference-architecture-mongodb/

Thank you
Rob Walters | MongoDB
robert.walters@mongodb.com
https://www.linkedin.com/in/robwaltersprofile/
https://developer.mongodb.com/community/forums/c/connectors-integrations

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"Stream processing systems traditionally gave their users the choice between at least once processing and at most once processing: accepting duplicate data or missing data. But ideally we would provide exactly-once processing, where every event in the input data is represented exactly once in the output. Kafka provides a transaction API that enables exactly-once when using Kafka as your source and sink. But this API has turned out to not be well suited for use by high level streaming systems, requiring various work arounds to still provide transactional processing. In this talk, I’ll cover how the transaction API works, and how systems like Arroyo and Flink have used it to build exactly-once support, and how improvements to the transactional API will enable better end-to-end support for consistent stream processing."

Exactly-once Stream Processing with Arroyo and Kafka

HostedbyConfluent

"In this talk, we will explore the exciting world of IoT and computer vision by presenting a unique project: Fish Plays Pokemon. Using an ESP Eye camera connected to an ESP32 and other IoT devices, to monitor fish's movements in an aquarium. This project showcases the power of IoT and computer vision, demonstrating how even a fish can play a popular video game. We will discuss the challenges we faced during development, including real-time processing, IoT device integration, and Kafka message consumption. By the end of the talk, attendees will have a better understanding of how to combine IoT, computer vision, and the usage of a serverless cloud to create innovative projects. They will also learn how to integrate IoT devices with Kafka to simulate keyboard behavior, opening up endless possibilities for real-time interactions between the physical and digital worlds."

Fish Plays Pokemon | Kafka Summit London

HostedbyConfluent

Tiered Storage 101 | Kafla Summit London

HostedbyConfluent

"Real-time 24/7 monitoring and verification of massive data is challenging – even more so for the world’s second largest manufacturer of memory chips and semiconductors. Tolerance levels are incredibly small, any small defect needs to be identified and dealt with immediately. The goal of semiconductor manufacturing is to improve yield and minimize unnecessary work. However, even with real-time data collection, the data was not easy to manipulate by users and it took many days to enable stream processing requests – limiting its usefulness and value to the business. You’ll hear why SK hynix switched to Confluent and how we developed a self-service stream process portal on top of it. Now users have an easy-to-use service to manipulate the data they want. Results have been impressive, stream processing requests are available the same day – previously taking 5 days! We were also able to drive down costs by 10% as stream processing requests no longer require additional hardware. What you’ll take away from our talk: - What were the pain points in the previous environment - How we transitioned to Confluent without service downtime - Creating a self-service stream processing portal built on top of Connect and ksqlDB - Use case of stream process portal"

Building a Self-Service Stream Processing Portal: How And Why

HostedbyConfluent

"Discover how default configurations might impact ingestion times, especially when dealing with large files. We'll explore a real-world scenario with a 20,000,000+ line file, assessing metrics and exploring the bottleneck in the default setup. Understand the intricacies of batch size calculations and how to optimize them based on your unique data characteristics. Walk away with actionable insights as we showcase a practical example, turning a 7-hour ingestion process into a mere 30 minutes for over 30,000,000 records in a Kafka topic. Uncover metrics, configurations, and best practices to elevate the performance of your Kafka Connect CSV source connectors. Don't miss this opportunity to optimize your data pipeline and ensure smooth, efficient data flow."

From the Trenches: Improving Kafka Connect Source Connector Ingestion from 7 ...

HostedbyConfluent

"In order to meet the current and ever-increasing demand for near-zero RPO/RTO systems, a focus on resiliency is critical. While Kafka offers built-in resiliency features, a perfect blend of client and cluster resiliency is necessary in order to achieve a highly resilient Kafka client application. At Fidelity Investments, Kafka is used for a variety of event streaming needs such as core brokerage trading platforms, log aggregation, communication platforms, and data migrations. In this lightening talk, we will discuss the governance framework that has enabled producers and consumers to achieve their SLAs during unprecedented failure scenarios. We will highlight how we automated resiliency tests through chaos engineering and tightly integrated observability dashboards for Kafka clients to analyze and optimize client configurations. And finally, we will summarize the chaos test suite and the ""test, test and test"" mantra that are helping Fidelity Investments reach its goal of a future with zero down-time."

Future with Zero Down-Time: End-to-end Resiliency with Chaos Engineering and ...

HostedbyConfluent

"There are various strategies for securely connecting to Kafka clusters between different networks or over the public internet. Many cloud providers even offer endpoints that privately route traffic between networks and are not exposed to the internet. But, depending on your network setup and how you are running Kafka, these options ... might not be an option! In this session, we’ll discuss how you can use SSH bastions or a self managed PrivateLink endpoint to establish connectivity to your Kafka clusters without exposing brokers directly to the internet. We explain the required network configuration, and show how we at Materialize have contributed to librdkafka to simplify these scenarios and avoid fragile workarounds."

Navigating Private Network Connectivity Options for Kafka Clusters

HostedbyConfluent

"In my talk, we will examine all the stages of building our self-service Streaming Data Platform based on Apache Flink and Kafka Connect, from the selection of a solution for stateful streaming data processing, right up to the successful design of a robust self-service platform, covering the challenges that we’ve met. I will share our experience in providing non-Java developers with a company-wide self-service solution, which allows them to quickly and easily develop their streaming data pipelines. Additionally, I will highlight specific business use cases that would not have been implemented without our platform.0 characters0 characters"

Apache Flink: Building a Company-wide Self-service Streaming Data Platform

HostedbyConfluent

"Almost everyone has heard about large language models, and tens of millions of people have tried out OpenAI ChatGPT and Google Bard. However, the intricate architecture and underlying mathematics driving these remarkable systems remain elusive to many. LLM's are fascinating - so let's grab a drink and find out how these systems are built and dive deep into their inner workings. In the length of time it to enjoy a round of drinks, you'll understand the inner workings of these models. We'll take our first sip of word vectors, enjoy the refreshing taste of the transformer, and drain a glass understanding how these models are trained on phenomenally large quantities of data. Large language models for your streaming application - explained with a little maths and a lot of pub stories"

Explaining How Real-Time GenAI Works in a Noisy Pub

HostedbyConfluent

"Monitoring is a fundamental operation when running Kafka and Kafka applications in production. There are numerous metrics available when using Kafka, however the sheer number is overwhelming, making it challenging to know where to start and how to properly utilise them. This session will introduce you to some of the key metrics that should be monitored and best practices in fine tuning your monitoring. We will delve into which metrics are the indicators for cluster’s availability and performance and are the most helpful when debugging client applications."

TL;DR Kafka Metrics | Kafka Summit London

HostedbyConfluent

Kafka Streams relies on state restoration for maintaining standby tasks as failure recovery mechanism as well as for restoring the state after rebalance scenarios. When you are scaling up or down your application instances, it is necessary to know the current state of the restoration process for each active and standby task in order to prevent a long restoration process as much as possible. During this presentation, you will get an understanding of how KIP-869 provides valuable information about the current active task restoration after a rebalance and KIP-988 opens a window to the continuous process of standby restoration. When you encounter a situation in which you need to choose whether or not to scale up or down your application instances, both KIPs will be an invaluable ally for you.

A Window Into Your Kafka Streams Tasks | KSL

HostedbyConfluent

"In this talk, we will dive into the world of Kafka producer configs and explore how to understand and optimize them for better performance. We will cover the different types of configs, their impact on performance, and how to tune them to achieve the best results. Whether you're new to Kafka or a seasoned pro, this session will provide valuable insights and practical tips for improving your Kafka producer performance. - Introduction to Kafka producer internal and workflow - Understanding the producer configs like linger.ms, batch.size, buffer.memory and their impact on performance - Learning about producer configs like max.block.ms, delivery.timeout.ms, request.timeout.ms and retries to make producer more resilient. - Discuss configs like enable.idempotence, max.in.flight.requests.per.connection and transaction related configs to achieve delivery guarantees. - Q&A session with attendees to address specific questions and concerns."

Mastering Kafka Producer Configs: A Guide to Optimizing Performance

HostedbyConfluent

"Data contracts are one of the hottest topics in the data management community. A data contract is a formal agreement between a data producer and its consumers, aimed at reducing data downtime and improving data quality. Schemas are an important part of data contracts, but they are not the only relevant element. In this talk, we’ll: 1. see why data contracts are so important but also difficult to implement; 2. identify the characteristics of a well-designed data contract: discuss the anatomy of a data contract, its main elements and, how to formally describe them; 3. show how to manage the lifecycle of a data contract leveraging Confluent Platform's services."

Data Contracts Management: Schema Registry and Beyond

HostedbyConfluent

"In the realm of stateful stream processing, Apache Flink has emerged as a powerful and versatile platform. However, the conventional SQL-based approach often limits the full potential of Flink applications. We will delve into the benefits of adopting a code-first approach, which provides developers with greater control over application logic, facilitates complex transformations, and enables more efficient handling of state and time. We will also discuss how the code-first approach can lead to more maintainable and testable code, ultimately improving the overall quality of your Flink applications. Whether you're a seasoned Flink developer or just starting your journey, this talk will provide valuable insights into how a code-first approach can revolutionize your stream processing applications."

Code-First Approach: Crafting Efficient Flink Apps

HostedbyConfluent

"Change Data Capture (CDC) has become a commodity in data engineering, much in part due to the ever-rising success of Debezium [1]. But is that all there is? In this lightning talk, we’ll outline the current state of the CDC ecosystem, and understand why adopting a Debezium alternative is still a hard sell. If you’ve ever wondered what else is out there, but can’t keep up with the sprawling of new tools in the ecosystem; we’ll wrap it up for you! [1] https://debezium.io/"

Debezium vs. the World: An Overview of the CDC Ecosystem

HostedbyConfluent

"Separation of compute and storage has become the de-facto standard in the data industry for batch processing. The addition of tiered storage to open source Apache Kafka is the first step in bringing true separation of compute and storage to the streaming world. In this talk, we'll discuss in technical detail how to take the concept of tiered storage to its logical extreme by building an Apache Kafka protocol compatible system that has zero local disks. Eliminating all local disks in the system requires not only separating storage from compute, but also separating data from metadata. This is a monumental task that requires reimagining Kafka's architecture from the ground up, but the benefits are worth it. This approach enables a stateless, elastic, and serverless deployment model that minimizes operational overhead and also drives inter-zone networking costs to almost zero."

Beyond Tiered Storage: Serverless Kafka with No Local Disks

HostedbyConfluent

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Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.

Understanding Discord NSFW Servers A Guide for Responsible Users.pdf

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Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?

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The value of a flexible API Management solution for Open Banking Steve Melan, Manager for IT Innovation and Architecture - State's and Saving's Bank of Luxembourg Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

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The Digital Insurer

Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

sammart93

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With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.

Boost PC performance: How more available memory can improve productivity

Principled Technologies

The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.

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Streaming Data in the Cloud with Confluent and MongoDB Atlas | Robert Walters, MongoDB

1. Streaming Data with Confluent Cloud and MongoDB Atlas Robert Walters | MongoDB

2. Presenter Robert Walters Product Manager Connectors and Things MongoDB https://www.linkedin.com/in/robwaltersprofile/

3. Agenda MongoDB Atlas MongoDB in the Confluent Cloud Demo Confluent Cloud Connectors MongoDB Atlas AWS: us-east Virginia MongoDB Atlas AWS: eu-west Ireland

4. MongoDB Adoption Continues to Grow DB-Engines Rankings Fastest Growing Database over the past decade Worldwide Activations Most Wanted Database: 4 Years Straight 2020 Stack Overflow Developer Survey 155,000,000+ MongoDB Downloads 1,500,000+ Online Education Course Registrations 1,750,000+ MongoDB Atlas Clusters 1,000+ Technology and Services Partners 24,800+ Customers Across All Industries 155M+

5. Self-hosted MongoDB Turn-key modern database You install, patch, maintain, scale, etc.. MongoDB as a service plus a whole lot more… MongoDB & MongoDB Atlas

6. At the core is the database for modern applications ● Transactional guarantees at a global scale ● Intuitive and flexible data model ● Unique data distribution capabilities ● MongoDB Query Language (MQL) is built for nearly any workload Distributed Intuitive & Flexible Data Model Transactional

7. We fully manage it for you in the cloud ● Fully managed database lifecycle with MongoDB Atlas. ● Multi-cloud, available in ~ 80 regions across AWS, GCP, Azure ● Sophisticated security controls and next-gen end user privacy ● Autopilot features such as auto-scale, performance advisor, and more ● Built-in data access, movement, manipulation services for rapid application development

8. Interactive data visualization for MongoDB data ● MongoDB Charts is the fastest and easiest way to create visualizations of MongoDB data ● Share, embed, and collaborate on live data ● Support for the richness of document model, including nested and hierarchical data

9. Integrated full-text search capabilities ● Atlas Search allows you to implement full-text search on top of your data in cloud with no need to replicate your data elsewhere and no additional systems to learn or manage ● Atlas search queries use the MongoDB Query Language

10. Tier, query and analyze your data using MQL ● Auto-archive aged data into Atlas Data Lake ● Blend, query, and analyze the structured / unstructured data in your cloud object storage using the MongoDB Query Language ● Support for federated queries means you can submit a single query and analyze operational data in MongoDB Atlas alongside your data in S3

11. Tour of MongoDB Atlas

12. Streaming data Confluent Cloud <-> MongoDB Atlas Confluent Cloud Connectors MongoDB Atlas Source "Quantity" : { $gte : 5 } MongoDB Atlas Sink MongoDB Atlas AWS: us-east Virginia MongoDB Atlas AWS: eu-west Ireland Python data generator application

13. Not ready for “- as a service” ? https://www.confluent.io/resources/confluent-platform-reference-architecture-mongodb/

14. Thank you Rob Walters | MongoDB robert.walters@mongodb.com https://www.linkedin.com/in/robwaltersprofile/ https://developer.mongodb.com/community/forums/c/connectors-integrations

Streaming Data in the Cloud with Confluent and MongoDB Atlas | Robert Walters, MongoDB

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