Legacy migration is a journey. Mainframes cannot be replaced in a single project. A big bang will fail. This has to be planned long-term.
Mainframe offloading and replacement with Apache Kafka and its ecosystem can be used to keep a more modern data store in real-time sync with the mainframe, while at the same time persisting the event data on the bus to enable microservices, and deliver the data to other systems such as data warehouses and search indexes.
This session walks through the different steps some companies are already gone through. Technical options like Change Data Capture (CDC), MQ, and third-party tools for mainframe integration, offloading and replacement are explored.
Real-Life Use Cases & Architectures for Event Streaming with Apache KafkaKai Wähner
Streaming all over the World: Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka.
Learn about various case studies for event streaming with Apache Kafka across industries. The talk explores architectures for real-world deployments from Audi, BMW, Disney, Generali, Paypal, Tesla, Unity, Walmart, William Hill, and more. Use cases include fraud detection, mainframe offloading, predictive maintenance, cybersecurity, edge computing, track&trace, live betting, and much more.
The Top 5 Apache Kafka Use Cases and Architectures in 2022Kai Wähner
I see the following topics coming up more regularly in conversations with customers, prospects, and the broader Kafka community across the globe:
Kappa Architecture: Kappa goes mainstream to replace Lambda and Batch pipelines (that does not mean that there is no batch processing anymore). Examples: Kafka-powered Kappa architectures from Uber, Disney, Shopify, and Twitter.
Hyper-personalized Omnichannel: Retail and customer communication across online and offline channels becomes the new black, including context-specific upselling, recommendations, and location-based services. Examples: Omnichannel Retail and Customer 360 in Real-Time with Apache Kafka.
Multi-Cloud Deployments: Business units and IT infrastructures span across regions, continents, and cloud providers. Linking clusters for bi-directional replication of data in real-time becomes crucial for many business models. Examples: Global Kafka deployments.
Edge Analytics: Low latency requirements, cost efficiency, or security requirements enforce the deployment of (some) event streaming use cases at the far edge (i.e., outside a data center), for instance, for predictive maintenance and quality assurance on the shop floor level in smart factories. Examples: Edge analytics with Kafka.
Real-time Cybersecurity: Situational awareness and threat intelligence need to process massive data in real-time to defend against cyberattacks successfully. The many successful ransomware attacks across the globe in 2021 were a warning for most CIOs. Examples: Cybersecurity for situational awareness and threat intelligence in real-time.
Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job?
This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka.
No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck.
A detailed article about this topic:
https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/
IoT Architectures for Apache Kafka and Event Streaming - Industry 4.0, Digita...Kai Wähner
The Internet of Things (IoT) is getting more and more traction as valuable use cases come to light. Whether you are in Healthcare, Telecommunications, Manufacturing, Banking or Retail to name a few industries, there is one key challenge and that's the integration of backend IoT data logs and applications, business services and cloud services to process the data in real time and at scale.
In this talk, we will be sharing how Kafka has become the leading technology used throughout the business to provide Real Time Event Streaming. Explore real life use cases of Kafka Connect, Kafka Streams and KSQL independent of the data deployment be it on a private or public Cloud, On Premise or at the Edge.
Audi - Connected car infrastructure
Robert Bosch Power Tools - Track and Trace of devices and people at construction areas
Deutsche Bahn - Customer 360 for train timetable updates
E.ON - IoT Streaming Platform to integrate and build smart home, smart building and smart grid infrastructures
Running Apache Kafka in production is only the first step in the Kafka operations journey. Professional Kafka users are ready to handle all possible disasters - because for most businesses having a disaster recovery plan is not optional.
In this session, we’ll discuss disaster scenarios that can take down entire Kafka clusters and share advice on how to plan, prepare and handle these events. This is a technical session full of best practices - we want to make sure you are ready to handle the worst mayhem that nature and auditors can cause.
Visit www.confluent.io for more information.
Top 5 Event Streaming Use Cases for 2021 with Apache KafkaKai Wähner
Apache Kafka and Event Streaming are two of the most relevant buzzwords in tech these days. Ever wonder what the predicted TOP 5 Event Streaming Architectures and Use Cases for 2021 are? Check out the following presentation. Learn about edge deployments, hybrid and multi-cloud architectures, service mesh-based microservices, streaming machine learning, and cybersecurity.
On-demand video recording: https://videos.confluent.io/watch/XAjxV3j8hzwCcEKoZVErUJ
Service Mesh with Apache Kafka, Kubernetes, Envoy, Istio and LinkerdKai Wähner
Microservice architectures are not free lunch! Microservices need to be decoupled, flexible, operationally transparent, data aware and elastic. Most material from last years only discusses point-to-point architectures with inflexible and non-scalable technologies like REST / HTTP. This video takes a look at cutting edge technologies like Apache Kafka, Kubernetes, Envoy, Linkerd and Istio to implement a cloud-native service mesh to solve these challenges and bring microservices to the next level of scale, speed and efficiency.
Key takeaways:
- Apache Kafka decouples services, including event streams and request-response
- Kubernetes provides a cloud-native infrastructure for the Kafka ecosystem
- Service Mesh helps with security and observability at ecosystem / organization scale
- Envoy and Istio sit in the layer above Kafka and are orthogonal to the goals Kafka addresses
Blog post: http://www.kai-waehner.de/blog/2019/09/24/cloud-native-apache-kafka-kubernetes-envoy-istio-linkerd-service-mesh
Video recording of this slide deck: https://youtu.be/Us_C4RFOUrA
Real-Life Use Cases & Architectures for Event Streaming with Apache KafkaKai Wähner
Streaming all over the World: Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka.
Learn about various case studies for event streaming with Apache Kafka across industries. The talk explores architectures for real-world deployments from Audi, BMW, Disney, Generali, Paypal, Tesla, Unity, Walmart, William Hill, and more. Use cases include fraud detection, mainframe offloading, predictive maintenance, cybersecurity, edge computing, track&trace, live betting, and much more.
The Top 5 Apache Kafka Use Cases and Architectures in 2022Kai Wähner
I see the following topics coming up more regularly in conversations with customers, prospects, and the broader Kafka community across the globe:
Kappa Architecture: Kappa goes mainstream to replace Lambda and Batch pipelines (that does not mean that there is no batch processing anymore). Examples: Kafka-powered Kappa architectures from Uber, Disney, Shopify, and Twitter.
Hyper-personalized Omnichannel: Retail and customer communication across online and offline channels becomes the new black, including context-specific upselling, recommendations, and location-based services. Examples: Omnichannel Retail and Customer 360 in Real-Time with Apache Kafka.
Multi-Cloud Deployments: Business units and IT infrastructures span across regions, continents, and cloud providers. Linking clusters for bi-directional replication of data in real-time becomes crucial for many business models. Examples: Global Kafka deployments.
Edge Analytics: Low latency requirements, cost efficiency, or security requirements enforce the deployment of (some) event streaming use cases at the far edge (i.e., outside a data center), for instance, for predictive maintenance and quality assurance on the shop floor level in smart factories. Examples: Edge analytics with Kafka.
Real-time Cybersecurity: Situational awareness and threat intelligence need to process massive data in real-time to defend against cyberattacks successfully. The many successful ransomware attacks across the globe in 2021 were a warning for most CIOs. Examples: Cybersecurity for situational awareness and threat intelligence in real-time.
Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job?
This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka.
No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck.
A detailed article about this topic:
https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/
IoT Architectures for Apache Kafka and Event Streaming - Industry 4.0, Digita...Kai Wähner
The Internet of Things (IoT) is getting more and more traction as valuable use cases come to light. Whether you are in Healthcare, Telecommunications, Manufacturing, Banking or Retail to name a few industries, there is one key challenge and that's the integration of backend IoT data logs and applications, business services and cloud services to process the data in real time and at scale.
In this talk, we will be sharing how Kafka has become the leading technology used throughout the business to provide Real Time Event Streaming. Explore real life use cases of Kafka Connect, Kafka Streams and KSQL independent of the data deployment be it on a private or public Cloud, On Premise or at the Edge.
Audi - Connected car infrastructure
Robert Bosch Power Tools - Track and Trace of devices and people at construction areas
Deutsche Bahn - Customer 360 for train timetable updates
E.ON - IoT Streaming Platform to integrate and build smart home, smart building and smart grid infrastructures
Running Apache Kafka in production is only the first step in the Kafka operations journey. Professional Kafka users are ready to handle all possible disasters - because for most businesses having a disaster recovery plan is not optional.
In this session, we’ll discuss disaster scenarios that can take down entire Kafka clusters and share advice on how to plan, prepare and handle these events. This is a technical session full of best practices - we want to make sure you are ready to handle the worst mayhem that nature and auditors can cause.
Visit www.confluent.io for more information.
Top 5 Event Streaming Use Cases for 2021 with Apache KafkaKai Wähner
Apache Kafka and Event Streaming are two of the most relevant buzzwords in tech these days. Ever wonder what the predicted TOP 5 Event Streaming Architectures and Use Cases for 2021 are? Check out the following presentation. Learn about edge deployments, hybrid and multi-cloud architectures, service mesh-based microservices, streaming machine learning, and cybersecurity.
On-demand video recording: https://videos.confluent.io/watch/XAjxV3j8hzwCcEKoZVErUJ
Service Mesh with Apache Kafka, Kubernetes, Envoy, Istio and LinkerdKai Wähner
Microservice architectures are not free lunch! Microservices need to be decoupled, flexible, operationally transparent, data aware and elastic. Most material from last years only discusses point-to-point architectures with inflexible and non-scalable technologies like REST / HTTP. This video takes a look at cutting edge technologies like Apache Kafka, Kubernetes, Envoy, Linkerd and Istio to implement a cloud-native service mesh to solve these challenges and bring microservices to the next level of scale, speed and efficiency.
Key takeaways:
- Apache Kafka decouples services, including event streams and request-response
- Kubernetes provides a cloud-native infrastructure for the Kafka ecosystem
- Service Mesh helps with security and observability at ecosystem / organization scale
- Envoy and Istio sit in the layer above Kafka and are orthogonal to the goals Kafka addresses
Blog post: http://www.kai-waehner.de/blog/2019/09/24/cloud-native-apache-kafka-kubernetes-envoy-istio-linkerd-service-mesh
Video recording of this slide deck: https://youtu.be/Us_C4RFOUrA
Mainframe Integration, Offloading and Replacement with Apache KafkaKai Wähner
Video recording of this presentation:
https://youtu.be/upWzamacOVQ
Blog post with more details:
https://www.kai-waehner.de/blog/2020/04/24/mainframe-offloading-replacement-apache-kafka-connect-ibm-db2-mq-cdc-cobol/
Mainframes are still hard at work, processing over 70 percent of the world’s most essential computing transactions every day. Very high cost, monolithic architectures, and missing experts are the key challenges for mainframe applications. Time to get more innovative, even with the mainframe!
Mainframe offloading with Apache Kafka and its ecosystem can be used to keep a more modern data store in real-time sync with the mainframe. At the same time, it is persisting the event data on the bus to enable microservices, and deliver the data to other systems such as data warehouses and search indexes.
But the final goal and ultimate vision are to replace the mainframe by new applications using modern and less costly technologies. Stand up to the dinosaur, but keep in mind that legacy migration is a journey! Kai will guide you to the next step of your company’s evolution!
You will learn:
- how to not only reduce operational expenses but provide a path for architecture modernization, agility and eventually mainframe replacement
- what steps some of Confluent’s customers already took, leveraging technologies like Change Data Capture (CDC) or MQ for mainframe offloading
- how an event streaming platform enables cost reduction, architecture modernization, and a combination of a mainframe with new technologies
Data Streaming with Apache Kafka in the Defence and Cybersecurity IndustryKai Wähner
Agenda:
1) Defence, Modern Warfare, and Cybersecurity in 202X
2) Data in Motion with Apache Kafka as Defence Backbone
3) Situational Awareness
4) Threat Intelligence
5) Forensics and AI / Machine Learning
6) Air-Gapped and Zero Trust Environments
7) SIEM / SOAR Modernization
Technologies discussed in the presentation include Apache Kafka, Kafka Streams, kqlDB, Kafka Connect, Elasticsearch, Splunk, IBM QRadar, Zeek, Netflow, PCAP, TensorFlow, AWS, Azure, GCP, Sigma, Confluent Cloud,
Apache Kafka vs. Integration Middleware (MQ, ETL, ESB) - Friends, Enemies or ...confluent
MQ, ETL and ESB middleware are often used as integration backbone between legacy applications, modern microservices and cloud services. This introduces several challenges and complexities like point-to-point integration or non-scalable architectures. This session discusses how to build a completely event-driven streaming platform leveraging Apache Kafka’s open source messaging, integration and streaming components to leverage distributed processing, fault-tolerance, rolling upgrades and the ability to reprocess events. Learn the differences between a event-driven streaming platform leveraging Apache Kafka and middleware like MQ, ETL and ESBs – including best practices and anti-patterns, but also how these concepts and tools complement each other in an enterprise architecture.
A brief introduction to Apache Kafka and describe its usage as a platform for streaming data. It will introduce some of the newer components of Kafka that will help make this possible, including Kafka Connect, a framework for capturing continuous data streams, and Kafka Streams, a lightweight stream processing library.
Building Cloud-Native App Series - Part 11 of 11
Microservices Architecture Series
Service Mesh - Observability
- Zipkin
- Prometheus
- Grafana
- Kiali
Kappa vs Lambda Architectures and Technology ComparisonKai Wähner
Real-time data beats slow data. That’s true for almost every use case. Nevertheless, enterprise architects build new infrastructures with the Lambda architecture that includes separate batch and real-time layers.
This video explores why a single real-time pipeline, called Kappa architecture, is the better fit for many enterprise architectures. Real-world examples from companies such as Disney, Shopify, Uber, and Twitter explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for a Lambda architecture.
The main focus of the discussion is on Apache Kafka (and its ecosystem) as the de facto standard for event streaming to process data in motion (the key concept of Kappa), but the video also compares various technologies and vendors such as Confluent, Cloudera, IBM Red Hat, Apache Flink, Apache Pulsar, AWS Kinesis, Amazon MSK, Azure Event Hubs, Google Pub Sub, and more.
Video recording of this presentation:
https://youtu.be/j7D29eyysDw
Further reading:
https://www.kai-waehner.de/blog/2021/09/23/real-time-kappa-architecture-mainstream-replacing-batch-lambda/
https://www.kai-waehner.de/blog/2021/04/20/comparison-open-source-apache-kafka-vs-confluent-cloudera-red-hat-amazon-msk-cloud/
https://www.kai-waehner.de/blog/2021/05/09/kafka-api-de-facto-standard-event-streaming-like-amazon-s3-object-storage/
A Kafka journey and why migrate to Confluent Cloud?confluent
Using a success story as an example, we talk about how FRSHUB with the Apache Kafka Eco-System became an optimal business intelligence platform that interprets data from multiple sources to show real-time and predictive views of the for different business units. We talk about our IAC deployment in Azure, the API management, and why we are migrating to the Confluent Cloud.
Building Cloud-Native App Series - Part 2 of 11
Microservices Architecture Series
Event Sourcing & CQRS,
Kafka, Rabbit MQ
Case Studies (E-Commerce App, Movie Streaming, Ticket Booking, Restaurant, Hospital Management)
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
Not all workloads allow cloud computing. Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration.
This session explores architectures and design patterns for software and hardware considerations to deploy hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell and Confluent Cloud.
The Rise of Data in Motion in the Healthcare Industry - Use Cases, Architectures and Examples powered by Apache Kafka.
Use Cases for Data in Motion in the Healthcare Industry:
- Know Your Patient (= “Customer 360”)
- Operations (Healthcare 4.0 including Drug R&D, Patient Care, etc.)
- IT Perspective (Cybersecurity, Mainframe Offload, Hybrid Cloud, Streaming ETL, etc)
Real-world examples include Covid-19 Electronic Lab Reporting, Cerner, Optum, Centene, Humana, Invitae, Bayer, Celmatix, Care.com.
Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?Kai Wähner
The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems.
Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a presentation.
The slides cover technologies such as Apache Kafka, Apache Spark, Confluent, Databricks, Snowflake, Elasticsearch, AWS Redshift, GCP with Google Bigquery, and Azure Synapse.
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
Developing custom transformation in the Kafka connect to minimize data redund...HostedbyConfluent
Compacted topics grow over time and are often utilizing high performance, low latency and relatively expensive storage solutions. Reducing duplicated data plays a critical role in the size of compacted topics. with less data on the topics, the Kafka cluster consumes less disk space which in turn it leads to lower operation cost.
in this use case-driven talk, we are going to demonstrate how our team at UnitedHealth Group leveraged existing transformers to extract data from the message metadata in the topic as well as how we developed our customized transformers to minimize the amount of duplicated data in each message in the topic.
Apache Kafka in the Airline, Aviation and Travel IndustryKai Wähner
Aviation and travel are notoriously vulnerable to social, economic, and political events, as well as the ever-changing expectations of consumers. Coronavirus is just a piece of the challenge.
This presentation explores use cases, architectures, and references for Apache Kafka as event streaming technology in the aviation industry, including airline, airports, global distribution systems (GDS), aircraft manufacturers, and more.
Examples include Lufthansa, Singapore Airlines, Air France Hop, Amadeus, and more. Technologies include Kafka, Kafka Connect, Kafka Streams, ksqlDB, Machine Learning, Cloud, and more.
Apache Kafka in the Transportation and LogisticsKai Wähner
Event Streaming with Apache Kafka in the Transportation and Logistics.
Track & Trace, Real-time Locating System, Customer 360, Open API, and more…
Examples include Swiss Post, SBB, Deutsche Bahn, Hermes, Migros, Here Technologies, Otonomo, Lyft, Uber, Free Now, Lufthansa, Air France, Singapore Airlines, Amadeus Group, and more.
Architecture patterns for distributed, hybrid, edge and global Apache Kafka d...Kai Wähner
Architecture patterns for distributed, hybrid, edge and global Apache Kafka deployments
Multi-cluster and cross-data center deployments of Apache Kafka have become the norm rather than an exception. This session gives an overview of several scenarios that may require multi-cluster solutions and discusses real-world examples with their specific requirements and trade-offs, including disaster recovery, aggregation for analytics, cloud migration, mission-critical stretched deployments and global Kafka.
Key takeaways:
In many scenarios, one Kafka cluster is not enough. Understand different architectures and alternatives for multi-cluster deployments.
Zero data loss and high availability are two key requirements. Understand how to realize this, including trade-offs.
Learn about features and limitations of Kafka for multi cluster deployments
Global Kafka and mission-critical multi-cluster deployments with zero data loss and high availability became the normal, not an exception.
Confluent REST Proxy and Schema Registry (Concepts, Architecture, Features)Kai Wähner
High level introduction to Confluent REST Proxy and Schema Registry (leveraging Apache Avro under the hood), two components of the Apache Kafka open source ecosystem. See the concepts, architecture and features.
Can Apache Kafka Replace a Database? – The 2021 Update | Kai Waehner, ConfluentHostedbyConfluent
Can and should Apache Kafka replace a database? How long can and should I store data in Kafka? How can I query and process data in Kafka? These are common questions that come up more and more. This session explains the idea behind databases and different features like storage, queries, transactions, and processing to evaluate when Kafka is a good fit, and when it is not. The discussion includes different Kafka-native add-ons like Tiered Storage for long-term, cost-efficient storage, and ksqlDB as an event streaming database. The relation and trade-offs between Kafka and other databases are explored to complement each other instead of thinking about a replacement. This includes different options for pull and push-based bi-directional integration.
Can and should Apache Kafka replace a database? How long can and should I store data in Kafka? How can I query and process data in Kafka? These are common questions that come up more and more. This session explains the idea behind databases and different features like storage, queries, transactions, and processing to evaluate when Kafka is a good fit and when it is not.
The discussion includes different Kafka-native add-ons like Tiered Storage for long-term, cost-efficient storage and ksqlDB as event streaming database. The relation and trade-offs between Kafka and other databases are explored to complement each other instead of thinking about a replacement. This includes different options for pull and push-based bi-directional integration.
Key takeaways:
- Kafka can store data forever in a durable and high available manner
- Kafka has different options to query historical data
- Kafka-native add-ons like ksqlDB or Tiered Storage make Kafka more powerful than ever before to store and process data
- Kafka does not provide transactions, but exactly-once semantics
- Kafka is not a replacement for existing databases like MySQL, MongoDB or Elasticsearch
- Kafka and other databases complement each other; the right solution has to be selected for a problem
- Different options are available for bi-directional pull and push-based integration between Kafka and databases to complement each other
Video Recording:
https://youtu.be/7KEkWbwefqQ
Blog post:
https://www.kai-waehner.de/blog/2020/03/12/can-apache-kafka-replace-database-acid-storage-transactions-sql-nosql-data-lake/
Mainframe Integration, Offloading and Replacement with Apache KafkaKai Wähner
Video recording of this presentation:
https://youtu.be/upWzamacOVQ
Blog post with more details:
https://www.kai-waehner.de/blog/2020/04/24/mainframe-offloading-replacement-apache-kafka-connect-ibm-db2-mq-cdc-cobol/
Mainframes are still hard at work, processing over 70 percent of the world’s most essential computing transactions every day. Very high cost, monolithic architectures, and missing experts are the key challenges for mainframe applications. Time to get more innovative, even with the mainframe!
Mainframe offloading with Apache Kafka and its ecosystem can be used to keep a more modern data store in real-time sync with the mainframe. At the same time, it is persisting the event data on the bus to enable microservices, and deliver the data to other systems such as data warehouses and search indexes.
But the final goal and ultimate vision are to replace the mainframe by new applications using modern and less costly technologies. Stand up to the dinosaur, but keep in mind that legacy migration is a journey! Kai will guide you to the next step of your company’s evolution!
You will learn:
- how to not only reduce operational expenses but provide a path for architecture modernization, agility and eventually mainframe replacement
- what steps some of Confluent’s customers already took, leveraging technologies like Change Data Capture (CDC) or MQ for mainframe offloading
- how an event streaming platform enables cost reduction, architecture modernization, and a combination of a mainframe with new technologies
Data Streaming with Apache Kafka in the Defence and Cybersecurity IndustryKai Wähner
Agenda:
1) Defence, Modern Warfare, and Cybersecurity in 202X
2) Data in Motion with Apache Kafka as Defence Backbone
3) Situational Awareness
4) Threat Intelligence
5) Forensics and AI / Machine Learning
6) Air-Gapped and Zero Trust Environments
7) SIEM / SOAR Modernization
Technologies discussed in the presentation include Apache Kafka, Kafka Streams, kqlDB, Kafka Connect, Elasticsearch, Splunk, IBM QRadar, Zeek, Netflow, PCAP, TensorFlow, AWS, Azure, GCP, Sigma, Confluent Cloud,
Apache Kafka vs. Integration Middleware (MQ, ETL, ESB) - Friends, Enemies or ...confluent
MQ, ETL and ESB middleware are often used as integration backbone between legacy applications, modern microservices and cloud services. This introduces several challenges and complexities like point-to-point integration or non-scalable architectures. This session discusses how to build a completely event-driven streaming platform leveraging Apache Kafka’s open source messaging, integration and streaming components to leverage distributed processing, fault-tolerance, rolling upgrades and the ability to reprocess events. Learn the differences between a event-driven streaming platform leveraging Apache Kafka and middleware like MQ, ETL and ESBs – including best practices and anti-patterns, but also how these concepts and tools complement each other in an enterprise architecture.
A brief introduction to Apache Kafka and describe its usage as a platform for streaming data. It will introduce some of the newer components of Kafka that will help make this possible, including Kafka Connect, a framework for capturing continuous data streams, and Kafka Streams, a lightweight stream processing library.
Building Cloud-Native App Series - Part 11 of 11
Microservices Architecture Series
Service Mesh - Observability
- Zipkin
- Prometheus
- Grafana
- Kiali
Kappa vs Lambda Architectures and Technology ComparisonKai Wähner
Real-time data beats slow data. That’s true for almost every use case. Nevertheless, enterprise architects build new infrastructures with the Lambda architecture that includes separate batch and real-time layers.
This video explores why a single real-time pipeline, called Kappa architecture, is the better fit for many enterprise architectures. Real-world examples from companies such as Disney, Shopify, Uber, and Twitter explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for a Lambda architecture.
The main focus of the discussion is on Apache Kafka (and its ecosystem) as the de facto standard for event streaming to process data in motion (the key concept of Kappa), but the video also compares various technologies and vendors such as Confluent, Cloudera, IBM Red Hat, Apache Flink, Apache Pulsar, AWS Kinesis, Amazon MSK, Azure Event Hubs, Google Pub Sub, and more.
Video recording of this presentation:
https://youtu.be/j7D29eyysDw
Further reading:
https://www.kai-waehner.de/blog/2021/09/23/real-time-kappa-architecture-mainstream-replacing-batch-lambda/
https://www.kai-waehner.de/blog/2021/04/20/comparison-open-source-apache-kafka-vs-confluent-cloudera-red-hat-amazon-msk-cloud/
https://www.kai-waehner.de/blog/2021/05/09/kafka-api-de-facto-standard-event-streaming-like-amazon-s3-object-storage/
A Kafka journey and why migrate to Confluent Cloud?confluent
Using a success story as an example, we talk about how FRSHUB with the Apache Kafka Eco-System became an optimal business intelligence platform that interprets data from multiple sources to show real-time and predictive views of the for different business units. We talk about our IAC deployment in Azure, the API management, and why we are migrating to the Confluent Cloud.
Building Cloud-Native App Series - Part 2 of 11
Microservices Architecture Series
Event Sourcing & CQRS,
Kafka, Rabbit MQ
Case Studies (E-Commerce App, Movie Streaming, Ticket Booking, Restaurant, Hospital Management)
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
Not all workloads allow cloud computing. Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration.
This session explores architectures and design patterns for software and hardware considerations to deploy hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell and Confluent Cloud.
The Rise of Data in Motion in the Healthcare Industry - Use Cases, Architectures and Examples powered by Apache Kafka.
Use Cases for Data in Motion in the Healthcare Industry:
- Know Your Patient (= “Customer 360”)
- Operations (Healthcare 4.0 including Drug R&D, Patient Care, etc.)
- IT Perspective (Cybersecurity, Mainframe Offload, Hybrid Cloud, Streaming ETL, etc)
Real-world examples include Covid-19 Electronic Lab Reporting, Cerner, Optum, Centene, Humana, Invitae, Bayer, Celmatix, Care.com.
Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?Kai Wähner
The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems.
Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a presentation.
The slides cover technologies such as Apache Kafka, Apache Spark, Confluent, Databricks, Snowflake, Elasticsearch, AWS Redshift, GCP with Google Bigquery, and Azure Synapse.
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
Developing custom transformation in the Kafka connect to minimize data redund...HostedbyConfluent
Compacted topics grow over time and are often utilizing high performance, low latency and relatively expensive storage solutions. Reducing duplicated data plays a critical role in the size of compacted topics. with less data on the topics, the Kafka cluster consumes less disk space which in turn it leads to lower operation cost.
in this use case-driven talk, we are going to demonstrate how our team at UnitedHealth Group leveraged existing transformers to extract data from the message metadata in the topic as well as how we developed our customized transformers to minimize the amount of duplicated data in each message in the topic.
Apache Kafka in the Airline, Aviation and Travel IndustryKai Wähner
Aviation and travel are notoriously vulnerable to social, economic, and political events, as well as the ever-changing expectations of consumers. Coronavirus is just a piece of the challenge.
This presentation explores use cases, architectures, and references for Apache Kafka as event streaming technology in the aviation industry, including airline, airports, global distribution systems (GDS), aircraft manufacturers, and more.
Examples include Lufthansa, Singapore Airlines, Air France Hop, Amadeus, and more. Technologies include Kafka, Kafka Connect, Kafka Streams, ksqlDB, Machine Learning, Cloud, and more.
Apache Kafka in the Transportation and LogisticsKai Wähner
Event Streaming with Apache Kafka in the Transportation and Logistics.
Track & Trace, Real-time Locating System, Customer 360, Open API, and more…
Examples include Swiss Post, SBB, Deutsche Bahn, Hermes, Migros, Here Technologies, Otonomo, Lyft, Uber, Free Now, Lufthansa, Air France, Singapore Airlines, Amadeus Group, and more.
Architecture patterns for distributed, hybrid, edge and global Apache Kafka d...Kai Wähner
Architecture patterns for distributed, hybrid, edge and global Apache Kafka deployments
Multi-cluster and cross-data center deployments of Apache Kafka have become the norm rather than an exception. This session gives an overview of several scenarios that may require multi-cluster solutions and discusses real-world examples with their specific requirements and trade-offs, including disaster recovery, aggregation for analytics, cloud migration, mission-critical stretched deployments and global Kafka.
Key takeaways:
In many scenarios, one Kafka cluster is not enough. Understand different architectures and alternatives for multi-cluster deployments.
Zero data loss and high availability are two key requirements. Understand how to realize this, including trade-offs.
Learn about features and limitations of Kafka for multi cluster deployments
Global Kafka and mission-critical multi-cluster deployments with zero data loss and high availability became the normal, not an exception.
Confluent REST Proxy and Schema Registry (Concepts, Architecture, Features)Kai Wähner
High level introduction to Confluent REST Proxy and Schema Registry (leveraging Apache Avro under the hood), two components of the Apache Kafka open source ecosystem. See the concepts, architecture and features.
Can Apache Kafka Replace a Database? – The 2021 Update | Kai Waehner, ConfluentHostedbyConfluent
Can and should Apache Kafka replace a database? How long can and should I store data in Kafka? How can I query and process data in Kafka? These are common questions that come up more and more. This session explains the idea behind databases and different features like storage, queries, transactions, and processing to evaluate when Kafka is a good fit, and when it is not. The discussion includes different Kafka-native add-ons like Tiered Storage for long-term, cost-efficient storage, and ksqlDB as an event streaming database. The relation and trade-offs between Kafka and other databases are explored to complement each other instead of thinking about a replacement. This includes different options for pull and push-based bi-directional integration.
Can and should Apache Kafka replace a database? How long can and should I store data in Kafka? How can I query and process data in Kafka? These are common questions that come up more and more. This session explains the idea behind databases and different features like storage, queries, transactions, and processing to evaluate when Kafka is a good fit and when it is not.
The discussion includes different Kafka-native add-ons like Tiered Storage for long-term, cost-efficient storage and ksqlDB as event streaming database. The relation and trade-offs between Kafka and other databases are explored to complement each other instead of thinking about a replacement. This includes different options for pull and push-based bi-directional integration.
Key takeaways:
- Kafka can store data forever in a durable and high available manner
- Kafka has different options to query historical data
- Kafka-native add-ons like ksqlDB or Tiered Storage make Kafka more powerful than ever before to store and process data
- Kafka does not provide transactions, but exactly-once semantics
- Kafka is not a replacement for existing databases like MySQL, MongoDB or Elasticsearch
- Kafka and other databases complement each other; the right solution has to be selected for a problem
- Different options are available for bi-directional pull and push-based integration between Kafka and databases to complement each other
Video Recording:
https://youtu.be/7KEkWbwefqQ
Blog post:
https://www.kai-waehner.de/blog/2020/03/12/can-apache-kafka-replace-database-acid-storage-transactions-sql-nosql-data-lake/
Unleashing Apache Kafka and TensorFlow in Hybrid Cloud ArchitecturesKai Wähner
Talk at Strate Conference in London: Unleashing Apache Kafka and TensorFlow in Hybrid Cloud Architectures with Confluent:
How do you leverage the flexibility and extreme scale of the public cloud and the Apache Kafka ecosystem to build scalable, mission-critical machine learning infrastructures that span multiple public clouds—or bridge your on-premises data centre to the cloud?
Join Kai Wähner to learn how to use technologies such as TensorFlow with Kafka’s open source ecosystem for machine learning infrastructures. You’ll learn how to build a scalable, mission-critical machine learning infrastructure for data ingestion and processing, model training, deployment, and monitoring.
The discussed architecture includes capabilities like scalable data preprocessing for training and predictions, a combination of different deep learning frameworks, data replication between data centers, intelligent real-time microservices running on Kubernetes, and local deployment of analytic models for offline predictions.
Learn how the public cloud allows extreme scale for building analytic models and how the Apache Kafka open source ecosystem enables building a cloud-independent infrastructure for preprocessing and ingestion of data and inference and monitoring of analytic models in real time
Understand why hybrid architectures and local model deployment are key for success in many scenarios and why you need a flexible machine learning architecture that supports different technologies and frameworks
Supply Chain Optimization with Apache KafkaKai Wähner
Supply Chain optimization leveraging Event Streaming with Apache Kafka. See real-world use cases and architectures from Walmart, BMW, Porsche, and other enterprises to improve the Supply Chain Management (SCM) processes. Automation, robustness, flexibility, real-time, decoupling, data integration, and hybrid deployments...
Video recording: https://youtu.be/dUkgungBmPs
Blog post: https://www.kai-waehner.de/apache-kafka-supply-chain-management-scm-optimization-scor-six-sigma-real-time
Apache Kafka vs. Cloud-native iPaaS Integration Platform MiddlewareKai Wähner
Enterprise integration is more challenging than ever before. The IT evolution requires the integration of more and more technologies. Applications are deployed across the edge, hybrid, and multi-cloud architectures. Traditional middleware such as MQ, ETL, ESB does not scale well enough or only processes data in batch instead of real-time.
This presentation explores why Apache Kafka is the new black for integration projects, how Kafka fits into the discussion around cloud-native iPaaS (Integration Platform as a Service) solutions, and why event streaming is a new software category.
A concrete real-world example shows the difference between event streaming and traditional integration platforms respectively cloud-native iPaaS.
Video Recording of this presentation:
https://www.youtube.com/watch?v=I8yZwKg_IJc&t=2842s
Blog post about this topic:
https://www.kai-waehner.de/blog/2021/11/03/apache-kafka-cloud-native-ipaas-versus-mq-etl-esb-middleware/
Apache Kafka, Tiered Storage and TensorFlow for Streaming Machine Learning wi...Kai Wähner
Don’t underestimate the Hidden Technical Debt in Machine Learning Systems.
Leverage Apache Kafka’s open ecosystem as a scalable and flexible Event Streaming Platform to build one pipeline for real-time and batch use cases.
Use Streaming Machine Learning with Apache Kafka, Tiered Storage, and TensorFlow IO to simplify your big data architecture.
Tiered Storage for Kafka provides:
- one platform for all data processing
- an event-based source of truth for materialized views
- no need for a pipeline between Kafka and a Data Lake like Hadoop
Benefits:
- cost reduction
- long-term backup
- performance isolation (real-time and historical analysis in the same cluster)
Use Cases for Reprocessing Historical Events:
- New consumer application
- Error-handling
- Compliance / regulatory processing
- Query and analyze existing events
- Model training
Apache Kafka, Tiered Storage and TensorFlow for Streaming Machine Learning wi...confluent
Machine Learning (ML) is separated into model training and model inference. ML frameworks typically use a data lake like HDFS or S3 to process historical data and train analytic models. But it’s possible to completely avoid such a data store, using a modern streaming architecture.
This talk compares a modern streaming architecture to traditional batch and big data alternatives and explains benefits like the simplified architecture, the ability of reprocessing events in the same order for training different models, and the possibility to build a scalable, mission-critical ML architecture for real time predictions with muss less headaches and problems.
The talk explains how this can be achieved leveraging Apache Kafka, Tiered Storage and TensorFlow.
Connected Vehicles and V2X with Apache KafkaKai Wähner
This session discusses uses cases leveraging Apache Kafka open source ecosystem as streaming platform to process IoT data.
See use cases, architectural alternatives and a live demo of how devices connect to Kafka via MQTT. Learn how to analyze the IoT data either natively on Kafka with Kafka Streams/KSQL, or on an external big data cluster like Spark, Flink or Elastic leveraging Kafka Connect, and how to leverage TensorFlow for Machine Learning.
The focus is on connected cars / connected vehicles and V2X use cases respectively mobility services.
A live demo shows how to build a cloud-native IoT infrastructure on Kubernetes to connect and process streaming data in real-time from 100.000 cars to do predictive maintenance at scale in real-time.
Code for the live demo on Github:
https://github.com/kaiwaehner/hivemq-mqtt-tensorflow-kafka-realtime-iot-machine-learning-training-inference
Lesfurest.com invited me to talk about the KAPPA Architecture style during a BBL.
Kappa architecture is a style for real-time processing of large volumes of data, combining stream processing, storage, and serving layers into a single pipeline. It's different from the Lambda architecture, uses separate batch and stream processing pipelines.
Event streaming: A paradigm shift in enterprise software architectureSina Sojoodi
This talk helps developers and architects understand the benefits, opportunities and challenges in moving from traditional point-to-point integration in application architecture to one with event streaming. Apache Kafka and Spring provide a solid foundation for enterprise and large organizations to implement event streaming solutions. Examples and common patterns are covered
towards the end.
Many thanks to James Watters and all the original content authors, editors and aggregators referenced in the slides.
Serverless Kafka and Spark in a Multi-Cloud Lakehouse ArchitectureKai Wähner
Apache Kafka in conjunction with Apache Spark became the de facto standard for processing and analyzing data. Both frameworks are open, flexible, and scalable.
Unfortunately, the latter makes operations a challenge for many teams. Ideally, teams can use serverless SaaS offerings to focus on business logic. However, hybrid and multi-cloud scenarios require a cloud-native platform that provides automated and elastic tooling to reduce the operations burden.
This session explores different architectures to build serverless Apache Kafka and Apache Spark multi-cloud architectures across regions and continents.
We start from the analytics perspective of a data lake and explore its relation to a fully integrated data streaming layer with Kafka to build a modern data Data Lakehouse.
Real-world use cases show the joint value and explore the benefit of the "delta lake" integration.
How to Leverage the Apache Kafka Ecosystem to Productionize Machine Learning ...Codemotion
This talk shows how to productionize Machine Learning models in mission-critical and scalable real time applications by leveraging Apache Kafka as streaming platform. The talk discusses the relation between Machine Learning frameworks such as TensorFlow, DeepLearning4J or H2O and the Apache Kafka ecosystem. A live demo shows how to build a Machine Learning environment leveraging different Kafka components: Kafka messaging and Kafka Connect for data movement, Kafka Streams for model deployment and inference in real time, and KSQL for real time analytics of predictions, accuracy and alerts.
Deep Learning at Extreme Scale (in the Cloud) with the Apache Kafka Open Sou...Kai Wähner
How to Build a Machine Learning Infrastructure with Kafka, Connect, Streams, KSQL, etc…
This talk shows how to build Machine Learning models at extreme scale and how to productionize the built models in mission-critical real time applications by leveraging open source components in the public cloud. The session discusses the relation between TensorFlow and the Apache Kafka ecosystem - and why this is a great fit for machine learning at extreme scale.
The Machine Learning architecture includes: Kafka Connect for continuous high volume data ingestion into the public cloud, TensorFlow leveraging Deep Learning algorithms to build an analytic model on powerful GPUs, Kafka Streams for model deployment and inference in real time, and KSQL for real time analytics of predictions, alerts and model accuracy.
Sensor analytics for predictive alerting in real time is used as real world example from Internet of Things scenarios. A live demo shows the out-of-the-box integration and dynamic scalability of these components on Google Cloud.
Key takeaways for the audience
• Learn how to build a Machine Learning infrastructure at extreme scale and how to productionize the built models in mission-critical real time applications
• Understand the benefits of a machine learning platform on the public cloud
• Learn about an extreme scale Machine Learning architecture around the Apache Kafka open source ecosystem including Kafka Connect, Kafka Streams and KSQL
• See a live demo for an Internet of Things use case: Sensor analytics for predictive alerting in real time
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).
Event-Driven Stream Processing and Model Deployment with Apache Kafka, Kafka ...Kai Wähner
Talk from Kafka Summit San Francisco 2019 (https://kafka-summit.org/sessions/event-driven-model-serving-stream-processing-vs-rpc-kafka-tensorflow/). Video recording will be available for free on the Summit website.
Event-based stream processing is a modern paradigm to continuously process incoming data feeds, e.g. for IoT sensor analytics, payment and fraud detection, or logistics. Machine Learning / Deep Learning models can be leveraged in different ways to do predictions and improve the business processes. Either analytic models are deployed natively in the application or they are hosted in a remote model server. In the latter you combine stream processing with RPC / Request-Response paradigm instead of direct doing direct inference within the application. This talk discusses the pros and cons of both approaches and shows examples of stream processing vs. RPC model serving using Kubernetes, Apache Kafka, Kafka Streams, gRPC and TensorFlow Serving. The trade-offs of using a public cloud service like AWS or GCP for model deployment are also discussed and compared to local hosting for offline predictions directly “at the edge”.
Key takeaways
• Machine Learning / Deep Learning models can be used in different ways to do predictions. Scalability and loose coupling are important success factors
• Stream processing vs. RPC / Request-Response for model serving has many trade-offs – learn about alternatives and best practices for your different scenarios
• Understand the alternatives and trade-offs of model deployment in modern infrastructures like Kubernetes or Cloud Services like AWS or GCP
• See live demos with Java, gRPC, Apache Kafka, KSQL and TensorFlow Serving to understand the trade-offs
Event-Driven Model Serving: Stream Processing vs. RPC with Kafka and TensorFl...confluent
Event-based stream processing is a modern paradigm to continuously process incoming data feeds, e.g. for IoT sensor analytics, payment and fraud detection, or logistics. Machine Learning / Deep Learning models can be leveraged in different ways to do predictions and improve the business processes. Either analytic models are deployed natively in the application or they are hosted in a remote model server. In the latter you combine stream processing with RPC / Request-Response paradigm instead of direct doing direct inference within the application. This talk discusses the pros and cons of both approaches and shows examples of stream processing vs. RPC model serving using Kubernetes, Apache Kafka, Kafka Streams, gRPC and TensorFlow Serving. The trade-offs of using a public cloud service like AWS or GCP for model deployment are also discussed and compared to local hosting for offline predictions directly "at the edge".
Data scientists and data engineers love Python for transforming, filtering, and processing data to train and deploy analytic models with frameworks such as TensorFlow. However, in real-world deployments, all of these steps require a scalable and reliable infrastructure. This session shows how data experts can use Python for data processing and model inference at scale, leveraging Python, Jupyter, Apache Kafka, and KSQL.
Talk from Oracle Code One / Oracle World 2019 in San Francisco.
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.
Real time Messages at Scale with Apache Kafka and CouchbaseWill Gardella
Kafka is a scalable, distributed publish subscribe messaging system that's used as a data transmission backbone in many data intensive digital businesses. Couchbase Server is a scalable, flexible document database that's fast, agile, and elastic. Because they both appeal to the same type of customers, Couchbase and Kafka are often used together.
This presentation from a meetup in Mountain View describes Kafka's design and why people use it, Couchbase Server and its uses, and the use cases for both together. Also covered is a description and demo of Couchbase Server writing documents to a Kafka topic and consuming messages from a Kafka topic. using the Couchbase Kafka Connector.
Similar to Mainframe Integration, Offloading and Replacement with Apache Kafka | Kai Waehner, Confluent (20)
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...HostedbyConfluent
"In this talk, attendees will be provided with an introduction to Kafka Connect and the basics of Single Message Transforms (SMTs) and how they can be used to transform data streams in a simple and efficient way. SMTs are a powerful feature of Kafka Connect that allow custom logic to be applied to individual messages as they pass through the data pipeline. The session will explain how SMTs work, the types of transformations they can be used for, and how they can be applied in a modular and composable way.
Further, the session will discuss where SMTs fit in with Kafka Connect and when they should be used. Examples will be provided of how SMTs can be used to solve common data integration challenges, such as data enrichment, filtering, and restructuring. Attendees will also learn about the limitations of SMTs and when it might be more appropriate to use other tools or frameworks.
Additionally, an overview of the alternatives to SMTs, such as Kafka Streams and KSQL, will be provided. This will help attendees make an informed decision about which approach is best for their specific use case.
Whether attendees are developers, data engineers, or data scientists, this talk will provide valuable insights into how Kafka Connect and SMTs can help streamline data processing workflows. Attendees will come away with a better understanding of how these tools work and how they can be used to solve common data integration challenges."
"While Apache Kafka lacks native support for topic renaming, there are scenarios where renaming topics becomes necessary. This presentation will delve into the utilization of MirrorMaker 2.0 as a solution for renaming Kafka topics. It will illustrate how MirrorMaker 2.0 can efficiently facilitate the migration of messages from the old topic to the new one and how Kafka Connect Metrics can be employed to monitor the mirroring progress. The discussion will encompass the complexity of renaming Kafka topics, addressing certain limitations, and exploring potential workarounds when using MirrorMaker 2.0 for this purpose. Despite not being originally designed for topic renaming, MirrorMaker 2.0 has a suitable solution for renaming Kafka topics.
Blog Post : https://engineering.hellofresh.com/renaming-a-kafka-topic-d6ff3aaf3f03"
Evolution of NRT Data Ingestion Pipeline at TrendyolHostedbyConfluent
"Trendyol, Turkey's leading e-commerce company, is committed to positively impacting the lives of millions of customers. Our decision-making processes are entirely driven by data. As a data warehouse team, our primary goal is to provide accurate and up-to-date data, enabling the extraction of valuable business insights.
We utilize the benefits provided by Kafka and Kafka Connect to facilitate the transfer of data from the source to our analytical environment. We recently transitioned our Kafka Connect clusters from on-premise VMs to Kubernetes. This shift was driven by our desire to effectively manage rapid growth(marked by a growing number of producers, consumers, and daily messages), ensuring proper monitoring and consistency. Consistency is crucial, especially in instances where we employ Single Message Transforms to manipulate records like filtering based on their keys or converting a JSON Object into a JSON string.
Monitoring our cluster's health is key and we achieve this through Grafana dashboards and alerts generated through kube-state-metrics. Additionally, Kafka Connect's JMX metrics, coupled with NewRelic, are employed for comprehensive monitoring.
The session will aim to explain our approach to NRT data ingestion, outlining the role of Kafka and Kafka Connect, our transition journey to K8s, and methods employed to monitor the health of our clusters."
Ensuring Kafka Service Resilience: A Dive into Health-Checking TechniquesHostedbyConfluent
"Join our lightning talk to delve into the strategies vital for maintaining a resilient Kafka service.
While proactive monitoring is key for issue prevention, failures will still occur. Rapid detection tools will enable you to identify and resolve problems before they impact end-users. This session explores the techniques employed by Kafka cloud providers for this detection, many of which are also applicable if you are managing independent Kafka clusters or applications.
The talk focuses on health-checking, a powerful tool that encompasses an application and its monitoring to validate Kafka environment availability. The session navigates through Kafka health-check methods, sharing best practices, identifying common pitfalls, and highlighting the monitoring of critical performance metrics like throughput and latency for early issue detection.
Attendees will gain valuable insights into the art of health-checking their Kafka environment, equipping them with the tools to identify and address issues before they escalate into critical problems. We invite all Kafka enthusiasts to join us in this talk to foster a deeper understanding of Kafka health-checking and ensure the continued smooth operation of your Kafka environment."
Exactly-once Stream Processing with Arroyo and KafkaHostedbyConfluent
"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."
"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."
What is tiered storage and what is it good for? After this session you will know how to leverage the tiered storage feature to enable longer retention than the storage attached to brokers allows. You will get acquainted with the different configuration options and know what to expect when you enable the feature, like for example when will the first upload to the remote object storage take place.
Building a Self-Service Stream Processing Portal: How And WhyHostedbyConfluent
"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"
From the Trenches: Improving Kafka Connect Source Connector Ingestion from 7 ...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."
Future with Zero Down-Time: End-to-end Resiliency with Chaos Engineering and ...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."
Navigating Private Network Connectivity Options for Kafka ClustersHostedbyConfluent
"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."
Apache Flink: Building a Company-wide Self-service Streaming Data PlatformHostedbyConfluent
"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"
Explaining How Real-Time GenAI Works in a Noisy PubHostedbyConfluent
"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"
"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."
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.
Mastering Kafka Producer Configs: A Guide to Optimizing PerformanceHostedbyConfluent
"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."
Data Contracts Management: Schema Registry and BeyondHostedbyConfluent
"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."
"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."
Debezium vs. the World: An Overview of the CDC EcosystemHostedbyConfluent
"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/"
Beyond Tiered Storage: Serverless Kafka with No Local DisksHostedbyConfluent
"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."
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
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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.
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.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
Mainframe Integration, Offloading and Replacement with Apache Kafka | Kai Waehner, Confluent
1. Mainframe Integration, Offloading and Replacement
with Apache Kafka
Stand up to the Dinosaur!
Kai Waehner
Field CTO
contact@kai-waehner.de
linkedin.com/in/kaiwaehner
@KaiWaehner
confluent.io
kai-waehner.de
2. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
1. Mainframe Status Quo and Challenges
2. Use Cases for Data in Mo:on
3. Apache Ka@a as Cloud-na:ve and Hybrid Infrastructure
4. Mainframe Integra:on, Offloading and Replacement
5. Case Study
Agenda
3. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
1. Mainframe Status Quo and Challenges
2. Use Cases for Data in Motion
3. Apache Kafka as Cloud-native and Hybrid Infrastructure
4. Mainframe Integration, Offloading and Replacement
5. Case Study
Agenda
4. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
The Mainframe is here to stay!
“Mainframes are still hard at work,
processing over 70 percent of the world’s
most important computing transactions
every day. Organizations like banks, credit
card companies, airlines, medical facilities,
insurance companies, and others that can
absolutely not afford downtime and errors
depend on the mainframe to get the job
done. Nearly three-quarters of all Fortune
500 companies still turn to the mainframe to
get the critical processing work completed”
https://www.bmc.com/blogs/mainframe-mips-an-introduction/
5. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
What is a Mainframe?
Modern mainframe design is characterized less by raw
computa8onal speed and more by:
• High reliability and security
• Extensive input-output ("I/O") facili8es with the ability to
offload to separate engines
• Strict backward compa8bility with older soGware
• High hardware and computa8onal u8liza8on rates through
virtualiza8on to support massive throughput
• Hot-swapping of hardware, such as processors and memory
Vendors: “IBM and the Seven Dwarfs”
The IBM z15, announced in 2019,
with up to 40TB RAM and 190 Cores,
typically costs millions $$$
(variable software costs not included)
6. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Neobanks and FinTechs Hunting the Traditional Banks
Monolithic
Proprietary
Complex
Inflexible
7. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Not just the FinTechs Modernize their Architecture!
9
https://www.confluent.io/kafka-summit-london18/distributing-computing-key-player-in-corebanking-platforms/
Sberbank re-implemented their Core Banking Pla6orm
around Ka9a and Event Streaming to be ready for the future!
8. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
MIPS (million instructions per second)
to normalize CPU usage across CPU types and models or hardware configs
MSU (million service units)
hardware and software metrics calculated directly by the operating system
12
9. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
No Cobol developers out there!
13
https://edition.cnn.com/2020/04/08/business/coronavirus-cobol-programmers-new-jersey-trnd/index.html
10. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Huge demand to build an open, flexible, scalable platform
• Real-&me
• Scalability
• High availability
• Decoupling
• Cost-efficient
• Flexibility
• Elas&city
• Standards-based
• Extensibility
• Security
• Infrastructure-independent
• Mul&-region / global
11. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
1. Mainframe Status Quo and Challenges
2. Use Cases for Data in Motion
3. Apache Kafka as Cloud-native and Hybrid Infrastructure
4. Mainframe Integration, Offloading and Replacement
5. Case Study
Agenda
12. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Data in Motion in Financial Services
Check past Kafka Summit videos for details about the use cases:
https://kafka-summit.org/past-events/
13. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Data in Motion for
Traditional and New Innovative Use Cases
in Financial Services
Real Time Processing Digital Transforma3on Strategic Goals
Short-Sale Risk Calculation
/ Trade Approval
Mainframe Offloading and
Replacement
Credit Card
Fraud Detection
Next-Best
Offer
Robotic Process Automation
(e.g. Know Your Customer, KYC)
Customer Service
(e.g. Chat Bots)
IT ModernizaNon
Regulatory
Reporting
Account Login
Fraud Detection
Anomaly Detection
across Assets and Locations
Derivatives
Pricing Compliance
Trading
Post-Processing
Strategic Planning
and Simulations
14. Mainframe Integra?on, Offloading and Replacement with Apache KaAa – @KaiWaehner - www.kai-waehner.de
https://www.confluent.io/customers/rbc/
“… rescue data off of the mainframe, in a cloud native, microservice-
based fashion … [to] … significantly reduce the reads on the mainframe,
saving RBC fixed infrastructure costs (OPEX). RBC stayed compliant with
bank regulations and business logic, and is now able to create new
applications using the same event-based architecture.”
15. Mainframe Integra?on, Offloading and Replacement with Apache KaAa – @KaiWaehner - www.kai-waehner.de
1. Mainframe Status Quo and Challenges
2. Use Cases for Data in Motion
3. Apache Kafka as Cloud-native and Hybrid Infrastructure
4. Mainframe Integration, Offloading and Replacement
5. Case Study
Agenda
16. Mainframe Integra?on, Offloading and Replacement with Apache KaAa – @KaiWaehner - www.kai-waehner.de
STREAM
PROCESSING
Create and store
materialized views
Filter
Analyze in-flight
Time
C C
C
Data in Motion
17. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
MES
ERP
Sensors
Mobile
Customer 360
Real-time
Alerting System
Data
warehouse
Producers
Consumers
Streams and storage of real time events
Stream
processing
apps
Connectors
Connectors
Stream
processing
apps
Supplier
Alert
Forecast
Inventory Customer
Order
23
An Event Streaming Platform
is the Underpinning of an Event-driven Architecture
19. Mainframe Integra?on, Offloading and Replacement with Apache KaAa – @KaiWaehner - www.kai-waehner.de
Disaster Recovery – RPO and RTO
RPO = Recovery Point Objective
RTO = Recovery Time Objective
20. Mainframe Integra?on, Offloading and Replacement with Apache KaAa – @KaiWaehner - www.kai-waehner.de
IBM GDPS
● Synchronous and asynchronous mirroring
● Different RTO / RPO setups
● Built for resiliency and disaster recovery
● Supports multiple sites
● Uses concepts like XCF (Cross-system
Coupling Facility), Parallel Sysplex, Disk
Mirroring, HyperSwap, etc.
● Independent of transaction manager (e.g.
CICS, IMS) or database manager (e.g. DB2 ,
IMS, VSAM)
https://www.ibm.com/it-infrastructure/z/technologies/gdps
https://ibmsystemsmag.com/IBM-Z/07/2019/resiliency-gdps-solutions
21. Mainframe Integra?on, Offloading and Replacement with Apache KaAa – @KaiWaehner - www.kai-waehner.de
Global Event Streaming with Kafka
Aggregate Small Footprint
Edge Deployments with
Replication (Aggregation)
Simplify Disaster Recovery
Operations with
Multi-Region Clusters
with RPO=0 and RTO~0
Stream Data Globally with
Replication and Cluster Linking
22. Mainframe Integra?on, Offloading and Replacement with Apache KaAa – @KaiWaehner - www.kai-waehner.de
Multi-Region Cluster
(Only available in Confluent Platform)
Kafka Clusters
can Stretch over Regions
Zero Downtime + Zero Data loss
(RPO = 0 and RTO ~ 0)
e.g. Stretched over US East + Mid + West
Automate Disaster Recovery
Sync or Async Replication per Topic
Offset Preserving
Automated Client Failover without Custom Code
23. Mainframe Integra?on, Offloading and Replacement with Apache KaAa – @KaiWaehner - www.kai-waehner.de
Example of a Multi-Region Cluster in a Bank
Large FinServ Customer
Payment
Log
Payment
Log
Location Loca-on
synchronous
asynchronous
● ‘Payment’ transactions enter from us-
east and us-west with fully
synchronous replication
● ‘Log’ and ‘Location’ information in the
same cluster use async - optimized for
latency
● Automated disaster recovery (zero
downtime, zero data loss)
Result: Clearing time from ‘deposit’ to
‘available’ goes from 5 days to 5 seconds
(including security checks)
24. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
1. Mainframe Status Quo and Challenges
2. Use Cases for Data in Motion
3. Apache Kafka as Cloud-native and Hybrid Infrastructure
4. Mainframe Integration, Offloading and Replacement
5. Case Study
Agenda
25. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Mainframe Offloading
Database
change
Microservices
events
SaaS
data
Customer
experiences
Streams of real time events
Legacy
App
Modern
App 1
Complex business logic
Push changes once
Write
Write
continuously
Read
continuously
Modern
App 2
Write
continuously
Read
continuously
MIPS / MSU
MIPS / MSU
MIPS / MSU
Read
No MIPS / MSU
26. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Mainframe Replacement
Database
change
Microservices
events
SaaS
data
Customer
experiences
Streams of real time events
Legacy
App
Modern
App 1
Complex business logic
Push changes once
Write
Write
continuously
Read
continuously
Modern
App 2
Write
continuously
Read
continuously
MIPS / MSU
MIPS / MSU
MIPS / MSU
Read
No MIPS / MSU
27. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Mainframe Replacement (without Writing New Apps)
https://learnworthy.net/could-java-be-the-next-cobol/
https://medium.com/@FranzRoses/the-enterprise-journey-to-decompose-the-cobol-banking-core-into-java-the-developer-perspective-2e8a53bb528e
https://bs2manuals.ts.fujitsu.com/psBEANCONNECTV65en/beanconnect-user-guide-user-guide-13821/cobol2java-bc-ug-499/mapping-cobol-data-types-to-java-classes-bc-ug-500
What about
the 5% of
(complex) code
that cannot
be migrated
automatically?
Showstopper?
28. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Orders Customers
Payments
Stock
WebSockets / SSE
JMS
ESB
REST
Java
Connect
RPC
MQ
Integration between Kafka and Mainframes
MQ
C++ File
???
29. Mainframe Integra?on, Offloading and Replacement with Apache KaAa – @KaiWaehner - www.kai-waehner.de
Integration Options between Kafka and Mainframe
• IBM InfoSphere Data Replication (IIDR) Change Data Capture (CDC) solution for Mainframe
• Open-source CDC solution (e.g. Debezium) – but you still need an IIDR license, this is the same challenge as with Oracle
and GoldenGate CDC)
• 3rd Party middleware solution (e.g. DBS-H, B.O.S. Software, …) – some provide much more than just CDC for mainframe
integration
• DB2 SQL Integration: Create interface tables + Kafka Connect + JDBC connector
• IBM MQ interface + Kafka Connect’s IBM MQ connector
• VSAM File Integration / CDC (e.g. B.O.S, Luminex, Syncsort/Precisely, Qlik Replicate, IBM InfoSphere CDC, …)
• Confluent REST Proxy, HTTP Sink connector and HTTP(S) communication with the mainframe
• Kafka Client APIs on the mainframe
Evaluate the performance / scalability / feature set / cost
of the tools and the footprint on the mainframe!
Don’t underestimate politics…
30. Mainframe Integra?on, Offloading and Replacement with Apache KaAa – @KaiWaehner - www.kai-waehner.de
Change Data Capture
Transaction log-based CDC pushes data changes (insert, update, delete) from the
mainframe database to Kafka in real time.
+ Real time push updates to Kafka
+ Eliminate disruptive full loads, i.e. minimize production impact
+ Reduce MIPS consumption
+ Full support
+ IIDR or 3rd Party tools
- Typically very high licensing costs
- Referential integrity between related tables
31. Mainframe Integra?on, Offloading and Replacement with Apache KaAa – @KaiWaehner - www.kai-waehner.de
Kafka Connect
Integration of Kafka and Mainframe Applications via IBM DB2 or IBM MQ
Mainframe
42
CICS
Batch I/O
Requests
CICS
Program
Failure
Flow
CICS
Program
Cloud
On-Prem
JDBC
Connector
z/OS
MQ Connect Worker
running on zIIP
32. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
44
HTTP(S)
Mainframe
Cobol or other ApplicaNon Code
Confluent Cluster
REST API
Client App with
HTTP Kafka Producer /
HTTP Kafka Consumer
HTTP(S)
REST API for Kafka
Workaround for the Integration of Kafka and Mainframe Applications
33. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
1. Mainframe Status Quo and Challenges
2. Use Cases for Data in Motion
3. Apache Kafka as Cloud-native and Hybrid Infrastructure
4. Mainframe Integration, Offloading and Replacement
5. Case Study
Agenda
34. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Cloud
Adop4on
Journey from Mainframe
to Hybrid and Cloud
PHASE 3
Hybrid
Cloud
Cloud-First
Development
PHASE 2
PHASE 1
https://www.accenture.com/_acnmedia/pdf-70/accenture-moving-to-the-cloud-strategy-for-banks-in-north-america.pdf
Case Study - Bank CEO
“This is the last 5-year $20M IBM contract.
Get rid off the mainframe!”
35. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Strangler Design Pattern
https://paulhammant.com/2013/07/14/legacy-application-strangulation-case-studies/
https://martinfowler.com/bliki/StranglerFigApplication.html
“The most important
reason to consider a
strangler fig application
over a cut-over rewrite
is reduced risk.”
Martin Fowler
36. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Year 0: Direct Communication between Mainframe and App
Application
1) Direct Legacy Mainframe Communication to App
Date Amount
1/27/2017 $4.56
1/22/2017 $32.14
Core Banking ‘1970’
(Mainframe)
37. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Year 1: Kafka for Decoupling between Mainframe and App
Application
1) Direct Legacy Mainframe Communication to App
2) Kafka for Decoupling between Mainframe and App
Date Amount
1/27/2017 $4.56
1/22/2017 $32.14
Core Banking ‘1970’
(Mainframe)
Mainframe Integration
- Change Data Capture (IIDR)
- Kafka Connect (JMS, MQ, JDBC)
- REST Proxy
- Kafka Client
- 3rd Party CDC Tool
38. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Year 2 to 4: New Projects and Applications
Applica3on
Microservices
Agile, Lightweight
(but Scalable, Robust)
Applications
Big Data Project (Elastic,
Spark,
AWS Services, …)
1) Direct Legacy Mainframe Communication to App
2) Kafka for Decoupling between Mainframe and App
3) New Projects and Applications
External
Solution
Date Amount
1/27/2017 $4.56
1/22/2017 $32.14
Core Banking ‘1970’
(Mainframe)
Mainframe Integration
- Change Data Capture (IIDR)
- Kafka Connect (JMS, MQ, JDBC)
- REST Proxy
- Kafka Client
- 3rd Party CDC Tool
39. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Year 5: Mainframe Replacement
Application
Microservices
Agile, Lightweight
(but Scalable, Robust)
Applications
Big Data Project (Elastic,
Spark,
AWS Services, …)
1) Direct Legacy Mainframe Communication to App
2) Kafka for Decoupling between Mainframe and App
3) New Projects and Applications
4) Mainframe Replacement
External
Solution
Core Banking ‘2020’
(Modern Technology)
Date Amount
1/27/2017 $4.56
1/22/2017 $32.14
41. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
IBM Mainframe Database and Transaction Managers
54
IMS
• Hierarchical Database
• Transaction Manager
• Supports Cobol, Assembler, PL/1, Java
• IMS Connect for Integration with WebSphere MQ, SOAP, …
DB2
• Relational Database
CICS
• Transaction Manager
• Database “Lite” (VSAM Datasets)
• Integration and Application Programming Capabilities similar to IMS,
but much easier to use
• Advanced Features like Transaction Prioritization
The Heart
of your
Business
App
42. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
“Transactions” in Apache Kafka
55
Exactly-Once Semantics (EOS)
available since Kafka 0.11:
https://cwiki.apache.org/confluence/display/KAFKA/KIP-98+-+Exactly+Once+Delivery+and+Transactional+Messaging
https://www.confluent.io/kafka-summit-london18/dont-repeat-yourself-introducing-exactly-once-semantics-in-apache-kafka/
44. I N V E S T M E N T & T I M E
V
A
L
U
E
3
4
5
1
2
Event Streaming Maturity Model
Initial Awareness /
Pilot (1 Kafka
Cluster)
Start to Build
Pipeline / Deliver 1
New Outcome
(1 Kafka Cluster)
Mission-Critical
Deployment
(Stretched, Hybrid,
Multi-Region)
Build Contextual
Event-Driven Apps
(Stretched, Hybrid,
Multi-Region)
Central Nervous
System
(Global Kafka)
Product, Support, Training, Partners, Technical Account Management...
60
45. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
The Rise of Data in Motion
2010
Apache Kafka
created at LinkedIn by
Confluent founders
2014
2020
80%
Fortune 100
Companies
trust and use
Apache Kafka
61
46. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Car Engine Car Self-driving Car
Confluent Completes Apache Kafka
47. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
IBM + Confluent for Mainframe Integration
64
48. Mainframe Integration, Offloading and Replacement with Apache Kafka – @KaiWaehner - www.kai-waehner.de
Kai Waehner
Field CTO
contact@kai-waehner.de
@KaiWaehner
kai-waehner.de
confluent.io
linkedin.com/in/kaiwaehner
Questions? Feedback?
Let’s connect!