5 lessons learned for successful migration to Confluent cloud | Natan Silinitsky, wix.com
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Confluent Cloud makes Devops engineers lives a lot more easier. Yet moving 1500 microservices, 10K topics and 100K partitions to a multi-cluster Confluent cloud can be a challenge. In this talk you will hear about 5 lessons that Wix has learned in order to successfully meet this challenge. These lessons include: 1. Automation, Automation, Automation - all the process has to be completely automated at such scale 2. Prefer a gradual approach - E.g. migrate topics in small chunks and not all at once. Reduces risks if things go bad 3. Cleanup first - avoid migrating unused topics or topics with too many unnecessary partitions
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5 lessons learned for Successful Migration to Confluent Cloud
Confluent Cloud makes Devops engineers lives a lot more easier.
Yet moving 1500 microservices, 10K topics and 100K partitions to a multi-cluster Confluent cloud can be a challenge.
In this talk you will hear about 5 lessons that Wix has learned in order to successfully meet this challenge.
These lessons include:
1. Automation, Automation, Automation - all the process has to be completely automated at such scale
2. Prefer a gradual approach - E.g. migrate topics in small chunks and not all at once. Reduces risks if things go bad
3. First migrate test topics with relayed real traffic - So data will be real but will not effect production.
4. Cleanup first - avoid migrating unused topics or topics with too many unnecessary partitions
5. Adapt to Confluent Cloud APIs - e.g. lag monitoring
Devoxx UK - Migrating to Multi Cluster Managed Kafka
As Wix Kafka usage grew to 2.5B messages per day, >20K topics and >100K leader partitions serving 2000 microservices,
we decided to migrate from self-operated single cluster per data-center to a managed cloud service (Like Amazon MSK or Confluent Cloud) with a multi-cluster setup.
The classic approach would be to perform this transition when all incoming traffic is removed from the data center.
But draining an entire data-center for an undetermined period of time, until all 2000 services complete the switch was too risky for us.
This talk is about how we gradually migrated all of our Kafka consumers and producers with 0 downtime while they continued to handle regular traffic. You will learn practical steps you can take to greatly reduce the risks and speed up the migration timeline.
Migrating to Multi Cluster Managed Kafka - Conf42 - CloudNative
As Wix Kafka usage grew to 2.5B messages per day, >20K topics and >100K leader partitions serving 2000 microservices,
we decided to migrate from self-operated single cluster per data-center to a managed cloud service (Like Amazon MSK or Confluent Cloud) with a multi-cluster setup.
The classic approach would be to perform this transition when all incoming traffic is removed from the data center.
But draining an entire data-center for an undetermined period of time, until all 2000 services complete the switch was too risky for us.
This talk is about how we gradually migrated all of our Kafka consumers and producers with 0 downtime while they continued to handle regular traffic. You will learn practical steps you can take to greatly reduce the risks and speed up the migration timeline.
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The auto industry is midst a data revolution that is transforming how companies do business. Once a scarce resource, data has now become abundant and cheap. What are the new technologies that change the way we produce, collect, process, store, and analyze data. What new streams of data are being created with Industry 4.0 and the Internet of Things on the horizon, is there significant value to taking a strategic approach to Fast Data. How is Porsche building the next level Data Streaming Platform with open source technologies and how we are using CI/CD pipelines amongst others in order to serve our use cases.
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The Apache Kafka ecosystem is very rich with components and pieces that make for designing and implementing secure, efficient, fault-tolerant and scalable event stream processing (ESP) systems. Using real-world examples, this talk covers why Apache Kafka is an excellent choice for cloud-native and hybrid architectures, how to go about designing, implementing and maintaining ESP systems, best practices and patterns for migrating to the cloud or hybrid configurations, when to go with PaaS or IaaS, what options are available for running Kafka in cloud or hybrid environments and what you need to build and maintain successful ESP systems that are secure, performant, reliable, highly-available and scalable.
Confluent On Azure: Why you should add Confluent to your Azure toolkit | Alic...
As a data professional, you are the glue that makes cross-platform integrations possible. With the increase in adoption of hybrid cloud architectures, Kafka is an increasingly relevant tool for building data pipelines between platforms and accelerating delivery on cloud projects. Early exposure to Kafka on Azure capabilities gives you an edge to build better mousetraps at the design phase.
Customers already running Kafka on premises and are looking to extend Kafka systems to Azure can get started quickly with Confluent Cloud. Additionally, DevOps for self-managed options can be easily scalable with Ansible for Virtual Machines or containers via Azure Kubernetes Services or Azure Container Instances.
This session is presented from the Microsoft Solution Architect perspective by Israel Ekpo, Microsoft Cloud Solution Architect and Alicia Moniz, Microsoft MVP. They will cover use cases and scenarios, along with key Azure integration points and architecture patterns.
Building Streaming Data Pipelines with Google Cloud Dataflow and Confluent Cl...
We will demonstrate how easy it is to use Confluent Cloud as the data source of your Beam pipelines. You will learn how to process the information that comes from Confluent Cloud in real time, make transformations on such information and feed it back to your Kafka topics and other parts of your architecture.
A Look into the Mirror: Patterns and Best Practices for MirrorMaker2 | Cliff ...
From migrations between Apache Kafka clusters to multi-region deployments across datacenters, the introduction of MirrorMaker2 has expanded the possibilities for Apache Kafka deployments and use cases. In this session you will learn about patterns, best practices, and learnings compiled from running MirrorMaker2 in production at every scale.
Building Event Streaming Microservices with Spring Boot and Apache Kafka | Ja...
This document summarizes Jan Svoboda's presentation on building event streaming microservices with Spring Boot and Apache Kafka. Jan discusses his motivation for learning Kafka and microservices development. He outlines a typical journey from strangling monoliths to building event-driven architectures using Kafka. Jan explains how Spring and Kafka integrate well together. The remainder of the presentation demonstrates building a sample application using these techniques and dives into common architecture patterns like event sourcing, CQRS, scaling stateful services, and reverse proxy routing.
How Confluent Completes the Event Streaming Platform (Addison Huddy & Dan Ros...
Confluent Platform 6.0 and Project Metamorphosis complete the event streaming platform by providing elastic scalability, infinite storage, global access, and transforming Kafka. Key features include self-balancing clusters and dynamic scaling on Confluent Cloud, tiered storage and infinite retention on the platform, and cluster linking to simplify hybrid and multi-cloud deployments. These new capabilities help remove limitations on scale, storage, and deployment that traditionally challenged Kafka applications.
Kafka Excellence at Scale – Cloud, Kubernetes, Infrastructure as Code (Vik Wa...
Cloud is changing the world; Kubernetes is changing the world; real-time event streaming is changing the world. In this talk we explore some of best practices to synergistically combine the power of these paradigm shifts to achieve a much greater return on your Kafka investments. From declarative deployments, zero-downtime upgrades, elastic scaling to self-healing and automated governance, learn how you can bring the next level of speed, agility, resilience, and security to your Kafka implementations.
Building a Modern, Scalable Cyber Intelligence Platform with Apache Kafka | J...
As cyber threats continuously grow in sophistication and frequency, companies need to quickly acclimate to effectively detect, respond, and protect their environments. At Intel, we’ve addressed this need by implementing a modern, scalable Cyber Intelligence Platform (CIP) based on Splunk and Apache Kafka. We believe that CIP positions us for the best defense against cyber threats well into the future.
Our CIP ingests tens of terabytes of data each day and transforms it into actionable insights through streams processing, context-smart applications, and advanced analytics techniques. Kafka serves as a massive data pipeline within the platform. It provides us the ability to operate on data in-stream, enabling us to reduce Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR). Faster detection and response ultimately leads to better prevention.
In our session, we’ll discuss the details described in the IT@Intel white paper that was published in Nov 2020 with same title.
Comparing three data ingestion approaches where Apache Kafka integrates with ...
Using Kafka to stream data into TigerGraph, a distributed graph database, is a common pattern in our customers’ data architecture. We have seen the integration in three different layers around TigerGraph’s data flow architecture, and many key use case areas such as customer 360, entity resolution, fraud detection, machine learning, and recommendation engine. Firstly, TigerGraph’s internal data ingestion architecture relies on Kafka as an internal component. Secondly, TigerGraph has a builtin Kafka Loader, which can connect directly with an external Kafka cluster for data streaming. Thirdly, users can use an external Kafka cluster to connect other cloud data sources to TigerGraph cloud database solutions through the built-in Kafka Loader feature. In this session, we will present the high-level architecture in three different approaches and demo the data streaming process.
Stream Processing with Kafka and KSQL in Jupiter | Namit Mahuvakar, Jupiter
Stream processing allows analyzing streaming data in real-time using tools like KSQL. KSQL builds on Kafka Streams to provide a SQL-like interface for transforming, filtering, and creating derived streams from immutable event data ingested into Kafka. Jupiter, a digital banking platform, uses KSQL to reconcile transactions from different interfaces in real-time and check rewards eligibility by analyzing consolidated transaction streams. While powerful, KSQL deployments require monitoring, richer SQL support, and integration with big data platforms for long-term storage.
Exposing and Controlling Kafka Event Streaming with Kong Konnect Enterprise |...
Event streaming allows companies to build more scalable and loosely coupled real-time applications supporting massive concurrency demands and simplifying the construction of services.
At the same time, API management provides capabilities to securely control the upstream services consumption, including the event processing infrastructure.
This session shows how Kong Konnect Enterprise can complement Kafka Event Streaming, exposing it to new and external consumers while applying specific and critical policies to control its consumption, including API key, OAuth/OIDC and others for authentication, rate limiting, caching, log processing, etc.
Elastically Scaling Kafka Using Confluent
This document discusses how Confluent Platform provides elastic scaling for Apache Kafka. It offers fully managed cloud services through Confluent Cloud or self-managed software. Confluent Cloud allows users to easily scale Kafka workloads from 0 MBps to GBps without complex provisioning. It also offers pay-for-use pricing where customers only pay for the data streamed, with the ability to scale to zero. For self-managed deployments, Confluent Platform enables dynamic scaling of Kafka clusters on Kubernetes through features like tiered storage and self-balancing clusters that can rebalance partitions in seconds versus hours for other Kafka services.
Kafka and Kafka Streams in the Global Schibsted Data Platform
This document discusses Schibsted's use of Kafka and Kafka Streams in its global data platform. It describes how Schibsted processes streaming data using Kafka Streams to build a new streaming pipeline called Yggdrasil that provides low latency and delivery guarantees. It also discusses how Kafka Connect is used to get data into and out of the platform and how third party analytics tools can connect. The document outlines some challenges experienced in scaling up the platform.
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Mainframe Integration, Offloading and Replacement with Apache Kafka | Kai Wae...
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Kafka in Context, Cloud, & Community (Simon Elliston Ball, Cloudera) Kafka Su...
0-330km/h: Porsche's Data Streaming Journey | Sridhar Mamella, Porsche
0-330km/h: Porsche's Data Streaming Journey | Sridhar Mamella, Porsche
Cloud-Based Event Stream Processing Architectures and Patterns with Apache Ka...
Cloud-Based Event Stream Processing Architectures and Patterns with Apache Ka...
Confluent On Azure: Why you should add Confluent to your Azure toolkit | Alic...
Confluent On Azure: Why you should add Confluent to your Azure toolkit | Alic...
Building Streaming Data Pipelines with Google Cloud Dataflow and Confluent Cl...
Building Streaming Data Pipelines with Google Cloud Dataflow and Confluent Cl...
A Look into the Mirror: Patterns and Best Practices for MirrorMaker2 | Cliff ...
A Look into the Mirror: Patterns and Best Practices for MirrorMaker2 | Cliff ...
Building Event Streaming Microservices with Spring Boot and Apache Kafka | Ja...
Building Event Streaming Microservices with Spring Boot and Apache Kafka | Ja...
How Confluent Completes the Event Streaming Platform (Addison Huddy & Dan Ros...
How Confluent Completes the Event Streaming Platform (Addison Huddy & Dan Ros...
Kafka Excellence at Scale – Cloud, Kubernetes, Infrastructure as Code (Vik Wa...
Kafka Excellence at Scale – Cloud, Kubernetes, Infrastructure as Code (Vik Wa...
Building a Modern, Scalable Cyber Intelligence Platform with Apache Kafka | J...
Building a Modern, Scalable Cyber Intelligence Platform with Apache Kafka | J...
Comparing three data ingestion approaches where Apache Kafka integrates with ...
Comparing three data ingestion approaches where Apache Kafka integrates with ...
Stream Processing with Kafka and KSQL in Jupiter | Namit Mahuvakar, Jupiter
Stream Processing with Kafka and KSQL in Jupiter | Namit Mahuvakar, Jupiter
Exposing and Controlling Kafka Event Streaming with Kong Konnect Enterprise |...
Exposing and Controlling Kafka Event Streaming with Kong Konnect Enterprise |...
Kafka and Kafka Streams in the Global Schibsted Data Platform
Kafka and Kafka Streams in the Global Schibsted Data Platform
Similar to 5 lessons learned for successful migration to Confluent cloud | Natan Silinitsky, wix.com
5 lessons learned for Successful Migration to Confluent Cloud
Confluent Cloud makes Devops engineers lives a lot more easier.
Yet moving 1500 microservices, 10K topics and 100K partitions to a multi-cluster Confluent cloud can be a challenge.
In this talk you will hear about 5 lessons that Wix has learned in order to successfully meet this challenge.
These lessons include:
1. Automation, Automation, Automation - all the process has to be completely automated at such scale
2. Prefer a gradual approach - E.g. migrate topics in small chunks and not all at once. Reduces risks if things go bad
3. First migrate test topics with relayed real traffic - So data will be real but will not effect production.
4. Cleanup first - avoid migrating unused topics or topics with too many unnecessary partitions
5. Adapt to Confluent Cloud APIs - e.g. lag monitoring
Devoxx UK - Migrating to Multi Cluster Managed Kafka
As Wix Kafka usage grew to 2.5B messages per day, >20K topics and >100K leader partitions serving 2000 microservices,
we decided to migrate from self-operated single cluster per data-center to a managed cloud service (Like Amazon MSK or Confluent Cloud) with a multi-cluster setup.
The classic approach would be to perform this transition when all incoming traffic is removed from the data center.
But draining an entire data-center for an undetermined period of time, until all 2000 services complete the switch was too risky for us.
This talk is about how we gradually migrated all of our Kafka consumers and producers with 0 downtime while they continued to handle regular traffic. You will learn practical steps you can take to greatly reduce the risks and speed up the migration timeline.
Migrating to Multi Cluster Managed Kafka - Conf42 - CloudNative
As Wix Kafka usage grew to 2.5B messages per day, >20K topics and >100K leader partitions serving 2000 microservices,
we decided to migrate from self-operated single cluster per data-center to a managed cloud service (Like Amazon MSK or Confluent Cloud) with a multi-cluster setup.
The classic approach would be to perform this transition when all incoming traffic is removed from the data center.
But draining an entire data-center for an undetermined period of time, until all 2000 services complete the switch was too risky for us.
This talk is about how we gradually migrated all of our Kafka consumers and producers with 0 downtime while they continued to handle regular traffic. You will learn practical steps you can take to greatly reduce the risks and speed up the migration timeline.
Migrating to Multi Cluster Managed Kafka - ApacheKafkaIL
As Wix Kafka usage grew to 2.5B messages per day, >20K topics and >100K leader partitions serving 2000 microservices,
we decided to migrate from self-operated single cluster per data-center to a managed cloud service (Like Amazon MSK or Confluent Cloud) with a multi-cluster setup.
The classic approach would be to perform this transition when all incoming traffic is removed from the data center.
But draining an entire data-center for an undetermined period of time, until all 2000 services complete the switch was too risky for us.
This talk is about how we gradually migrated all of our Kafka consumers and producers with 0 downtime while they continued to handle regular traffic. You will learn practical steps you can take to greatly reduce the risks and speed up the migration timeline.
Migrating to Multi Cluster Managed Kafka - DevopStars 2022
As Wix Kafka usage grew to 2.5B messages per day, >20K topics and >100K leader partitions serving 2000 microservices,
we decided to migrate from self-operated single cluster per data-center to a managed cloud service (Like Amazon MSK or Confluent Cloud) with a multi-cluster setup.
The classic approach would be to perform this transition when all incoming traffic is removed from the data center.
But draining an entire data-center for an undetermined period of time, until all 2000 services complete the switch was too risky for us.
This talk is about how we gradually migrated all of our Kafka consumers and producers with 0 downtime while they continued to handle regular traffic. You will learn practical steps you can take to greatly reduce the risks and speed up the migration timeline.
Kafka Summit London - Kafka based Global Data Mesh at Wix
As your organization rapidly grows in scale, so do the amount of challenges.
Growing scale comes in multiple dimensions - traffic, geographic presence, products portfolio, various technologies, amount of developers, etc.
Coming up with an architecture that can handle all of the data flows in a universal, simple way is key.
This talk is about Wix's Kafka based global data architecture and platform.
How we made it very easy for Wix 2000 microservices to publish and subscribe to data, no matter where they are deployed in the world, or what technological stack they use.
All the while offering various SDKs (some of them open-source), tools, and features for adapting to growing scale and insuring high resilience.
Devoxx Ukraine - Kafka based Global Data Mesh
As your organization rapidly grows in scale, so do the amount of challenges.
Growing scale comes in multiple dimensions - traffic, geographic presence, products portfolio, various technologies, amount of developers, etc.
Coming up with an architecture that can handle all of the data flows in a universal, simple way is key.
This talk is about Wix's Kafka based global data architecture and platform.
How we made it very easy for Wix 2000 microservices to publish and subscribe to data, no matter where they are deployed in the world, or what technological stack they use.
All the while offering various SDKs (some of them open-source), tools, and features for adapting to growing scale and insuring high resilience.
Dev Days Europe - Kafka based Global Data Mesh at Wix
As your organization rapidly grows in scale, so do the amount of challenges.
Growing scale comes in multiple dimensions - traffic, geographic presence, products portfolio, various technologies, amount of developers, etc.
Coming up with an architecture that can handle all of the data flows in a universal, simple way is key.
This talk is about Wix's Kafka based global data architecture and platform.
How we made it very easy for Wix 2000 microservices to publish and subscribe to data, no matter where they are deployed in the world, or what technological stack they use.
All the while offering various SDKs (some of them open-source), tools, and features for adapting to growing scale and insuring high resilience.
Kafka based Global Data Mesh at Wix with Natan Silnitsky | Kafka Summit Londo...
As your organization rapidly grows in scale, so do the amount of challenges.
Growing scale comes in multiple dimensions - traffic, geographic presence, products portfolio, various technologies, amount of developers, etc.
Coming up with an architecture that can handle all of the data flows in a universal, simple way is key.
This talk is about Wix's Kafka based global data architecture and platform.
How we made it very easy for Wix 2000 microservices to publish and subscribe to data, no matter where they are deployed in the world, or what technological stack they use.
All the while offering various SDKs (some of them open-source), tools, and features for adapting to growing scale and insuring high resilience.
Apache Kafka - A modern Stream Processing Platform
After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL.
Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go.
Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.
James Watters Kafka Summit NYC 2019 Keynote
The document discusses how Spring Boot and Kafka can form the basis of a new enterprise application platform that enables continuous delivery and efficient scaling through microservices and event-driven architecture. It provides examples of companies like Netflix and T-Mobile that have successfully adopted this approach. The document advocates an "event-first" design and argues this platform approach allows for arbitrary scaling, multi-cloud deployment, and increased developer autonomy and agility.
Polyglot, Fault Tolerant Event-Driven Programming with Kafka, Kubernetes and ...
At Wix, we have created a universal event-driven programming infrastructure on top of the Kafka message broker.
This infra makes sure messages are eventually successfully consumed and produced no matter what failure it encounters.
In this talk, you will learn about the features we introduced in order to make sure our distributed system can safely handle an ever growing message throughput in a fault tolerant manner.
You will be introduced to such techniques as retry topics, local persistent queues, and cooperative fibers that help make your flows more resilient and performant.
You will also learn how to make this infra work for all programming languages tech stacks with optimal resource manage using the power of Kubernetes and gRPC.
When to use a client library, and when to deploy an external pod (DaemonSet, StatefulSet) or even deploy a sidecar.
Polyglot, fault-tolerant event-driven programming with kafka, kubernetes and ...
At Wix, we have created a universal event-driven programming infrastructure on top of the Kafka message broker.
This infra makes sure messages are eventually successfully consumed and produced no matter what failure it encounters.
In this talk, you will learn about the features we introduced in order to make sure our distributed system can safely handle an ever growing message throughput in a fault tolerant manner.
You will be introduced to such techniques as retry topics, local persistent queues, and cooperative fibers that help make your flows more resilient and performant.
You will also learn how to make this infra work for all programming languages tech stacks with optimal resource manage using the power of Kubernetes and gRPC.
When to use a client library, and when to deploy an external pod (DaemonSet) or even deploy a sidecar.
KUBEBOOT - SPRING BOOT DEPLOYMENT ON KUBERNETES HAS NEVER BEEN SO EASY
Have you ever thought how to deploy Cloud Native Java Applications (Spring Boot) on Kubernetes? Kubernetes has now become a de-facto standard for deploying Cloud Native Applications, but still there is myth that they are not ready for Java workloads. The aim of this session is to break that myth to show Kubernetes is well suited for Cloud Native Java applications. The session starts with a brief history of Microservices; the framework, the libraries, the platform and the tools that have been part of every cloud native applications and how they become deprecated with Cloud Native Java applications deployed to Kubernetes. The session explores the cloud native characteristics such as Discovery, Blue/Green Deployments, Elasticity, Canary Deployments, Resiliency, Pipeline(CI/CD), Authentication etc., becomes implicit characteristics to your Spring Boot Java applications that are deployed on Kubernetes/OpenShift. In this session, we will see how to build, debug, deploy and discover Spring Boot applications on Kubernetes, covering in depth details of the tools, libraries and platform that could be used to make your spring boot deployment smooth and easy.
Lesson learns from Japan cloud trend
"Lesson learns from Japan cloud trend" explains the followings
- CloudStack Mascot History
- Japan SP / Academic cloud use cases
- Japan CloudStack Community
Introduction to Apache Kafka and Confluent... and why they matter
Milano Apache Kafka Meetup by Confluent (First Italian Kafka Meetup) on Wednesday, November 29th 2017.
Il talk introduce Apache Kafka (incluse le APIs Kafka Connect e Kafka Streams), Confluent (la società creata dai creatori di Kafka) e spiega perché Kafka è un'ottima e semplice soluzione per la gestione di stream di dati nel contesto di due delle principali forze trainanti e trend industriali: Internet of Things (IoT) e Microservices.
Budapest Data/ML - Building Modern Data Streaming Apps with NiFi, Flink and K...
Budapest Data/ML - Building Modern Data Streaming Apps with NiFi, Flink and Kafka
Apache NiFi, Apache Flink, Apache Kafka
Timothy Spann
Principal Developer Advocate
Cloudera
Data in Motion
https://budapestdata.hu/2023/en/speakers/timothy-spann/
Timothy Spann
Principal Developer Advocate
Cloudera (US)
LinkedIn · GitHub · datainmotion.dev
June 8 · Online · English talk
Building Modern Data Streaming Apps with NiFi, Flink and Kafka
In my session, I will show you some best practices I have discovered over the last 7 years in building data streaming applications including IoT, CDC, Logs, and more.
In my modern approach, we utilize several open-source frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Kafka. From there we build streaming ETL with Apache Flink SQL. We will stream data into Apache Iceberg.
We use the best streaming tools for the current applications with FLaNK. flankstack.dev
BIO
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming.
Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.
Apache Kafka - Scalable Message Processing and more!
After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL.
Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go.
Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.
Kafka based Global Data Mesh at Wix
The document describes Wix's use of Kafka and Greyhound to build a global data mesh. It addresses challenges of large scale traffic, hundreds of developers, and diverse tech stacks. Kafka and Greyhound provide a messaging platform and self-service APIs. Greyhound wraps Kafka for multiple languages. The data mesh supports cross-DC replication and producing to specific DCs. This allows events to be routed to where they are needed across Wix's infrastructure.
Confluent Operator as Cloud-Native Kafka Operator for Kubernetes
Agenda:
- Cloud Native vs. SaaS / Serverless Kafka
- The Emergence of Kubernetes
- Kafka on K8s Deployment Challenges
- Confluent Operator as Kafka Operator
- Q&A
Confluent Operator enables you to:
Provisioning, management and operations of Confluent Platform (including ZooKeeper, Apache Kafka, Kafka Connect, KSQL, Schema Registry, REST Proxy, Control Center)
Deployment on any Kubernetes Platform (Vanilla K8s, OpenShift, Rancher, Mesosphere, Cloud Foundry, Amazon EKS, Azure AKS, Google GKE, etc.)
Automate provisioning of Kafka pods in minutes
Monitor SLAs through Confluent Control Center or Prometheus
Scale Kafka elastically, handle fail-over & Automate rolling updates
Automate security configuration
Built on our first hand knowledge of running Confluent at scale
Fully supported for production usage
Similar to 5 lessons learned for successful migration to Confluent cloud | Natan Silinitsky, wix.com (20)
5 lessons learned for Successful Migration to Confluent Cloud
5 lessons learned for Successful Migration to Confluent Cloud
Devoxx UK - Migrating to Multi Cluster Managed Kafka
Devoxx UK - Migrating to Multi Cluster Managed Kafka
Migrating to Multi Cluster Managed Kafka - Conf42 - CloudNative
Migrating to Multi Cluster Managed Kafka - Conf42 - CloudNative
Migrating to Multi Cluster Managed Kafka - ApacheKafkaIL
Migrating to Multi Cluster Managed Kafka - ApacheKafkaIL
Migrating to Multi Cluster Managed Kafka - DevopStars 2022
Migrating to Multi Cluster Managed Kafka - DevopStars 2022
Kafka Summit London - Kafka based Global Data Mesh at Wix
Kafka Summit London - Kafka based Global Data Mesh at Wix
Dev Days Europe - Kafka based Global Data Mesh at Wix
Dev Days Europe - Kafka based Global Data Mesh at Wix
Kafka based Global Data Mesh at Wix with Natan Silnitsky | Kafka Summit Londo...
Kafka based Global Data Mesh at Wix with Natan Silnitsky | Kafka Summit Londo...
Apache Kafka - A modern Stream Processing Platform
Apache Kafka - A modern Stream Processing Platform
Polyglot, Fault Tolerant Event-Driven Programming with Kafka, Kubernetes and ...
Polyglot, Fault Tolerant Event-Driven Programming with Kafka, Kubernetes and ...
Polyglot, fault-tolerant event-driven programming with kafka, kubernetes and ...
Polyglot, fault-tolerant event-driven programming with kafka, kubernetes and ...
KUBEBOOT - SPRING BOOT DEPLOYMENT ON KUBERNETES HAS NEVER BEEN SO EASY
KUBEBOOT - SPRING BOOT DEPLOYMENT ON KUBERNETES HAS NEVER BEEN SO EASY
Introduction to Apache Kafka and Confluent... and why they matter
Introduction to Apache Kafka and Confluent... and why they matter
Budapest Data/ML - Building Modern Data Streaming Apps with NiFi, Flink and K...
Budapest Data/ML - Building Modern Data Streaming Apps with NiFi, Flink and K...
Apache Kafka - Scalable Message Processing and more!
Apache Kafka - Scalable Message Processing and more!
Confluent Operator as Cloud-Native Kafka Operator for Kubernetes
Confluent Operator as Cloud-Native Kafka Operator for Kubernetes
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Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
"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."
Renaming a Kafka Topic | Kafka Summit London
"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 Trendyol
"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 Techniques
"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 Kafka
"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."
Fish Plays Pokemon | Kafka Summit London
"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."
Tiered Storage 101 | Kafla Summit London
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 Why
"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 ...
"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 ...
"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 Clusters
"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 Platform
"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 Pub
"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"
TL;DR Kafka Metrics | Kafka Summit London
"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."
A Window Into Your Kafka Streams Tasks | KSL
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 Performance
"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 Beyond
"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."
Code-First Approach: Crafting Efficient Flink Apps
"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 Ecosystem
"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 Disks
"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."
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Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
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“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
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Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
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UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
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Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
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HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
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My slides at Nordic Testing Days 6.6.2024
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Paper: https://doi.org/10.1007/978-3-031-61000-4_16
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During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
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Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
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5 lessons learned for successful migration to Confluent cloud | Natan Silinitsky, wix.com
- 1. 5 lessons learned for Successful Migration to Confluent Cloud Natan Silnitsky Backend Infra Developer, Wix.com natans@wix.com twitter@NSilnitsky linkedin/natansilnitsky natansil.com
- 2. >200M Registered users (website builders) from 190 countries @NSilnitsky At Wix >2,000M Kafka messages produced / Day 2,000 Microservices
- 4. Data Center 2 Data Center 1 @NSilnitsky Confluent Cloud Kafka Cluster On premise Kafka Cluster I Kafka Cluster II Kafka Cluster III Kafka Cluster IV * AWS/GCP Microservice Microservice 2000
- 6. A Scala/Java high-level SDK for Apache Kafka. Greyhound @NSilnitsky
- 7. Site Apps Service Kafka Consumer Kafka Producer Greyhound wraps Kafka Ecom Catalog Service Kafka Broker Greyhound Producer Greyhound Consumer @NSilnitsky
- 8. Data Center 2 Data Center 1 Microservice @NSilnitsky Confluent Cloud Kafka Cluster On premise Kafka Cluster I Kafka Cluster II Kafka Cluster III Kafka Cluster IV * all at once?
- 9. Gradual over All at once 02
- 10. Data Center 2 Data Center 1 @NSilnitsky Kafka Cluster On premise Confluent Cloud Microservice HTTP HTTP Kafka Cluster I Kafka Cluster II Kafka Cluster III Kafka Cluster IV Option B Replicator Topic 5 Topic 4 Topic 3 Topic 2 Topic 1 Topic 6 Migration Orchestrator * self service - no downtime
- 11. First migrate test topics with relayed real traffic 03
- 12. Data Center 2 @NSilnitsky Kafka Cluster On premise Confluent Cloud Test app Real Topic->Test Topic Test Topics Kafka Cluster I Kafka Cluster II Kafka Cluster III Kafka Cluster IV Migration Orchestrator Replicator
- 14. Data Center 2 Data Center 1 Microservice @NSilnitsky 0 Bytes 100 MB 170 MB Confluent Cloud
- 15. Adapt to Confluent Cloud APIs 05
- 16. Data Center 2 Data Center 1 Offset lag Monitoring @NSilnitsky Confluent Cloud Kafka Cluster On premise __consumer_offsets __consumer_offsets Kafka Admin SDK Kafka Admin SDK Unavailable Available
- 17. So, We used Greyhound and dedicated orchestration services to have automatic, safe, and gradual migration to Confluent Cloud @NSilnitsky
- 18. A Scala/Java high-level SDK for Apache Kafka. 0.1.5 is out! github.com/wix/greyhound
- 19. Thank You natans@wix.com twitter @NSilnitsky linkedin/natansilnitsky github.com/natansil