This document discusses building real-time analytics pipelines using Confluent and AWS. It describes how Confluent makes real-time data streams a top priority through features like Apache Kafka. It also explains how to build a typical real-time data pipeline using an event streaming platform to capture, process, and analyze streaming data in real-time. Additionally, it covers how Confluent and AWS can be used together to unlock various use cases across industries through deep integrations and services. Finally, it provides an overview of different stream processing options on AWS including ksqlDB, Kafka Streams, Kinesis Data Analytics, and AWS Lambda.
Apache Kafka as Event Streaming Platform for Microservice ArchitecturesKai Wähner
This session introduces Apache Kafka, an event-driven open source streaming platform. Apache Kafka goes far beyond scalable, high volume messaging. In addition, you can leverage Kafka Connect for integration and the Kafka Streams API for building lightweight stream processing microservices in autonomous teams. The Confluent Platform adds further components such as a Schema Registry, REST Proxy, KSQL, Clients for different programming languages and Connectors for different technologies.
The session discusses how tech giants like LinkedIn, Ebay or Airbnb leverage Apache Kafka as event streaming platform to solve various different business problems and how to create a scalable, flexible microservice architecture. A live demo shows how you can easily process and analyze streams of events using Apache Kafka and KSQL.
Hybrid Kafka, Taking Real-time Analytics to the Business (Cody Irwin, Google ...HostedbyConfluent
Apache Kafka users who want to leverage Google Cloud Platform's (GCPs) data analytics platform and open source hosting capabilities can bridge their existing Kafka infrastructure on-premise or in other clouds to GCP using Confluent's replicator tool and managed Kafka service on GCP. Using actual customer examples and a reference architecture, we'll showcase how existing Kafka users can stream data to GCP and use it in popular tools like Apache Beam on Dataflow, BigQuery, Google Cloud Storage (GCS), Spark on Dataproc, and Tensorflow for data warehousing, data processing, data storage, and advanced analytics using AI and ML.
Vous apprendrez également à :
• Créer plus rapidement des produits et fonctionnalités à l’aide d’une suite complète de connecteurs et d’outils de gestion des flux, et à connecter vos environnements à des pipelines de données
• Protéger vos données et charges de travail les plus critiques grâce à des garanties intégrées en matière de sécurité, de gouvernance et de résilience
• Déployer Kafka à grande échelle en quelques minutes tout en réduisant les coûts et la charge opérationnelle associés
Apache Kafka as Event Streaming Platform for Microservice ArchitecturesKai Wähner
This session introduces Apache Kafka, an event-driven open source streaming platform. Apache Kafka goes far beyond scalable, high volume messaging. In addition, you can leverage Kafka Connect for integration and the Kafka Streams API for building lightweight stream processing microservices in autonomous teams. The Confluent Platform adds further components such as a Schema Registry, REST Proxy, KSQL, Clients for different programming languages and Connectors for different technologies.
The session discusses how tech giants like LinkedIn, Ebay or Airbnb leverage Apache Kafka as event streaming platform to solve various different business problems and how to create a scalable, flexible microservice architecture. A live demo shows how you can easily process and analyze streams of events using Apache Kafka and KSQL.
Hybrid Kafka, Taking Real-time Analytics to the Business (Cody Irwin, Google ...HostedbyConfluent
Apache Kafka users who want to leverage Google Cloud Platform's (GCPs) data analytics platform and open source hosting capabilities can bridge their existing Kafka infrastructure on-premise or in other clouds to GCP using Confluent's replicator tool and managed Kafka service on GCP. Using actual customer examples and a reference architecture, we'll showcase how existing Kafka users can stream data to GCP and use it in popular tools like Apache Beam on Dataflow, BigQuery, Google Cloud Storage (GCS), Spark on Dataproc, and Tensorflow for data warehousing, data processing, data storage, and advanced analytics using AI and ML.
Vous apprendrez également à :
• Créer plus rapidement des produits et fonctionnalités à l’aide d’une suite complète de connecteurs et d’outils de gestion des flux, et à connecter vos environnements à des pipelines de données
• Protéger vos données et charges de travail les plus critiques grâce à des garanties intégrées en matière de sécurité, de gouvernance et de résilience
• Déployer Kafka à grande échelle en quelques minutes tout en réduisant les coûts et la charge opérationnelle associés
Resilient Real-time Data Streaming across the Edge and Hybrid Cloud with Apac...Kai Wähner
Hybrid cloud architectures are the new black for most companies. A cloud-first strategy is evident for many new enterprise architectures, but some use cases require resiliency across edge sites and multiple cloud regions. Data streaming with the Apache Kafka ecosystem is a perfect technology for building resilient and hybrid real-time applications at any scale. This talk explores different architectures and their trade-offs for transactional and analytical workloads. Real-world examples include financial services, retail, and the automotive industry.
Video recording:
https://qconlondon.com/london2022/presentation/resilient-real-time-data-streaming-across-the-edge-and-hybrid-cloud
Keine Angst vorm Dinosaurier: Mainframe-Integration und -Offloading mit Confl...Precisely
Mainframes sind immer noch weit verbreitet im Einsatz und verarbeiten täglich über 70 Prozent der wichtigsten Rechentransaktionen der Welt. Sehr hohe Kosten, monolithische Architekturen und fehlende Experten sind die größten Herausforderungen für Mainframe-Anwendungen. Es ist an der Zeit, innovativer zu werden, auch mit dem Mainframe! Stellen wir uns gemeinsam dem Dinosaurier!
Mainframe Offloading mit Confluent, Apache Kafka und dem zugehörigen Ökosystem kann genutzt werden, um moderne Dateninfrastrukturen in Echtzeit mit dem Mainframe synchron zu halten. Dabei ermöglich Kafka sowohl die Datenverarbeitung als auch die Integration mit Systemen wie Data Warehouses und Analytics-Plattformen. Dabei können via Change Data Capture (CDC) permanent Mainframe-Änderungen im hochvoluminösen Bereich nach Kafka gepusht werden.
In dieser on-demand-präsentation zeigen Confluent und Precisely, wie Unternehmen diesen Schritt zur Legacy-Migration machen, Kosten sparen, eine skalierbare und offene Architektur schaffen und so neue Dienste und Anwendungen ermöglichen.
2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipel...Timothy Spann
2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipelines
https://www.meetup.com/futureofdata-newyork/events/298660453/
Unlocking Financial Data with Real-Time Pipelines
(Flink Analytics on Stocks with SQL )
By Timothy Spann
Financial institutions thrive on accurate and timely data to drive critical decision-making processes, risk assessments, and regulatory compliance. However, managing and processing vast amounts of financial data in real-time can be a daunting task. To overcome this challenge, modern data engineering solutions have emerged, combining powerful technologies like Apache Flink, Apache NiFi, Apache Kafka, and Iceberg to create efficient and reliable real-time data pipelines. In this talk, we will explore how this technology stack can unlock the full potential of financial data, enabling organizations to make data-driven decisions swiftly and with confidence.
Introduction: Financial institutions operate in a fast-paced environment where real-time access to accurate and reliable data is crucial. Traditional batch processing falls short when it comes to handling rapidly changing financial markets and responding to customer demands promptly. In this talk, we will delve into the power of real-time data pipelines, utilizing the strengths of Apache Flink, Apache NiFi, Apache Kafka, and Iceberg, to unlock the potential of financial data. I will be utilizing NiFi 2.0 with Python and Vector Databases.
Timothy Spann
Principal Developer Advocate, Cloudera
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Kafka, Apache Pulsar, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.
https://twitter.com/PaaSDev
https://www.linkedin.com/in/timothyspann/
https://medium.com/@tspann
https://github.com/tspannhw/FLiPStackWeekly/
Serverless Kafka on AWS as Part of a Cloud-native Data Lake ArchitectureKai Wähner
AWS Data Lake / Lake House + Confluent Cloud for Serverless Apache Kafka. Learn about use cases, architectures, and features.
Data must be continuously collected, processed, and reactively used in applications across the entire enterprise - some in real time, some in batch mode. In other words: As an enterprise becomes increasingly software-defined, it needs a data platform designed primarily for "data in motion" rather than "data at rest."
Apache Kafka is now mainstream when it comes to data in motion! The Kafka API has become the de facto standard for event-driven architectures and event streaming. Unfortunately, the cost of running it yourself is very often too expensive when you add factors like scaling, administration, support, security, creating connectors...and everything else that goes with it. Resources in enterprises are scarce: this applies to both the best team members and the budget.
The cloud - as we all know - offers the perfect solution to such challenges.
Most likely, fully-managed cloud services such as AWS S3, DynamoDB or Redshift are already in use. Now it is time to implement "fully-managed" for Kafka as well - with Confluent Cloud on AWS.
Building a central integration layer that doesn't care where or how much data is coming from.
Implementing scalable data stream processing to gain real-time insights
Leveraging fully managed connectors (like S3, Redshift, Kinesis, MongoDB Atlas & more) to quickly access data
Confluent Cloud in action? Let's show how ao.com made it happen!
Translated with www.DeepL.com/Translator (free version)
Navigating Your Data Landscape With Siddharth Desai and Elena Cuevas | Curren...HostedbyConfluent
Navigating Your Data Landscape With Siddharth Desai and Elena Cuevas | Current 2022
We see data and the way organizations using it to create unique customer experiences is being completely reimagined. Every time a customer clicks, types or swipes data is in constant motion spanning systems, environments and applications. This in turn requires business’ manage complex integrations, synchronizations and processing of data spread across cloud and on-prem environments.
To accelerate time to value, data needs to be seamlessly ingested, integrated and/or replicated to a cloud environment to take advantage of its analytical, BI and AI use cases. Google Cloud delivers a simplified approach for all your Data Movement needs through a highly differentiated product portfolio.
In this session, learn how organizations can unlock data value using best-in-class, cloud native products on Google Cloud and its partners such as Confluent.
Streaming Time Series Data With Kenny Gorman and Elena Cuevas | Current 2022HostedbyConfluent
Streaming Time Series Data With Kenny Gorman and Elena Cuevas | Current 2022
Modern streaming use cases are generating massive amounts of data - much of it needs to be organized and queried over time. The sheer amount and complexity of this problem presents new challenges for data engineers and developers alike.
To solve this problem Apache Kafka and MongoDB Time Series collections are a powerful combination. In this talk, Kenny Gorman and Elena Cuevas will present how Apache Kafka on Confluent Cloud can stream massive amounts of data to Time Series Collections via the MongoDB Connector for Apache Kafka. Elena and Kenny will discuss the required configuration details and critical components of Confluent Cloud and MongoDB Atlas as well as some tips, tricks and best practices.
You will leave armed with the knowledge of how Confluent Cloud, Apache Kafka, MongoDB Atlas, and Time Series collections fit into your event-driven architecture.
Streaming Data Ingest and Processing with Apache KafkaAttunity
Apache™ Kafka is a fast, scalable, durable, and fault-tolerant
publish-subscribe messaging system. It offers higher throughput, reliability and replication. To manage growing data volumes, many companies are leveraging Kafka for streaming data ingest and processing.
Join experts from Confluent, the creators of Apache™ Kafka, and the experts at Attunity, a leader in data integration software, for a live webinar where you will learn how to:
-Realize the value of streaming data ingest with Kafka
-Turn databases into live feeds for streaming ingest and processing
-Accelerate data delivery to enable real-time analytics
-Reduce skill and training requirements for data ingest
The recorded webinar on slide 32 includes a demo using automation software (Attunity Replicate) to stream live changes from a database into Kafka and also includes a Q&A with our experts.
For more information, please go to www.attunity.com/kafka.
App modernization on AWS with Apache Kafka and Confluent CloudKai Wähner
Presentation from AWS ReInvent 2020.
Learn how you can accelerate application modernization and benefit from the open-source Apache Kafka ecosystem by connecting your legacy, on-premises systems to the cloud. In this session, hear real customer stories about timely insights gained from event-driven applications built on an event streaming platform from Confluent Cloud running on AWS, which stores and processes historical data and real-time data streams. Confluent makes Apache Kafka enterprise-ready using infinite Kafka storage with Amazon S3 and multiple private networking options including AWS PrivateLink, along with self-managed encryption keys for storage volume encryption with AWS Key Management Service (AWS KMS).
The Road Most Traveled: A Kafka Story | Heikki Nousiainen, AivenHostedbyConfluent
When moving to a cloud native architecture Moogsoft knew they needed more scale than Rabbit could provide. Moogsoft moved into Kafka which is known for quick writing and driving heavy event driven workloads on top of niceties such as replayability. Choosing the tool was easy, finding a vendor that ticked all their boxes was not. They needed to ensure scalability, upgradability, builds via existing IAC pipelines, and observability via existing tools. When Moogsoft found Aiven, they were impressed with their offering and ability to scale on demand. During this presentation we will explore how Moogsoft used Aiven for Kafka to manage and scale their data in the cloud.
Confluent Partner Tech Talk with Synthesisconfluent
A discussion on the arduous planning process, and deep dive into the design/architectural decisions.
Learn more about the networking, RBAC strategies, the automation, and the deployment plan.
Build real-time streaming data pipelines to AWS with Confluentconfluent
Traditional data pipelines often face scalability issues and challenges related to cost, their monolithic design, and reliance on batch data processing. They also typically operate under the premise that all data needs to be stored in a single centralized data source before it's put to practical use. Confluent Cloud on Amazon Web Services (AWS) provides a fully managed cloud-native platform that helps you simplify the way you build real-time data flows using streaming data pipelines and Apache Kafka.
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.
Why Cloud-Native Kafka Matters: 4 Reasons to Stop Managing it YourselfDATAVERSITY
With your most talented teams bogged down managing a massive Kafka deployment, it can be challenging to move the dial on projects that drive real value for your business. For example, launching your next major feature, fueling more best-in-breed services like AI/ML on your cloud provider platform, or developing your first use cases for real-time data movement across clouds. By shifting to a fully managed, cloud-native service for Kafka you can unlock your teams to work on the projects that make the best use of your data in motion.
In this webinar you will learn about:
• The increasing value of data in motion to your business
• Challenges and costs of self-managing a large-scale Kafka deployment
• Benefits of managed cloud services for non-core activities like data storage, data warehousing, and messaging
• Optimizing time usage for value-generating activity like new product launches
• Potential cost savings for your business with a cloud-native service for Kafka
Technical Deep Dive: Using Apache Kafka to Optimize Real-Time Analytics in Fi...confluent
Watch this talk here: https://www.confluent.io/online-talks/using-apache-kafka-to-optimize-real-time-analytics-financial-services-iot-applications
When it comes to the fast-paced nature of capital markets and IoT, the ability to analyze data in real time is critical to gaining an edge. It’s not just about the quantity of data you can analyze at once, it’s about the speed, scale, and quality of the data you have at your fingertips.
Modern streaming data technologies like Apache Kafka and the broader Confluent platform can help detect opportunities and threats in real time. They can improve profitability, yield, and performance. Combining Kafka with Panopticon visual analytics provides a powerful foundation for optimizing your operations.
Use cases in capital markets include transaction cost analysis (TCA), risk monitoring, surveillance of trading and trader activity, compliance, and optimizing profitability of electronic trading operations. Use cases in IoT include monitoring manufacturing processes, logistics, and connected vehicle telemetry and geospatial data.
This online talk will include in depth practical demonstrations of how Confluent and Panopticon together support several key applications. You will learn:
-Why Apache Kafka is widely used to improve performance of complex operational systems
-How Confluent and Panopticon open new opportunities to analyze operational data in real time
-How to quickly identify and react immediately to fast-emerging trends, clusters, and anomalies
-How to scale data ingestion and data processing
-Build new analytics dashboards in minutes
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Resilient Real-time Data Streaming across the Edge and Hybrid Cloud with Apac...Kai Wähner
Hybrid cloud architectures are the new black for most companies. A cloud-first strategy is evident for many new enterprise architectures, but some use cases require resiliency across edge sites and multiple cloud regions. Data streaming with the Apache Kafka ecosystem is a perfect technology for building resilient and hybrid real-time applications at any scale. This talk explores different architectures and their trade-offs for transactional and analytical workloads. Real-world examples include financial services, retail, and the automotive industry.
Video recording:
https://qconlondon.com/london2022/presentation/resilient-real-time-data-streaming-across-the-edge-and-hybrid-cloud
Keine Angst vorm Dinosaurier: Mainframe-Integration und -Offloading mit Confl...Precisely
Mainframes sind immer noch weit verbreitet im Einsatz und verarbeiten täglich über 70 Prozent der wichtigsten Rechentransaktionen der Welt. Sehr hohe Kosten, monolithische Architekturen und fehlende Experten sind die größten Herausforderungen für Mainframe-Anwendungen. Es ist an der Zeit, innovativer zu werden, auch mit dem Mainframe! Stellen wir uns gemeinsam dem Dinosaurier!
Mainframe Offloading mit Confluent, Apache Kafka und dem zugehörigen Ökosystem kann genutzt werden, um moderne Dateninfrastrukturen in Echtzeit mit dem Mainframe synchron zu halten. Dabei ermöglich Kafka sowohl die Datenverarbeitung als auch die Integration mit Systemen wie Data Warehouses und Analytics-Plattformen. Dabei können via Change Data Capture (CDC) permanent Mainframe-Änderungen im hochvoluminösen Bereich nach Kafka gepusht werden.
In dieser on-demand-präsentation zeigen Confluent und Precisely, wie Unternehmen diesen Schritt zur Legacy-Migration machen, Kosten sparen, eine skalierbare und offene Architektur schaffen und so neue Dienste und Anwendungen ermöglichen.
2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipel...Timothy Spann
2024 February 28 - NYC - Meetup Unlocking Financial Data with Real-Time Pipelines
https://www.meetup.com/futureofdata-newyork/events/298660453/
Unlocking Financial Data with Real-Time Pipelines
(Flink Analytics on Stocks with SQL )
By Timothy Spann
Financial institutions thrive on accurate and timely data to drive critical decision-making processes, risk assessments, and regulatory compliance. However, managing and processing vast amounts of financial data in real-time can be a daunting task. To overcome this challenge, modern data engineering solutions have emerged, combining powerful technologies like Apache Flink, Apache NiFi, Apache Kafka, and Iceberg to create efficient and reliable real-time data pipelines. In this talk, we will explore how this technology stack can unlock the full potential of financial data, enabling organizations to make data-driven decisions swiftly and with confidence.
Introduction: Financial institutions operate in a fast-paced environment where real-time access to accurate and reliable data is crucial. Traditional batch processing falls short when it comes to handling rapidly changing financial markets and responding to customer demands promptly. In this talk, we will delve into the power of real-time data pipelines, utilizing the strengths of Apache Flink, Apache NiFi, Apache Kafka, and Iceberg, to unlock the potential of financial data. I will be utilizing NiFi 2.0 with Python and Vector Databases.
Timothy Spann
Principal Developer Advocate, Cloudera
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Kafka, Apache Pulsar, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.
https://twitter.com/PaaSDev
https://www.linkedin.com/in/timothyspann/
https://medium.com/@tspann
https://github.com/tspannhw/FLiPStackWeekly/
Serverless Kafka on AWS as Part of a Cloud-native Data Lake ArchitectureKai Wähner
AWS Data Lake / Lake House + Confluent Cloud for Serverless Apache Kafka. Learn about use cases, architectures, and features.
Data must be continuously collected, processed, and reactively used in applications across the entire enterprise - some in real time, some in batch mode. In other words: As an enterprise becomes increasingly software-defined, it needs a data platform designed primarily for "data in motion" rather than "data at rest."
Apache Kafka is now mainstream when it comes to data in motion! The Kafka API has become the de facto standard for event-driven architectures and event streaming. Unfortunately, the cost of running it yourself is very often too expensive when you add factors like scaling, administration, support, security, creating connectors...and everything else that goes with it. Resources in enterprises are scarce: this applies to both the best team members and the budget.
The cloud - as we all know - offers the perfect solution to such challenges.
Most likely, fully-managed cloud services such as AWS S3, DynamoDB or Redshift are already in use. Now it is time to implement "fully-managed" for Kafka as well - with Confluent Cloud on AWS.
Building a central integration layer that doesn't care where or how much data is coming from.
Implementing scalable data stream processing to gain real-time insights
Leveraging fully managed connectors (like S3, Redshift, Kinesis, MongoDB Atlas & more) to quickly access data
Confluent Cloud in action? Let's show how ao.com made it happen!
Translated with www.DeepL.com/Translator (free version)
Navigating Your Data Landscape With Siddharth Desai and Elena Cuevas | Curren...HostedbyConfluent
Navigating Your Data Landscape With Siddharth Desai and Elena Cuevas | Current 2022
We see data and the way organizations using it to create unique customer experiences is being completely reimagined. Every time a customer clicks, types or swipes data is in constant motion spanning systems, environments and applications. This in turn requires business’ manage complex integrations, synchronizations and processing of data spread across cloud and on-prem environments.
To accelerate time to value, data needs to be seamlessly ingested, integrated and/or replicated to a cloud environment to take advantage of its analytical, BI and AI use cases. Google Cloud delivers a simplified approach for all your Data Movement needs through a highly differentiated product portfolio.
In this session, learn how organizations can unlock data value using best-in-class, cloud native products on Google Cloud and its partners such as Confluent.
Streaming Time Series Data With Kenny Gorman and Elena Cuevas | Current 2022HostedbyConfluent
Streaming Time Series Data With Kenny Gorman and Elena Cuevas | Current 2022
Modern streaming use cases are generating massive amounts of data - much of it needs to be organized and queried over time. The sheer amount and complexity of this problem presents new challenges for data engineers and developers alike.
To solve this problem Apache Kafka and MongoDB Time Series collections are a powerful combination. In this talk, Kenny Gorman and Elena Cuevas will present how Apache Kafka on Confluent Cloud can stream massive amounts of data to Time Series Collections via the MongoDB Connector for Apache Kafka. Elena and Kenny will discuss the required configuration details and critical components of Confluent Cloud and MongoDB Atlas as well as some tips, tricks and best practices.
You will leave armed with the knowledge of how Confluent Cloud, Apache Kafka, MongoDB Atlas, and Time Series collections fit into your event-driven architecture.
Streaming Data Ingest and Processing with Apache KafkaAttunity
Apache™ Kafka is a fast, scalable, durable, and fault-tolerant
publish-subscribe messaging system. It offers higher throughput, reliability and replication. To manage growing data volumes, many companies are leveraging Kafka for streaming data ingest and processing.
Join experts from Confluent, the creators of Apache™ Kafka, and the experts at Attunity, a leader in data integration software, for a live webinar where you will learn how to:
-Realize the value of streaming data ingest with Kafka
-Turn databases into live feeds for streaming ingest and processing
-Accelerate data delivery to enable real-time analytics
-Reduce skill and training requirements for data ingest
The recorded webinar on slide 32 includes a demo using automation software (Attunity Replicate) to stream live changes from a database into Kafka and also includes a Q&A with our experts.
For more information, please go to www.attunity.com/kafka.
App modernization on AWS with Apache Kafka and Confluent CloudKai Wähner
Presentation from AWS ReInvent 2020.
Learn how you can accelerate application modernization and benefit from the open-source Apache Kafka ecosystem by connecting your legacy, on-premises systems to the cloud. In this session, hear real customer stories about timely insights gained from event-driven applications built on an event streaming platform from Confluent Cloud running on AWS, which stores and processes historical data and real-time data streams. Confluent makes Apache Kafka enterprise-ready using infinite Kafka storage with Amazon S3 and multiple private networking options including AWS PrivateLink, along with self-managed encryption keys for storage volume encryption with AWS Key Management Service (AWS KMS).
The Road Most Traveled: A Kafka Story | Heikki Nousiainen, AivenHostedbyConfluent
When moving to a cloud native architecture Moogsoft knew they needed more scale than Rabbit could provide. Moogsoft moved into Kafka which is known for quick writing and driving heavy event driven workloads on top of niceties such as replayability. Choosing the tool was easy, finding a vendor that ticked all their boxes was not. They needed to ensure scalability, upgradability, builds via existing IAC pipelines, and observability via existing tools. When Moogsoft found Aiven, they were impressed with their offering and ability to scale on demand. During this presentation we will explore how Moogsoft used Aiven for Kafka to manage and scale their data in the cloud.
Confluent Partner Tech Talk with Synthesisconfluent
A discussion on the arduous planning process, and deep dive into the design/architectural decisions.
Learn more about the networking, RBAC strategies, the automation, and the deployment plan.
Build real-time streaming data pipelines to AWS with Confluentconfluent
Traditional data pipelines often face scalability issues and challenges related to cost, their monolithic design, and reliance on batch data processing. They also typically operate under the premise that all data needs to be stored in a single centralized data source before it's put to practical use. Confluent Cloud on Amazon Web Services (AWS) provides a fully managed cloud-native platform that helps you simplify the way you build real-time data flows using streaming data pipelines and Apache Kafka.
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.
Why Cloud-Native Kafka Matters: 4 Reasons to Stop Managing it YourselfDATAVERSITY
With your most talented teams bogged down managing a massive Kafka deployment, it can be challenging to move the dial on projects that drive real value for your business. For example, launching your next major feature, fueling more best-in-breed services like AI/ML on your cloud provider platform, or developing your first use cases for real-time data movement across clouds. By shifting to a fully managed, cloud-native service for Kafka you can unlock your teams to work on the projects that make the best use of your data in motion.
In this webinar you will learn about:
• The increasing value of data in motion to your business
• Challenges and costs of self-managing a large-scale Kafka deployment
• Benefits of managed cloud services for non-core activities like data storage, data warehousing, and messaging
• Optimizing time usage for value-generating activity like new product launches
• Potential cost savings for your business with a cloud-native service for Kafka
Technical Deep Dive: Using Apache Kafka to Optimize Real-Time Analytics in Fi...confluent
Watch this talk here: https://www.confluent.io/online-talks/using-apache-kafka-to-optimize-real-time-analytics-financial-services-iot-applications
When it comes to the fast-paced nature of capital markets and IoT, the ability to analyze data in real time is critical to gaining an edge. It’s not just about the quantity of data you can analyze at once, it’s about the speed, scale, and quality of the data you have at your fingertips.
Modern streaming data technologies like Apache Kafka and the broader Confluent platform can help detect opportunities and threats in real time. They can improve profitability, yield, and performance. Combining Kafka with Panopticon visual analytics provides a powerful foundation for optimizing your operations.
Use cases in capital markets include transaction cost analysis (TCA), risk monitoring, surveillance of trading and trader activity, compliance, and optimizing profitability of electronic trading operations. Use cases in IoT include monitoring manufacturing processes, logistics, and connected vehicle telemetry and geospatial data.
This online talk will include in depth practical demonstrations of how Confluent and Panopticon together support several key applications. You will learn:
-Why Apache Kafka is widely used to improve performance of complex operational systems
-How Confluent and Panopticon open new opportunities to analyze operational data in real time
-How to quickly identify and react immediately to fast-emerging trends, clusters, and anomalies
-How to scale data ingestion and data processing
-Build new analytics dashboards in minutes
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.
Similar to Unlock value with Confluent and AWS.pptx (20)
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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.
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/
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
How world-class product teams are winning in the AI era by CEO and Founder, P...
Unlock value with Confluent and AWS.pptx
1. Unlock value of your data in real-
time with Confluent and AWS
Ahmed Zamzam
Senior Partner Solutions Architect
2. Copyright 2023, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Agenda
Real-time Analytics with Confluent and AWS
Building event-streaming applications real time analytics
pipelines using Confluent and AWS
Presentation layer
Q/A
Confluent makes real-time data
streams top priority
Rise of data in motion
Event streaming with Confluent
Rearchitected Kafka, together with the features you
need to rapidly deploy production use cases
Enable BI and AI/ML use-cases
3. Copyright 2023, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Trending Now
Popular on Netflix
Top Picks for Joshua
Curbside
pickup
Loyalty rewards
Personalized
recommendations
Real-time trades
Ride ETA
Data is the fuel for a modern business
4. Copyright 2023, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Why Real-time data?
Source: Perishable insights, Mike Gualtieri, Forrester
Data loses value quickly over time
Real-time Seconds Minutes Hours Days Months
Value
of
data
to
decision-making
Preventive/Predictive
Actionable Reactive Historical
Time critical
decisions
Traditional “batch” business intelligence
Information half-life
in decision-making
5. Copyright 2023, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Typical real-time data pipeline
Data continuously
generated at a high
velocity from different
sources like IoT devices,
Application logs, Online
transactions, etc..
Source
Data captured and
stored in the order it
was received for set
duration of time, and
can be replayed
indefinitely.
Event Streaming
Process, analyse and
action on the data as
soon as it is generated
and in the order it was
received
Stream Processing
Sink data different
destinations. Dara Lakes
(most common) and/or
different Databases
Presentation
7. Copyright 2023, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
The rise of data in motion
70%
of fortune 500 companies
using Apache Kafka
(majority are Confluent customers)
8. Copyright 2023, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
...many more
Other
Systems
Other
Systems
Kafka
Connect
Kafka Cluster
Kafka
Connect
Apache Kafka is an Event Streaming Platform
11. Copyright 2023, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Everywhere
Be everywhere our
customers
want to be
Cloud-Native
Re-imagined
Kafka experience
for the Cloud
Complete
Enable developers
to reliably &
securely build next-
gen apps faster
The Confluent Product Advantage
12. Copyright 2023, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Leave Kafka reliability worries behind with
99.99% uptime SLA and 10x built-in durability
Never worry about Kafka storage limits again
with Infinite Storage that’s 10x more scalable
and performant
Scale and shrink to handle 0 to GBps+
workloads and peak customer demands
10x faster and easier
10x Kafka
Confluent Cloud offers a truly
fully managed, cloud-native
data streaming platform for
Apache Kafka, with 10x faster
scaling, infinitely more
storage, and built-in resilience
Resiliency
Storage
Elasticity
13. Copyright 2023, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Confluent Platform
The Enterprise Distribution of
Apache Kafka
Confluent Cloud
Apache Kafka reengineered
for the cloud
Self-managed software
Fully managed service
VM
Deploy on any platform, on premises, or
cloud
Available on
Confluent: Everywhere
14. Federated streaming, hybrid
and multi-cloud.
Data syndication and replication
across and between clouds and on-
premises, with self-service APIs, data
governance, and visual tooling.
Reliable & real-time data streams
between all customer sites, so you
can run always-on streaming
analytics on the data of the entire
enterprise, despite regional or cloud
provider outages.
Everywhere:
Cluster Linking Global Central Nervous System
15. “We are in the business of selling and renting clothes. We are not in the
business of managing an event streaming platform… If we had to
manage everything ourselves, I would’ve had to hire at least 10
more people to keep the systems up and running.”
● Architecture planning
● Cluster sizing
● Cluster provisioning
● Broker settings
● Zookeeper management
● Partition placement and data
durability
● Source/sink connectors
development and maintenance
● Monitoring and reporting tools
setup
● Software patches and upgrades
● Security controls and integrations
● Failover design and planning
● Mirroring and geo-replication
● Streaming data governance
● Load rebalancing and monitoring
● Expansion planning & execution
● Utilization optimization and
visibility
● Cluster migrations
● Infrastructure & performance
upgrades / enhancements
I N V E S T M E N T & T I M E
V
A
L
U
E
1
2
3
4
5
Experimentation
/ early interest
Central nervous
system
Mission critical,
disparate LOBs
Identify a
project
Mission-critical,
connected LOBs
Key challenges
Operational burden and resources
Manage and scale platform to support
ever-growing demand
Security and governance
Ensure streaming data is as safe and secure
as data-at-rest as Kafka usage scales
Real-time connectivity and processing
Leverage valuable legacy data to power modern,
cloud-based applications and experiences
Global availability
Maintain high availability across
environments with minimal downtime
Kafka is hard in experimentation. It only gets harder (and riskier)
as you add mission-critical data and use cases.
Operationalizing Kafka on your own is difficult
16. Discover, understand,
and trust your data
streams
Where did data come from?
Where is it going?
Where, when, and how was it transformed?
What’s the common taxonomy?
What is the current state of the stream?
Stream Catalog
Increase collaboration and productivity
with self-service data discovery
Stream Lineage
Understand complex data relationships
and uncover more insights
Stream Quality
Deliver trusted, high-quality event
streams to the business
“Confluent’s Stream Governance suite will play a major role in our expanded use of data in
motion and creation of a central nervous system for the enterprise. With the self-service
capabilities in stream catalog and stream lineage, we’ll be able to greatly simplify and
accelerate the onboarding of new teams working with our most valuable data."
17. Instantly connect popular data sources &
sinks
120+
prebuilt
connectors
100+ Confluent supported 20+ partner supported, Confluent verified
21. Copyright 2023, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Together Confluent and AWS empower Endless Use Cases
across many Industries
Retail
Healthcare
Finance &
Banking
Transportation
Common in all
Industries
Inventory
Management
Personalized
Promotions
Product
Development
& Introduction
Sentiment
Analysis
Streaming
Enterprise
Messaging
Systems of
Scale for High
Traffic Periods
Connected
Health
Records
Data
Confidentiality
& Accessibility
Dynamic Staff
Allocation
Optimization
Integrated
Treatment
Proactive Patient
Care
Real-Time
Monitoring
Early-On
Fraud
Detection
Capital
Management
Market Risk
Recognition &
Investigation
Preventive
Regulatory
Scanning
Real-Time What-
If
Analysis
Trade Flow
Monitoring
Advanced
Navigation
Environmental
Factor
Processing
Fleet
Management
Predictive
Maintenance
Threat Detection
& Real-Time
Response
Traffic
Distribution
Optimization
Data Pipelines
Hybrid Cloud
Integration
Microservices
Security and
Fraud
Customer 360 Streaming ETL
24. ksqlDB at a glance
What is it?
ksqlDB is an event-streaming
database for working with
streams and tables of data
All the key features of a
modern streaming solution
Aggregations Joins
Windowing
Event-time
Dual query
support
Exactly-once
semantics
Out-of-order
handling
User-defined
functions
CREATE TABLE activePromotions AS
SELECT rideId,
qualifyPromotion(distanceToDst) AS promotion
FROM locations
GROUP BY rideId
EMIT CHANGES
How does it work?
It separates compute from storage, and scales
elastically in a fault-tolerant manner
It remains highly available during disruption, even in
the face of failure to a quorum of its servers
25. Kafka clients Kafka streams ksqlDB
ConsumerRecords<String, String> records =
consumer.poll(100);
Map<String, Integer> counts = new DefaultMap<String,
Integer>();
for (ConsumerRecord<String, Integer> record : records) {
String key = record.key();
int c = counts.get(key)
c += record.value()
counts.put(key, c)
}
for (Map.Entry<String, Integer> entry : counts.entrySet())
{
int stateCount;
int attempts;
while (attempts++ < MAX_RETRIES) {
try {
stateCount = stateStore.getValue(entry.getKey())
stateStore.setValue(entry.getKey(), entry.getValue() +
stateCount)
break;
} catch (StateStoreException e) {
RetryUtils.backoff(attempts);
}
}
}
builder
.stream("input-stream",
Consumed.with(Serdes.String(), Serdes.String()))
.groupBy((key, value) -> value)
.count()
.toStream()
.to("counts", Produced.with(Serdes.String(),
Serdes.Long()));
SELECT x, count(*) FROM stream GROUP BY x EMIT
CHANGES;
Flexibility Simplicity
3 modalities of stream processing with Confluent
26. 2. Stateless Stream processing with AWS Lambda
Event
source
mapping
Lambda service
Confluent Kafka sink
connector
• Sink connector polls Kafka partitions and
invokes your function
• Lambda can be invoked synchronously or
asynchronously
• At least once semantics
• Provides a dead letter queue (DLQ) for any
failed invocations
• Sink connector scales up to a soft maximum
of 10 connectors
• Lambda service polls the Kafka partitions and invokes
your Lambda function synchronously
• Starts with one concurrent poller and customer
function
• Scaling
○ Lambda service checks every 3 minutes if
scaling is needed
○ Starts with 1 poller and scales up to ≤
#partitions
• Batch records based on a BatchSize or Batchwindow
32. ksqlDB Kafka Streams
Kinesis Data
Analytics
Lambda
Fully Managed ✅ — ✅ ✅
TYPE Stateful and Stateless
Stateful and
Stateless
Stateful and
Stateless
Stateless
FAULT TOLERANCE Exactly once Exactly once Exactly once At-least once
UDF SUPPORT ✅
(self-managed)
✅
(self-managed)
✅ ✅
LATENCY FAST VERY FAST VERY FAST FAST
When to use which?
34. Amazon Redshift
sink
AWS Lambda
sink
AWS Direct
Connect
ClusterLink
LEGACY EDW
MAINFRAME
LEGACY DB
JDBC/CDC
connectors
Connect
Leverage +120 Confluent prebuilt connectors to continuously
bring valuable data from existing services on-premises, including
enterprise data warehouse, databases, and mainframes
Modernize
Increase agility in getting applications to market and reduce TCO
when freeing up resources to focus on value-generating activities
and not in managing servers
On premises AWS Cloud
Bridge
Hybrid cloud streaming with
consistent, event-driven
architecture for modern apps
Amazon Athena
AWS Glue
Amazon
SageMaker
AWS Lake
Formation
Amazon
DynamoDB
Amazon
Aurora
Amazon S3 sink
Data streams
Applications
ksqlDB
Amazon S3
Amazon Redshift
AWS Lambda
Accelerate modernization from on premises
to AWS
35. Thank You & Next Steps
How did we do? Enter your feedback! Learn more
Check out Confluent - AWS Workshops -
https://confluent.awsworkshop.io/
Try it out yourself!
Subscribe to Confluent Cloud on the AWS
Marketplace and start with a free $400 (to
be used within 30 days).
1
2
3
Schedule a workshop/hackathon
Pick a problem and schedule a
workshop/hackathon with Confluent and
AWS for your team.
37. ● Source static data from
RDS – MySQL
● Process applications
using static data and real-
time events in Confluent
Cloud
● Visualize data sinked into
Redshift using
QuickSight
Live Demo!